Received December 19, 2019, accepted February 1, 2020, date of publication February 12, 2020, date of current version February 28, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.2973378
Back to Finger-Writing: Fingertip Writing
Technology Based on Pressure Sensing
GADDI BLUMROSEN 1,2 , KATSUYUKI SAKUMA 1 , (Senior Member, IEEE),
JOHN JEREMY RICE1 , (Member, IEEE), AND JOHN KNICKERBOCKER1
1 IBM
Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA
of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
2 Faculty
Corresponding author: Katsuyuki Sakuma (ksakuma@us.ibm.com)
(Gaddi Blumrosen and Katsuyuki Sakuma contributed equally to this work.)
ABSTRACT Handwriting was since the start of the history, a higher expression of human skills, and was used
for documentation of experiences, and for communication. Existing writing technology require a writing
tool, like a pen, and a dedicated writing surface, like paper, or more recently an electronic tablet. These
accessories of writing, of writing tool and service, are not available in many daily life situations. Furthermore,
the writing accessories, are not natural, in many cases are not ergonomic, and thus can cause fatigue, and
in extreme cases contribute to muscular and neurological diseases. In this work, we suggest to step back in
history and step forward in technology, and to create, for the first time, an alternate writing solution without
any accessories, using one own finger as writing tool, and write on almost any surface. For this, we used
directional pressure sensors attached to the fingernail. Changes in the pressure induced on the fingertip in
different directions while writing, are projected to the fingernail, and then assessed as a voltage pattern
by the sensor. Decoding the pattern, can reveal symbols like letters, punctuations, and writing commands.
In this paper, we describe the new pressure sensing modality and tailor processing methods. We tested the
new technology on two subjects having different writing patterns while writing alphabet and sentences on
different surfaces. We reached letter detection of over 80% while writing on a table, and the word detection
rate, was near 70%, after applying the correction algorithm include language priors. The results of this work
can revolutionize the way people write and communicate using more convenient, and more approachable,
finger-tip writing.
INDEX TERMS Handwriting recognition, human machine interface, natural language processing.
I. INTRODUCTION
Handwriting is an higher expression of human communication [1]. In beginning of human history, human started
using their hands for with painting on caves’ walls [2]. Along
time, writing tools like pencil, or pen were utilized to write
over different surfaces like paper, board, and recently electrical tablet [3]. The existing handwriting techniques requires
accessories of writing tool and dedicated writing surface,
suffer from hand fatigue, and can contribute to the severity of
muscular and neurological based diseases [4], and in extreme
cases for an injury. This motivated recent attempts to find
technologies to improve the writing experience.
An optimal writing technique should be: ergonomic; can be
used continuously for long time without fatigue and causing
The associate editor coordinating the review of this manuscript and
approving it for publication was Wenge Rong
VOLUME 8, 2020
.
injury; in any environment; and with minimal writing tools.
There are two main ways to minimize the use of the writing
accessories. One group of techniques built without dedicated
writing surfaces, like paper or tablet, another one without
writing tools like pens or pencils.
To exclude the need from a dedicated writing surface,
devices with sensory modules that can track the movement
and transmit it wirelessly to a terminal can be used. An electronic wireless pen was suggested in [5], [6]. It is based on
pressure measurements and/or motion of the pen, which is
used as features for handwriting recognition in the computation terminal. Pen with accelerometers was suggested [7].
Another surface-less solution, is a fingerless glove that can
implement a virtual keyboard for handwriting and was suggested in [8], and [9]. The angle at which the user’s finger
bends at the proximal interphalangeal joint is used to decode
a row of the keyboard. Discrimination between columns
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
35455
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
operated by the same finger is achieved through an abduction/adduction sensor.
To write without writing tool, some technologies use an
electronic surface that is sensitive to finger touch. A Touch
sensing display screen, can be on mobile devices like [10].
Writing letters on specific points on touch sensitive gloves
was presented in [11]. The paper in [12], suggested to use a
surface on the nail sensor to write different numbers. Both
touch sensitive techniques, might be not comfortable writing
in such device, not accessible, its performance are not always
sufficient [13].
A tool-less and surface less writing techniques, can be
based on assessing gestures that represent letters and other
symbols. One way, is to attach to the finger magnetic sensors [14], or inertial sensors [15]. A ring, that include multiple
sensor modalities was shown to implement a virtual standard
QWERTY keyboard [16]. Sign gesture recognition using
advanced temporal video image processing, and to decode
sign gesture recognition using genetic algorithm, was developed in [17], [18]. All these techniques, require additional
sensor modalities, are not fully seamlessly, need extensive
training sessions as they do not exploit the natural way of
writing. In this work, we suggest and demonstrate using a new
sensor technology based on sensor pressure measurement of
the index fingernail for finger-writing. The sensor technology fundamentals derived in IBM research labs [19]. This
sensor enables using the natural intuitive hand movement
seamlessly and thus is extremely ergonomic, uses the natural
human way of writing from back in history, and can work
on almost any available surface, in any environment. The
pressure sensor can be implemented by a Strain-Gauge sensor (SG sensor) [20], or by Photoplethysmograph Fingernail
Sensors [21]. The sensor is placed on the fingernail of the
index finger. In the work in [22], a pressure sensor based on
strain-gauge technology placed on the fingernail was introduced. It was shown, how the sensor is capable of measuring
pressure point in real-time, can be used for assessing parameters like grip strength vital for medical diagnosis, and with
machine learning tools, can also be used for coarse gesture
recognition that can be used for enabling enhanced humanmachine interface. In the work in [23], we showed first feasibility of using the sensor for writing and recognizing basic
shapes using only two pressure points. This paper extends
the work in [22], and [23], and focus on using any pressure
based sensor in task of writing full sentences in real-time in
challenging conditions like different surfaces.
The paper has the following contributions: 1) development
of a paradigm for continuous finger-writing recognition using
a fingernail pressure sensor. The paradigm enables using
natural hand and finger movement and thus is extremely
ergonomic, and emit the need from having writing accessories of writing tool like pen, and writing dedicated surface
like paper. 2) Development of analytical tools that enable
using the pressure sensor for writing and decoding of single symbol, word, and sentence in real-time. The methods are tailored to the unique characteristics of pressure
35456
FIGURE 1. A fingernail is used for writing (1.a) over different writing
surfaces (1.b). The writing process over the surface induces pressure over
the fingertip that is projected to the nail. A Pressure Sensor (PS) unit is
attached to the finger nail to capture the pressure pattern. It is
implemented by embedding few strain-gauge in different directions (1.c).
The raw data is then transmitted wirelessly to a storage and processing
unit (1.d).
measurements, and differ from other methods like [16], where
the analysis pipeline for accelerometers and gyroscopes was
reported. 3) Showing first feasibility of the technology and
comparing to accelerometer based only sensors. 4) Provide
first validation of the new finger writing technology in different challenging every daily-life condition, like writing over
different writing surfaces.
The paper outline is as follows: in section II the sensor
system and the sensor technology is described; in section III,
the methods tailored to the sensor for real-time writing finger
writing are derived; section IV, describe the experiment setup;
the results are shown and discussed in section V; and in
section VI a summary of the work, and future directions are
given.
II. SENSOR DESCRIPTION
The system is based on sensing the pressure projected into
the fingernail while writing on a surface. The sensor fundamental technology was derived in IBM research labs [19].
The pressure sensor can be implemented by a Strain-Gauge
sensor (SG sensor) [20]. This sensor enables using the natural
movement freely and thus is extremely ergonomic. The sensor is placed on the fingernail of the index finger, and doesn’t
disturb the subject to perform his/her natural activities. When
the subject is writing using his index fingertip, a pressure
is induced on the nail, which results in deformation of the
SG sensor. The deformation is translated to voltage-change
data stream, which is transmitted wirelessly to a computation
for decoding.
The suggest system is composed of the following parts: a
finger that is used for writing (a), a writing surface or medium
(b), Pressure Sensor (PS) unit (c), and storage and processing
unit (d). Figure 1 describes the system.
The PS unit is attached to the fingernail (Fig 1.a). The
surface for writing (Fig 1.b) can be any surface that induce
changes in pressure on the nail, when writing with the fingertip, e.g. 2-D surface like table, but can also be non-rigid
VOLUME 8, 2020
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
FIGURE 2. Processing data flow in finger-writing recognition. The processing consist of the flowing stages: a) pre-processing
of the PS raw-data, which include excluding of sensor bias, filtering, scaling, and interpolatio; b) detection of start and end
boundaries of each written shapes; c) shape feature extraction; d) symbol recognition based on the feature shape, that
include also multi-shape symbol, and is based on individual and public trained symbol data bases; and e) word and sentence
detection using Natural Language Processing (NLP) exploiting language priors, dictionary.
surface like cloth, or even the air (movement of the body parts
in the air, can induce changes in the pressure of the nail). The
sensing unit is shown in Fig. 1.c. The PS unit can be implemented by different technologies that can assess pressure in
different directions. In this work, we choose to implement the
PS by Strain-gauge technology (Fig 1.c1); additional sensor
like accelerometer can be aggregated (Fig 1.c2); the physical
pressure measurements, are digitized, and pre-processed in
the pre-processing unit (Fig 1.c3), and then sent wirelessly
for further processing to decode the finger writing and store
the results (Fig. 1.d).
The sensor is placed on the nail. When the fingertip interacts with surfaces, a unique pattern of pressure is induced on
fingertip and the pressure pattern is projected to the fingernail
(Fig. 1.a). The figure demonstrate the pressure map, as was
obtained by thermal mapping on the nail, when the pressure
is induced on the center. This pressure directionality pattern
can be used similar to [24], for evaluating the direction and
intensity of movement of the finger over time and enable
extracting informative features to decode the finger writing.
III. METHODS
The methods derived in this work, form the baseline for
Finger-Writing (FW) recognition using directional pressure
sensor. The subject writes and induces pressure on the surface, which translates to deformation of the nailin different
directions. When the subject stop writing, the pressure is
relaxed, and go to steady state values. The pressure pattern
VOLUME 8, 2020
induced while writing a shape on the surface is correlated
to the shapepattern. The shapes are than aggregated and
recognized as symbols of letters, or numbers, or punctuation,
or commands, or breaks between words. The symbols and
their probabilities are used to higher level meaning of words
and the complete sentence.
The FW method is composed of the flowing stages:
a) pre-processing of the PS raw-data; b) detection start and
of writing actions of writing the shapes; c) shape feature
extraction; d) symbol recognition; and e) sentence detection.
Figure 2 describes the processing data flow.
A. PROBLEM FORMULATION
A symbol is defined as the minimal information unit that
convey meaning, and can be a letter, punctuation, mathematical expression, general writing commands, or a keyboard like commands like break between words, or deletion
of a letter [25]. Let define the k’th symbol, as gk , where
k=K
gk ∈ {gk }k=1 s . A sentence is then composed from a series
of J symbols:
s = g1 g2 . . . gj . . . gJ
(1)
The criterion for FW recognition to find from the sensor’s raw
data the sentence that was written is:
ŝ[t,t+T0 ] = s[t,t+T0 ],î =argmax i P s[t,t+T0 ],i εt . . . εt+T0 , (2)
where εt , εt+1 , εt+T0 , are the PS’s inputs over period of time
[t, t + T0 ], corresponding to different location and finger
35457
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
orientation, and s[t,t+T0 ],i , is the i’th hypothesis of a possible sentence from almost infinite possibilities of symbol
permutations.
In case there are more sensor modalities, the recognition
depends also on the other sensor data, which is assumed here,
without loss of generality, as accelerometer data, denoted by,
at , at+1 . . . at+T0 . The recognition criterion than becomes:
ŝ[t,t+T0 ] = argmax i P s[t,t+T0 ],i εt , . . . εt+T0 , at , . . . at+T0
(3)
To solve the criterion, we divide the solution to four stages:
1) detect from the raw data, shape start and end time for
each pressure/relaxation session; 2) extract shape features;
3) recognize symbol’s set of shapes and then recognize
the symbol, like letters, punctuation, commands and breaks
between words; and 4) decode the higher level meaning of
words and the complete sentence.
B. PREPROCESSING
The N SG sensor (SGS) outputs, denoted by: ε1t , ε2t , . . . εNt ,
reflect the change of the voltage due to the nail deformation
from the finger writing operation. These outputs can be estimated as follows [20]:
Dti = KE ti εit
(4)
where i moves between 1 to N , Dti is the deformation,
Eit is the transfer constant of pressure to voltage, and K is a
normalization constant that is a function of the temperature.
The deformation of the nail is spatial, and varies with the
nail surface, and over time. Normalization of the pressure
measurements can then be seen as a measure to the nail
deformation up to the constant (KE ti ).
The pre-processing process consist of the following stages:
exclusion of the changing voltage bias, filtering out of artifacts, mitigate for missing samples, scaling the pressure signal, rotation of axes, and dimensionality reduction in case of
multiple pressure points and when interpretable directions are
needed.
To remove the DC effect, a high pass filtering of typically
0.1 Hz is used to exclude very slow movements and biases
that are not likely to be part of the writing. To mitigate
over missing samples, the missing samples are interpolated.
Distorted samples are then filtered out by using low-pass
filtering. The nail sensor can suffer from changing bias, due
to temperature change, from undesired induction in the circuit [26], or from battery aging. This bias can be removed estimating continuously the bias, and excluding it using Kalman
filtering similar to [27]. Scaling of the input is applied to standardize the features and improve their tolerance to different
subjects’ finger pressure profile, different pressure surfaces,
and by this to improve the overall classification results [28].
Dimensionality reduction can be also used when the number
of SGS is high.
The signal after pre-processing, is a signed temporal vector
for each SGs. It can be characterized by its amplitude, and
35458
polarity. The polarity, reflects the direction of the induced
voltage, and can be estimated in calibration phase. The SG’s
amplitude can be used to estimate the intensity of the pressure, while the sensor polarity, can be used to estimate the
direction of writing. The sensor polarity, is unique to the
subject, and is related to SGs location, nail topology and
sensor properties.
For the additional inputs (like accelerometers), a similar
pre-processing, based on characteristics of the signals, should
be obtained.
C. DETECT SHAPE BOUNDRIES
A precise detection of symbol start and end times is required
in FW recognition, similar to handwriting recognition [29].
Unlike visual based writing recognition methods, the task for
shape start and end of writing times, can’t be detected by
spatial time discontinuity, but need to be derived by temporal
measures or learning [7].
When the subject writes with his fingernail, a pressure is
induced on the surface, which is then projected to its fingernail and the measured pressure move from steady state level
to active pressure values. Then when he finishes inducing
pressure on the surface after writing the shape, the pressure
goes back to its stead state condition, which is not necessarily
the one with minimal pressure change of the sensor. This
steady state pressure level can be considered as the reference
point, or the ‘‘off’’ state, denoted as OFF. Deviation from this
state, are defined as ‘‘on’’ state, denoted as ON. These states,
are often referred to as loading and unloading states [30],
and can help to detect start and end writing times [31]. The
ways to measure the deviation from the reference state can be
performed by edge detection algorithms, e.g. by thresholding
the signal over the stead state level, and in case the PS have
deformation together, by using the envelope, composed of
average sum of the PS signal or on the PS components. The
PS envelope can be defined as:
q
p
2
2
2
2
2
2
(5)
r t = ε1t + ε2t . . . εNt = εt
where εt , is the vector of PS measurements at instance time t,
ε t = [ε t1 , ε2t , . . . εNt ].
Acommon way to detect shape start and end, is by
thresholding the envelope, when compared to the reference
t . More advanced methods, can use statistical
OFF state, rref
features of the envelope, like the Sobel edge detection algorithm [32]. We denote the start and end of the k’th shape
as tsk , tek .
D. EXTRACT SHAPE FEATURES
Each shape, in the writing interval, has a typical pattern.
Based on the pattern, features are extracted and fed continuously to a symbol classifier. This pattern can be used as
a feature, or to extract physical interpretable features like
pressure intensity, and shape duration. Spectral features like
peak and median frequencies in [33] can be used. Features can
be derived automatically by exploiting neural network [34].
VOLUME 8, 2020
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
Since FW is unique in the sense it is not limited to use with
memory-based surfaces like a tablet or paper, it lacks spatial
information. Thus, the FW recognition is more complex, and
it needs to be based more on temporal features, like start and
end of a new shape, shape duration, delay between consecutive shapes, and the temporal pattern to try to compensate over
the missing spatial information. Additional sensors can be
used to provide spatial information that can be aggregated to
the PS’s based features, and together provide spatial-temporal
information. We denote the k’th feature set as:
k
Fsh
= {F k1 , F k2 , . . .},
(6)
where k moves between 1, to K0 , and indicate the shapes’
index in the sentence writing time, [t, t + T 0 ].
E. SYMBOL RECOGNITION
In many cases, symbols like capital letters are combination
of a few shapes, and then aggregated to a symbol. The
aggregation uses statistical relationships between the shapes
and learning on prior-knowledge related to the shapes [23].
A shape aggregation criterion for finding the start and end of
the j’th symbol is:
h j ji n o
k<K0
ts ,te
k
j j
(7)
Fsh
, t s , te ,
ĵ = argmax j P s
k>1
where j moves between 1, and the number of possible
symbols, Nsym .
This criterion, can be solved by deploying clustering techniques like the one used in Sonar and Radar systems [35]. The
j j
symbol start and end time, ts , te can be found using statistical
methods that combine the best decoded hypothesis from a
pool of recognizers used in speech recognition [34], and in
mathematical expression recognition [7]. The hypothesis ĵ,
for the shapes related to the symbols, can be used to form the
j
symbol feature set Fsy . After start and end symbol detection,
the symbol classification recognition is reduced to simpler
pattern matching problem estimation:
h j ji
t ,t
j
,
(8)
ĵ[t,t+K0 ] = argmax j P s s e Fsy
n K
o
K +1
K
j
where Fsy = Fshj,s , Fshj,s . . . Fshj,e are the features of the
j
K
j
K
j’th symbol, and ts = ts j,s , and te = te j,e .
Without any loss of generality, we choose for this study,
set of features relate to the writing pattern, and are complementary set that emphasize non-pattern characteristics like
writing duration and intensity. The following features were
examined for each SG separately for the non-pattern features:
Letter writing duration, τ , maximal pressure, Amax statistical characteristics of pressure: mean, standard deviation,
skewness, kurtosis (Amean , τA , σA , S, K ); pattern properties
features: Number of positive and negative peaks, and their relative location (NPp , NNp , PPp , PNp ); and Spectral features of
peak and median frequencies, energy content at low, medium,
and high frequencies (fmax , fmed , EFl , EFm , EFh ).
VOLUME 8, 2020
In case where a symbol is composed of one shape,
Kj,s = Kj,e , and there is no need for shape aggregation.
For more accurate estimation, the criterion in (8) can be
conditioned on prior knowledge available on fingertip physiological movement constraints, like typical values, maximal
and minimal features values similar to [36]. The recognition
of the symbol in (8) is a classification problem and machine
learning techniques like handwriting can be used [6]. A training phase can incorporate the prior-knowledge related to the
symbol. The classifier output is a vector that consists of the
confidence probability of each symbol that can be fed to
the word/sentence decoding algorithm. The j’th decoded
symbols in a sentence, is denoted by gĵ , and its confidence
j
vector is psy .
In cases where recognizing the writing of one subject,
a relatively small change in the pattern between consecutive
writings of the letters (symbols) is expected, then methods
like Dynamic Time Wrapping (DTW), can be utilized like
in speech recognition [37]. The symbols pattern, with different velocities’ hypothesis, can be then correlated to a
dictionary of patterns of different symbols patterns of the
k=K
subject, {gk }k=1 s . When the pattern vary among different
subjects, more advanced classifiers like deep learning, should
be applied in future.
Design of commands in FP, is challenging task. In general,
symbols like letters and punctuations emerge from perceptual
and motor models [38], and thus are assumed to have low
spatial correlation. But in some punctuation symbols that
involve low and short pressure, like dot, or comma, they are
less separated, and are more likely to be erroneous. For commands, like space, special FP commands can be designed that
either have spatial difference, or induce change in temporal
pattern, like repeating short localized pressure, resembling a
point, few times, in different speeds. To choose commands
with low distance tools from space-time coding like in [39]
can be used.
Space is a special command, since it can be evaluated,
by the delay between the writing of different words, which is
natural, since the time between words in handwriting is also
longer than between words, as the pen needs to move a longer
distance. The distribution of the delay between words can
be assumed to have a Gamma distribution [40]. Therefore,
the likelihood of having a space between two consecutive
symbols is:
pjsy,space = Ŵ(tsj+1 − tej )
(9)
The parameters of the distributions can be found empirically, and are subject dependent. To find the end of one
word, or the start of new (keyboard space) a boundary between the distributions should be found, that minimizes false detection. Usually the subject is expected to
induce longer delay after the last letter in a word, following the natural way of writing, but even if not, through
training and visual feedback the false detection can be
minimized.
35459
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
F. SENTENCE DECODING
The symbols’ confidence values for each symbol over
time, together with language prior knowledge, as described
in Fig. 2, are fed to classifier to decode a full sentence. The
sentence decoding criterion, based on the raw sensor data
in (3), is reduced to the following criterion:
ŝ[t,t+K0 ] = argmax j P g1 . . . gj . . . gJ p1sy , . . . pJsy , PL , (10)
where PL , is language prior, trained on data set.
The language priors, that is commonly used in handwriting and speech recording is sometimes refer to as priors
of Natural Language Processing (NLP) [41]. It includes the
likelihood of letter sequence, which is related to human
vocal properties and limitations, word dictionary, relationship
between the word, as can be determined from higher level
processing and context. This relationship can be extracted by
training the model over a large amount of data with many
examples and large computation resources using advanced
machine learning techniques, such as deep learning.
A sub-optimal approach that requires reduced level of
trained data, and less cumbersome computations, is to use
the traditional approach of first detecting the words using
punctuations symbols, and subsequently use for each word,
a Hidden Markov Model (HMM) [42], that include the letter
probability and their relationship over time. Dictionary correction can be applied as post-processing stage. The accuracy
of this algorithm depends on the symbol detection accuracy,
the accuracy of transition between letters, and on the accuracy
of the break probabilities. Thus, for each word, the HMM,
solve in Maximum Likelihood (ML) optimal manner the
decoding of each word, conditioned on the features of the
letters of the word. Upper layers corrections, can be applied
after the stage, using language based using query spelling
suggestions [43].
IV. EXPERIMENT SETUP
A. EXPERIMENT SETUP DESCRIPTION
The experiment setup is composed of a PS sensor equipped
with wireless capability, a computer to process the data and
display the results, as described in section II, and is shown
in Fig 3.a. The sensor unit is composed of three SG sensors
and 3-D accelerometers, amplifiers, analog to digital converter (ADC), micro-controller for initial processing, a wireless data transmission and a battery. The sampling frequency
was set to 50 Hz. The A/D output signal range is 0 to 2047,
where the maximum voltage of ADC is 1.87V. An offline
power control was set that the maximal voltage range will
fit the strongest deformation of the SG. A 3-D acceleration
system on the nail was used as a reference system to the
pressure one. Before the experiment, the sensor was attached
to the index fingernail, using a nail glue. The directionality of
the sensor was then tested by rolling the finger on a surface,
to verify the coupling of the sensor to the nail and to assess its
directionality pattern. The driver and the processing methods
were implemented using MATLAB (Matlab.com, 2016b).
35460
FIGURE 3. Processing data flow in fingertip- The experimental setup
(Fig. 3a). Figure 3b, shows the three writing surfaces used in the
experiment, table, cloth, and the subject’s hand.
The experiment for evaluation of the method was performed
with two subjects, which English is their second language,
and thus their writing style is different. They performed
writing on a table, cloth, and on their hand, as demonstrated
in figure 3.b.
B. EXPERIMENT SETS
The aim of the experiment sets was to characterize the PS sensor signals, provide feasibility for the methods for decoding
the FW, and produce first order performance evaluation of the
new technology. For this, we designed an experiment set with
two subjects that write sentences, in natural way as possible,
on the different surfaces. We examined the inter-subject variability in a session, and the intra-subject variability. FW, similar to handwriting, has different characteristics pattern that
changes from subject to subject and over time, and writing
surface.
There were two main sets. One was built to examine the
performance of writing on different surfaces (table, cloth, and
hand), when trained on others (table). The other one was to
test the suggested encoding of full sentences, when trained
on only two repetition of the alphabet (one shout training),
which is short enough to be used in daily life system after
the deployment of the sensor. For both sets, the training was
writing the English alphabet with two repetitions on the table.
The first test set was writing on three surfaces (table, cloth,
and hand) twice, the entire alphabet. In the second set, each
subject wrote same 9 sentences (that were randomly chosen
from the start of the book ‘‘Moby Dick’’) on the table, with
varying word length from 2 to 9 letters, in total of 33 words.
V. RESULTS
A. SIGNAL PRE-PROCESSING
Before the start of the experiment, the sensor was tested to
verify that the sensor was attached properly, and to assess the
specific fingernail topology, similar to what is used in other
sensing technologies that require high level coupling like
Electroencephalography (EEG) sensor modality [44]. The
absolute directionality response of the sensor, and its polarity,
that can be used to estimate writing intensity and direction,
are obtained by rolling the finger from right to left (roll) with
VOLUME 8, 2020
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
FIGURE 4. Directionality and absolute response of the SGs from finger
rolling test demonstrated by the first subject. The pressure sensor three
SGs correspond differently in their amplitude and polarity (sign of the
voltage) to different pressure directions. These patterns, are unique to the
subjec’s nail topology and sensor properties can be used to estimate the
writing direction, and the pressure intensity.
different orientation. Figure 4, shows the amplitude and polarity (sign) of the rolling test, performed by the second subject.
The separation between the SGs sensors, which govern the
sensor directionality, is significant in finger rolling angles of
over 20 degrees. The correlation coefficient between left, and
center SGs is 0.5, right and center is negative 0.483, and right
and left, of negative 0.339. This indicated a small coupling
between left and center SGs, and more dominant negative
polarity of the right SG, which is related to the fingernail
morphology at the SG’s location.
The SG signal is pre-processed to mitigate over missing samples, exclude artifacts, and compensate for bias.
Figure 5 shows the signal in writing small letters alphabet.
The miss sample rate was less than 0.5 percent, and the
missing samples were distributed along the entire training
session, and thus can be mitigated by interpolation similar
to [45]. From the figure, it is seen that the SGs captures
around half of the voltage dynamic range in regular writing
operation, which will still support double force induced on
the surface. The voltage reference points changed between
each SG by less than 10 percent of the full dynamic range.
The signal as seen in Fig 5.a, suffers from slow frequency
drift over time, where there is a change of the OFF reference
point from around in the beginning of the session to decreases
by around 0.03 V in the end of the writing session after
around 50 second. A small-scale variable random change
can induce changing bias. For the removal of the slow bias,
a high-pass filter of 0.1 Hz was applied. For the continuous
drift, a Kalman filter that continuously estimate and subtract
the bias by using other sensor measurements similar to [27]
can be applied. The data is then normalized according to the
strongest pressure value in the session. The data before, and
after the filtering, is shown in (Fig. 5.b).
B. SYMBOL BOUNDARIES DETECTION
Accurate shape and symbol boundary detection affects the
accuracy estimation of the FP writing. In the case where a
symbol is a multi-shape symbol, like in mathematical expression, or in case of writing capital letters not in cursive writing,
the shape boundaries should be decoded, prior to symbol’s
shape aggregation and calculation of symbol start and stop.
In case of one-shape symbols, or a symbol that is composed
from multiple shapes, but its writing is performed continuously where the finger does is in OFF stage for only fraction
of second, like in cursive writing, the symbol start/end time
can be estimated directly.
Since in FP writing process tries to be with minimal limitation, and mimic the natural way of handwriting, the detection
algorithm for shape or symbols, can exploit prior knowledge
in handwriting, like typical delays between the symbols,
symbol duration, and the typical pressure level induced in
FIGURE 5. Signal of writing alphabet in small letters, before and after pre-processing. The pre-processing
includes interpolation to mitigate over possible missing samples, high pass filter to exclude bias, and
normalization of the signal.
VOLUME 8, 2020
35461
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
FIGURE 6. Signal of writing alphabet in small letters, before and after pre-processing. Punctuations of ‘‘?’’, and
‘‘!’’ symbols are shown in (6.a), and their shape seems quite separated. For other punctuations like dot, and other
writing commands like space, or delete, we suggest using dedicated gestures performed twice as shown (6.b):
bottom up, up bottom, left right, right left (comma, period, delete, space). The entire alphabet writing as used for
training, and its start and end are detected automatically, are shown in (6.c). The envelope used for detection of
OFF and ON states, is shown below.
the FP writing. This is used in setting the envelope detection
thresholds. The ON state was determined, when the letter
envelop in (5), went down from the peak to 30 percent above
the OFF reference level.
Figure 6.a, and 6.b, shows an example of writing punctuations and commands symbols. The punctuations were of ‘‘?’’,
and ‘‘!’’. For other punctuations like dot, and other writing
commands like space, or delete, we suggest using dedicated
gestures, as shown in Fig. 6.b. The commands are performed
twice, continuously, bottom up, up bottom, left right, right
left (comma, period, delete, space). Figure 6.c, shows the
entire alphabet writing that was used for training, and its start
and end are detected automatically At the bottom of Fig. 6.c,
the signal envelope (3) is shown, and the limits between OFF
and ON states.
C. SIGNAL REPRESENTATION/ FEATURE EXTRACTION
Each shape and symbol is characterized by a typical pattern, and a set of temporal, spectral, and kinematic features.
To characterize the signals pattern, we normalize their power,
and wrap them into a 256 length vector. The normalization,
helps to emphasize FP’s writing pattern differences that are
invariant of writing speed, and is similar to what is used in
machine learning based techniques [28]. Features matching
algorithms can then be applied on the pattern waveform.
Neural network based pattern matching techniques required
massive data sets, and are less interruptible [46]. In addition,
since the main focus of this initial study is to characterize the
signal in relation to the physical sensor features, we used as
features the coarse signal pattern, in addition to fundamental
common features commonly used in handwriting recognition
and have interpretable physical meaning [29].
35462
Figure 7 shows the normalized pattern of the three SGs
while writing English alphabet on the table during training
for the first, and second subjects. The left, central, and right
SGs patterns are in blue, red, and green colors, and the
inter-session variability (2 repetitions) is characterized by the
area around the line. The letters start and end were found
according to (3). The direction of the FP writing over time can
be decoded by the directionality pattern. The polarity of the
central and left SGs is positive, while the right SG, has dual
polarity, which can be related to the relation between the nail
morphology and the sensor location. It can be seen from the
figure, that the inter-session variability is much lower than the
inter-subject variability. It is explained by subjects’ different
writing style, that include writing same letters sometimes
in different directionality (like writing the letter l, bottom
up, or up bottom), different pressure (individual strength,
or different nail shape that project different pressures), and
different timing. This implies that subject specific patterns,
or massive data from many diverse subjects should be used to
reflect these kind of variations for training in future.
Writing of the letters c, and m, at different writing surfaces
(table, cloth, and hand), for the two subjects is shown in
Fig. 8. The letters c is one of the simplest letters that usually
has lower subject variability (writing half circle counter clock
wise) in its writing compared to other letters [23]; therefore,
it can be used as a reference letter. From the patterns, it seems
that the letter is written in similar way in the different experiment conditions (surfaces) for the different users, but with
higher inter-subject variability due to different directionality
pattern between the two subjects. The letter m is more complex in writing, and has higher variability in its writing, as it
contains three lines, and 2 half circles. For the two subjects,
VOLUME 8, 2020
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
FIGURE 7. Normalized writing patterns of alphabet for writing in the table for the first and second subjects. The inter-session
variability (2 repetitions), is characterized by the area around the line. The inter session variability is much lower for most
letters, than the inter-subject variability, due to each subject writing style, that include different letters directionality (writing
from left to right, vs. writing right to left), pressure (individual strength, or different nail shape that project different pressures)
and timing. Thus, subject specific patterns, or massive data from many diverse subjects, should be used for training.
FIGURE 8. The pattern change between subjects and writing surfaces for the letters c,
and m. The differences between the experiment conditions (surfaces) are much lower than
between the subjects. This indicates that for accurate results, subject specific training is
preferred, and that it is possible for each subject to recognize letter, when trained in other
surface.
at least two peaks (positive, or negative) can be recognized,
which suits the nature of writing similar shapes. The directionality of the PS can be used to assess the direction of
the FP movement. For instance, the pressure on the fingertip
while going down, and releasing the pressure while going
up, can be seen on the central SG, and in some of the SGs
that are coupled. The direction of movement can be assessed
by the magnitude of the un-normalized SG amplitudes in the
calibration as shown in Fig. 1. For instance, the right SG is
more dominant than the left one in most letters, due to the
nature of writing from right to left. This value of magnitude
is one of the features that are used in addition to the pattern
in the symbol recognition.
Writing the letter on a cloth, is characterized by higher
inter-session variability, and less smoothness compared to the
smoother surfaces of the hand and the table. Writing over the
VOLUME 8, 2020
hand, is characterized by the highest inter-session variability,
which can be explained by the different curves on the hand,
and different start writing points. For the two letters, the intersubject variability is higher than the inter-session variability,
which indicate on the need to tailor calibration for each
subject separately, or use massive data side, that capture all
the variations in different letters writing.
The average inter-subject letter variability is function of
the subject, his/her technique of writing, and of the writing
surfaces. Table 1. shows the inter-subject variability (between
experiment repetitions) in different surfaces, calculated as the
mean distance in the feature space. The table inter-subject
variability is the lowest, which can be explained by the relatively smooth and more certain surface of the table.
The other two surfaces tend to be more challenging for
writing, with more varying surface in one hand, which result
35463
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
FIGURE 9. Normalized features values in different surfaces for all letters
for the central pressure sensor for the two subjects. The deviation of
feature’ values and between the subjects is high. Some feature’ value are
consistent between the subjects, like the maximal pressure, Amax
(highest on the table compared to the hand and cloth), or the maximal
frequency of letters, that is the lowest on the table, due to the
smoothness of the table.
TABLE 1. Inter-subject variability.
in higher inter-subject variability. The inter-subject variability
(variability between the subject, intra-subject variability) is
much higher (>50% in average) than the inter-session variability of same subject, which can be explained by the difference in the sensor placement and the differences between the
subjects’ nail morphology.
Figure 9 shows the normalized mean and standard deviation of the features values in training data, averaged on all
subjects. It seems that there is no consistency among subjects
on the deviation of the features among the surfaces. The letter
duration seems to deviate between the two subjects, may be
due to the writing limitation of the experiment. The pressure
power seems to be the highest on the table and on writing
with on the cloth, for subject one, and highest on table, and
lower in cloth and hand surfaces. This can be explained by
the smoothness of the surface, and by the subject’s individual
motor control. For example, when the surface is smoother
(like the table) to have effective writing with efficient effect
on the nail pressure, the subject needs less writing power. The
maximal frequency of writing seems to be the lowest on the
table and can again be explained by the smoothness of the
writing surface.
Applying F-test, to the features, shows that the four most
significant features to recognize the letters in the training
sequence in decreasing order, are: µA (the average pressure
when writing the letter), Efm (the average frequency content
of the signal between 1 to 3 Hz), τA (the duration of writing
the symbol), σA , the standard deviation of the signal, which
indicate on the smoothness of the writing.
35464
FIGURE 10. Recognition accuracy dependency on the writing surface of
the PS (with DTW and DFA classifiers), in compare to accelerometer
(with DFA). The data was trained on writing over the table. The DTW
classifier, which exploit the pressure pattern only, seems to overperform
the DFA classifier, which exploit statistical features related to the pattern,
pressure strength, and duration.
D. SYMBOL RECOGNITION
Symbols can be letters, which are composed of shapes
(in case of capital letters), single shape letters (small letters),
punctuations, or writing commands like the one implemented
in keyboard. In this work, we focus, without loss of generality,
in recognizing single shape letters. Extension of the work to
the other symbols can be achieved using methods like in [23].
For the symbol recognition, we used two classifiers that
are traditionally used in handwriting recognition, Dynamical
Time Wrapping (DTW) [47], and Discriminative Function
Analysis (DFA) classifier [48]. The DTW works on the pattern while the other classifiers can work on the features,
pattern, and combination of both. To reduce overfitting in
the training phase, we added noise according to statistics
of the measurements similar to [27], and performed on a
features Principle Component Analysis (PCA) and kept the
components that preserved 98% of the explained variance.
For the training set we used for the DTW, the alphabet
FP on the table, which due to the smoothness is ‘‘cleaner’’
pattern of the signal, and thus is natural choice to preserve the
reference pattern. For the DFA classifier, the alphabet writing
at different surfaces was used, to reflect the spread changes of
the pattern in the different surfaces. The training error for the
DTW, and DFA, was 9.9, and 3.8, and 9.9, and 3.8 percent,
for the first, and second subjects, respectively.
The letter detection rate for the multiple experiment sets is
shown in Fig. 10. It shows the results of DTW, based on the
pattern only applied on the PS’s data; DFA classifier applied
on the PS’s data, based on pattern after dimensionality reduction using PCA to 20, in addition to the 7 significant features
from each PS point (left, center, and right), and DFA applied
similarly on the reference acceleration data. The accuracy of
the training set was high for all classifiers, and for both PS and
acceleration measurements. The accuracy of the DTW was
higher for the PS’s data, which indicate that the information
in the pattern is critical for high recognition rate. The test set
using the table as reference, was the highest, as expected,
since the classifier was trained on the table, and the table
is the smoothest surface, and therefore, is likely to maintain
VOLUME 8, 2020
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
FIGURE 11. Histogram of delays between consecutive writing. The
separation between words and letters is determined by threshold that
minimizes the word start false detection, which depends on the Gamma
approximation accuracy. The threshold was found to be 0.65, and
0.85 seconds for the first and second subjects respectively.
consistency with the patterns over time. The accuracy of
writing on the cloth, was still high, as beside the increase
friction, it doesn’t have unexpected curves, like the hand, that
has the lowest performance. Training the FP writing on the
cloth, and the hand, might come with higher detection rate for
these surfaces, but will down perform the performance on the
table, which is around 80%. This shows a tradeoff between
high detection performance and compatibility to different
surfaces. The acceleration reference, gave performance of
around 20%, which is less than third of the accuracy using the
pressure sensor, but around 5 times higher than the random
selection of 3.85% (shown in black line).
To detect word’s start/end times, a dedicated symbol can
be used, that can be for example matched to the keyboard
symbol set. FP writing can exploit the delay between letters and words and impose way of writing that is close to
natural writing style, but with the awareness of the subject to have longer break between words than compared to
between letters. This is equivalent to the increased duration
in handwriting, when moving the pen when start writing
new word, compared to the one when writing small letter.
Figure 11 shows the histogram of delays of between writing
letters vs. the one between writing words. The curves (red
and blue), show the Gamma distribution fitting for the letter
and words. The separation between words can be determined
by threshold that minimizes the word start false detection
according to the distribution tail, which depends on the
Gamma approximation accuracy. The threshold was found to
be 0.65, and 0.85 seconds for the first subject, second subject,
respectively. Using this threshold, with one misdetection of
word start, there were 4 (4.4%) and 1 (1.1%) false detection of word start (as part of the word). The difference in
the distributions, and thresholds between subjects is due to
difference in writing characteristics like velocity, and the way
the subject perform the OFF phase, when the subject raise
his/her finger, between writing the letters. Thus, more training
in FP, real-time visual feedback, and methods that know to
correct the spaces between words according to the word and
sentence content, are expected to improve the word start and
end recognition.
E. SENTENCE DECODING
Figure 12 demonstrate an example for a sentence data analysis flow for FP writing of the first subject. The sentence, is ‘‘never mind how long ago precisely’’ taken from
the book Moby Dick. Figure 12.a shows the pressure signals after post-processing and applying the symbol boundaries detection algorithm. Features are extracted and fed to
DWT based classifier, which output the likelihood of each
FIGURE 12. Histogram Recognition of FW example for the sentence from Moby Dick, ‘‘never mind how long ago
precisely’’. The letters boundaries are first detected. Then features are extracted and fed to classifier. Then are decoded
for small letters where each word recognized by its pattern. Unlike the capital letters that was composed from symbols
representing shapes, in small letters, the symbol can represent the all small letter.
VOLUME 8, 2020
35465
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
TABLE 2. Word recognition success rate statistics.
j
letter, psy , in the confidence matrix in Fig. 12.b. The right
column shows the probability of the break between words,
j
psy,space , denoted in the figure as Pb . To decode a letter,
we look on the maximal probability of the classifier in a row.
Then, an internal HMM model is applied, that output the
two maximal hypotheses. We apply the following efficient
algorithm that exploit the first, second, and the spelling corrections, used as prior knowledge of the language. If the first
HMM hypotheses is a valid dictionary word, then we choose
this word. If not, we apply the dictionary on the second HMM
hypothesis, if it is part a valid dictionary word, then we choose
this word. If not, we choose the dictionary correction of the
first HMM hypothesis. If there is no correction to the word,
which is likely with names that are not part of the dictionary,
then we keep the first HMM hypothesis. The sentence is then
fed in post processing stage, to an algorithm using query
spelling suggestions like NLP in [43], to improve the results
accuracy, as seen in the bottom of Fig. 12.C.
We compared three different methods for word recognition: 1) HMM hypothesis without applying any prior letter knowledge; 2) with word spelling correction applied on
first two HMM outputs, which is example of applying word
prior knowledge; and 3) with applying on the HMM with
prior knowledge outputs prior knowledge related to the sentence (NLP), using google suggestion tool. After first HMM
hypothesis, the word detection success rate were, 49.3, and
45.5 percent (compared to around 3 percent for random
selection). The detection rate of words without or with only
one spelling errors based on the first HMM hypothesis, were
84.8, and 75.7 percent. After applying the suggested dictionary correction based algorithm from above, the success
rate increased to 63.6, and 64.2 percent. This indicates that
only part of the one letter level errors could be mitigated by
using the prior knowledge about the words. After applying
NLP knowledge, the relationship between words in sentence,
and the content, the percentage increased to 69.7, and 66.0,
for the first and second subjects receptively. Table 2 summarizes the word performance.
VI. CONCLUSION AND FUTURE WORK
This work describes for the first time, systems and methods
that has the potential to enable natural writing with a finger
on almost any available surfaces without need of writing
accessories. In this work, the new concept was described,
and processing methods were tailored to the nail sensor.
The new technology was validated by two subjects with
35466
different writing style patterns, different nail morphology,
on different surfaces. The accuracy of the letter detection
on table was over 80%, and the word detection rate, was
near 70%, after applying the full correction algorithm include
language priors. Adding more pressure points, extending the
data base size to reflect all the settled writing variations, and
aggregating information from other sensors, can improve the
results. The results of this work can also assist in detection
of abnormal pattern of writing and assist in making writing
more accessible to new populations throughout the world.
Next planned work, will be to extend the data sizefor
each subject, extend the number of subjects, and apply more
advanced classifiers like deep learning classifiers that will
include automatic feature selection and can exploit big data
efficiently. The proposed finger writing recognition features,
like pressure and writing style, can be aggregated and used
to enhance existing biometric systems using techniques like
in [49]. A future challenge, is to implement this FW system in
real-time, connecting it directly to the watch or the cellphone
in interactive manner, to be used as human-machine interface.
REFERENCES
[1] K. P. Feder and A. Majnemer, ‘‘Handwriting development, competency, and intervention,’’ Develop. Med. Child Neurol., vol. 49, no. 4,
pp. 312–317, Apr. 2007.
[2] J. W. P. Walker, D. T. G. Clinnick, and J. B. W. Pedersen, ‘‘Profiled hands
in palaeolithic art: The first universally recognized symbol of the human
form,’’ World Art, vol. 8, no. 1, pp. 1–19, Jan. 2018.
[3] T. Schneider, G. Magyar, S. Barua, T. Ernst, N. Miller, S. Franklin,
E. Montbach, D. J. Davis, A. Khan, and J. W. Doane, ‘‘P-171: A flexible
touch-sensitive writing tablet,’’ in SID Symp. Dig., 2008, vol. 39, no. 1,
p. 1840.
[4] C. Gallea, S. G. Horovitz, M. ’Ali Najee-Ullah, and M. Hallett, ‘‘Impairment of a parieto-premotor network specialized for handwriting in writer’s
cramp,’’ Hum. Brain Mapping, vol. 37, no. 12, pp. 4363–4375, Dec. 2016.
[5] M. O’connor and D. S. Vannier, ‘‘Electronic pen device,’’
U.S. Patent 6 188 392, Feb. 13, 2001.
[6] M. Nakai, T. Sudo, H. Shimodaira, and S. Sagayama, ‘‘Pen pressure
features for writer-independent on-line handwriting recognition based on
substroke HMM,’’ in Proc. 16th Int. Conf. Pattern Recognit., vol. 3, 2002,
pp. 220–223.
[7] B. Milner, ‘‘Handwriting recognition using acceleration-based motion
detection,’’ IEE Colloq. Doc. Image Process. Multimedia, 1999, p. 5.
[8] F. Zngf and S. Huang, ‘‘Glove virtual keyboard for baseless typing,’’ U.S.
Patent Appl. 10/187 098, Jan. 1, 2004.
[9] F. Kuester, M. Chen, M. E. Phair, and C. Mehring, ‘‘Towards keyboard
independent touch typing in VR,’’ in Proc. ACM Symp. Virtual Reality
Softw. Technol., 2005, pp. 86–95.
[10] A. Butler, S. Izadi, and S. Hodges, ‘‘SideSight: Multi-touch interaction
around small devices,’’ in Proc. 21st Annu. ACM Symp. User Interface
Softw. Technol., 2008, pp. 201–204.
[11] E. Whitmire, M. Jain, D. Jain, G. Nelson, R. Karkar, S. Patel, and M. Goel,
‘‘DigiTouch: Reconfigurable thumb-to-finger input and text entry on headmounted displays,’’ in Proc. ACM Interact., Mobile, Wearable Ubiquitous
Technol., Sep. 2017, vol. 1, no. 3, pp. 1–21.
[12] L. Chan, ‘‘FingerPad: Private and subtle interaction using fingertips,’’
in Proc. 26th Annu. ACM Symp. User Interface Softw. Technol., 2013,
pp. 255–260.
[13] S. Lee and S. Zhai, ‘‘The performance of touch screen soft buttons,’’ in
Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2009, pp. 309–318.
[14] B.-Y. Chen, ‘‘Finger-touch tracking system,’’ U.S. Patent 14 508 448,
Apr. 7, 2016.
[15] J. Liu, L. Zhong, J. Wickramasuriya, and V. Vasudevan, ‘‘uWave:
Accelerometer-based personalized gesture recognition and its applications,’’ Pervasive Mobile Comput., vol. 5, no. 6, pp. 657–675, 2009.
VOLUME 8, 2020
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
[16] S. Nirjon, J. Gummeson, D. Gelb, and K.-H. Kim, ‘‘Typingring: A wearable ring platform for text input,’’ in Proc. 13th Annu. Int. Conf. Mobile
Syst., Appl., Services, 2015, pp. 227–239.
[17] R. Kaluri and C. H. Pradeep, ‘‘An enhanced framework for sign gesture
recognition using hidden Markov model and adaptive histogram technique,’’ Int. J. Intell. Eng. Syst., vol. 10, pp. 11–19, 2017.
[18] R. Kaluri and C. Pradeep Reddy, ‘‘A framework for sign gesture recognition using improved genetic algorithm and adaptive filter,’’ Cogent Eng.,
vol. 3, no. 1, Oct. 2016, Art. no. 1251730.
[19] S. Heisig and K. Sakuma, ‘‘Characterizing primate nail deformation,’’
U.S. Patent 15 410 836, Mar. 29, 2018.
[20] J. G. Da Silva, A. A. De Carvalho, and D. D. Da Silva, ‘‘A strain gauge tactile sensor for finger-mounted applications,’’ IEEE Trans. Instrum. Meas.,
vol. 51, no. 1, pp. 18–22, 2002.
[21] S. A. Mascaro and H. H. Asada, ‘‘Photoplethysmograph fingernail sensors
for measuring finger forces without haptic obstruction,’’ IEEE Trans.
Robot. Autom., vol. 17, no. 5, pp. 698–708, Oct. 2001.
[22] K. Sakuma, ‘‘Wearable nail deformation sensing for behavioral and biomechanical monitoring and human-computer interaction,’’ Sci. Rep., vol. 8,
no. 1, 2018, Art. no. 18031.
[23] K. Sakuma, G. Blumrosen, J. J. Rice, J. Rogers, and J. Knickerbocker,
‘‘Turning the finger into a writing tool,’’ in Proc. IEEE EMBS, Jul. 2019,
pp. 1239–1242.
[24] R. Williamson and B. J. Andrews, ‘‘Detecting absolute human knee angle
and angular velocity using accelerometers and rate gyroscopes,’’ Med. Biol.
Eng. Comput., vol. 39, no. 3, pp. 294–302, May 2001.
[25] M. Koschinski, H.-J. Winkler, and M. Lang, ‘‘Segmentation and recognition of symbols within handwritten mathematical expressions,’’ in Proc.
Int. Conf. Acoust., Speech, Signal Process. (ICASSP), vol. 4, 1995,
pp. 2439–2442.
[26] S. A. Gee, W. F. Van Den Bogert, and V. R. Akylas, ‘‘Strain-gauge mapping
of die surface stresses,’’ IEEE Trans. Compon., Hybrids, Manuf. Technol.,
vol. CHMT-12, no. 4, pp. 587–593, Dec. 1989.
[27] G. Blumrosen and A. Luttwak, ‘‘Human body parts tracking and kinematic
features assessment based on RSSI and inertial sensor measurements,’’
Sensors, vol. 13, no. 9, pp. 11289–11313, 2013.
[28] S. Ioffe and C. Szegedy, ‘‘Batch normalization: Accelerating deep network
training by reducing internal covariate shift,’’ 2015, arXiv:1502.03167.
[Online]. Available: http://arxiv.org/abs/1502.03167
[29] R. Plamondon and S. N. Srihari, ‘‘Online and off-line handwriting recognition: A comprehensive survey,’’ IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 22, no. 1, pp. 63–84, Jan. 2000.
[30] C. Pang, G.-Y. Lee, T.-I. Kim, S. M. Kim, H. N. Kim, S.-H. Ahn, and
K.-Y. Suh, ‘‘A flexible and highly sensitive strain-gauge sensor using
reversible interlocking of nanofibres,’’ Nature Mater., vol. 11, no. 9,
pp. 795–801, Sep. 2012.
[31] J. Han, L. Shao, D. Xu, and J. Shotton, ‘‘Enhanced computer vision with
microsoft Kinect sensor: A review,’’ IEEE Trans. Cybern., vol. 43, no. 5,
pp. 1318–1334, Oct. 2013.
[32] J. Canny, ‘‘A computational approach to edge detection,’’ IEEE Trans.
Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.
[33] E. Amichai, G. Blumrosen, and Y. Yovel, ‘‘Calling louder and longer: How
bats use biosonar under severe acoustic interference from other bats,’’ Proc.
Roy. Soc. B, Biol. Sci., vol. 282, no. 1821, Dec. 2015, Art. no. 20152064.
[34] V. Soto, O. Siohan, M. Elfeky, and P. Moreno, ‘‘Selection and combination
of hypotheses for dialectal speech recognition,’’ in Proc. IEEE Int. Conf.
Acoust. Speech Signal Process. (ICASSP), May 2016, pp. 5845–5849.
[35] C. G. Goetz, ‘‘Movement disorder society-sponsored revision of the
unified Parkinson’s disease rating scale (MDS-UPDRS): Process, format, and clinimetric testing plan,’’ Movement Disorders, vol. 22, no. 1,
pp. 41–47, 2007.
[36] Y. Lavi, D. Birnbaum, O. Shabaty, and G. Blumrosen, ‘‘Biometric system
based on Kinect skeletal, facial and vocal features,’’ in Proc. Future Technol. Conf. (FTC), Vancouver, BC, Canada. Cham, Switzerland: Springer,
Nov. 2018, pp. 884–903.
[37] H. Sakoe and S. Chiba, ‘‘Dynamic programming algorithm optimization for spoken word recognition,’’ IEEE Trans. Acoust., vol. 26, no. 1,
pp. 43–49, Feb. 1978.
[38] H. Cornhill and J. Case-Smith, ‘‘Factors that relate to good and poor
handwriting,’’ Amer. J. Occupational Therapy, vol. 50, no. 9, pp. 732–739,
Oct. 1996.
[39] G. Blumrosen and A. Freedman, ‘‘Sensitivity study and a practical algorithm for ML OSTBC and beam forming combination,’’ in Proc. 23rd IEEE
Conv. Elect. Electron. Eng. Israel, Sep. 2004, pp. 1–5.
VOLUME 8, 2020
[40] S. E. Levinson, ‘‘Continuously variable duration hidden Markov models
for automatic speech recognition,’’ Comput. Speech Lang., vol. 1, no. 1,
pp. 29–45, Mar. 1986.
[41] W. Swaileh, Language Modelling for Handwriting Recognition. Caen,
France: Normandie Univ., 2017.
[42] J. Qi and Z. Yang, ‘‘Learning dictionaries of sparse codes of 3D movements
of body joints for real-time human activity understanding,’’ PLoS ONE,
vol. 9, no. 12, Dec. 2014, Art. no. e114147.
[43] Y. Bassil and M. Alwani, ‘‘OCR post-processing error correction algorithm using Google online spelling suggestion,’’ 2012, arXiv:1204.0191.
[Online]. Available: http://arxiv.org/abs/1204.0191
[44] K. S. Guillory and D. Yatsenko, ‘‘Self-contained surface physiological
monitor with adhesive attachment,’’ U.S. Patent 11 827 387, Apr. 17, 2008.
[45] G. Blumrosen, B. Hod, T. Anker, D. Dolev, and B. Rubinsky, ‘‘Enhancing
RSSI-based tracking accuracy in wireless sensor networks,’’ ACM Trans.
Sensor Netw., vol. 9, no. 3, pp. 1–28, May 2013.
[46] A. Graves and J. Schmidhuber, ‘‘Offline handwriting recognition with
multidimensional recurrent neural networks,’’ in Proc. Adv. Neural Inf.
Process. Syst., 2009, pp. 545–552.
[47] Y.-L. Hsu, C.-L. Chu, Y.-J. Tsai, and J.-S. Wang, ‘‘An inertial pen with
dynamic time warping recognizer for handwriting and gesture recognition,’’ IEEE Sensors J., vol. 15, no. 1, pp. 154–163, Jul. 2015.
[48] L. Prevost, C. Michel-Sendis, A. Moises, L. Oudot, and M. Milgram,
‘‘Combining model-based and discriminative classifiers: Application to
handwritten character recognition,’’ in Proc. Proc. 7th Int. Conf. Document
Anal. Recognit., 2003, pp. 31–35.
[49] Y. Lavi, D. Birnbaum, O. Shabaty, and G. Blumrosen, ‘‘Biometric system
based on Kinect skeletal, facial and vocal features,’’ in Proc. Future
Technol. Conf., 2018, pp. 884–903.
GADDI BLUMROSEN was born in Jerusalem,
Israel. He received the B.S. and M.S. degrees in
electrical engineering from Tel Aviv University,
in 2005, and the Ph.D. degree in biomedical engineering from the School of Engineering and Computer Science, Hebrew University, in 2011.
From 2012 to 2014, he held a postdoctoral position with the Computer Science Department, Tel
Aviv University, in collaboration with the Zoology
Department, and Tel Hashomer Hospital, producing new sensing and analysis tools in biology and medicine mainly to
characterize human movement. In 2014, he was a Visiting Scholar with
the Harvard Medical School, where he developed and implemented new
tools to analyze the effect of electrical pulses for terminating cancer cells.
In 2015, he was a Visiting Scholar with the Neuroscience Department,
New York University, where he developed new tools to for non-primate facial
recognition. In 2016, he joined the IBM Thomas J. Watson Research Center,
to explore and quantize human gestures with new sensing and processing
methods, in particular to detect scores to evaluate human and patient with
Parkinson Disease performance. He is currently a Research Associate with
the Faculty of Engineering, Bar Ilan University, where he works to improve
methodologies in neural networks, inspired by the brain.
35467
G. Blumrosen et al.: Back to Finger-Writing: Fingertip Writing Technology Based on Pressure Sensing
KATSUYUKI SAKUMA (Senior Member, IEEE)
received the B.S. and M.S. degrees in mechanical engineering from Tohoku University, Japan,
in 1998 and 2000, respectively, and the Ph.D.
degree in nano science and nano engineering from
Waseda University, Japan, in 2010.
He joined IBM Research, Japan, in 2000.
In 2011, he moved to the IBM Semiconductor Research and Development Center, East
Fishkill, NY, USA. He transferred to the IBM
Thomas J. Watson Research Center, NY, in 2013, where he is currently a
Research Staff Member. He is currently a Visiting Professor with the Department of Biomedical Engineering, Tohoku University, Japan, and also with
National Chiao Tung University, Taiwan. He has authored coauthored four
book chapters, more than 85 peer-reviewed journal articles and conference
proceeding papers, and more than 60 issued or pending U.S. and international
patents.
Dr. Sakuma has been serving as a Committee Member for the IEEE ECTC,
since 2012, for the IEEE 3DIC, since 2016, and for the IEEE IRPS, since
2017. He was a recipient of the Exceptional Technical Achievement Award
from the IEEE Electronics Packaging Society, in 2018, and the Alumni
Achievement Award from the School of Engineering, Tohoku University,
in 2017. He has been recognized with an Outstanding Technical Achievement
Award (OTAA), in 2015, and the IBM Fourteenth Invention Achievement
Award, in 2019. He is currently serving as an Associate Editor for the IEEE
TRANSACTIONS ON CPMT.
JOHN KNICKERBOCKER received the Ph.D.
degree in materials science and engineering from
the University of Illinois, in 1982.
He has over 35 years’ experience at IBM in
development and research. He is currently an
IBM Distinguished Engineer, a member of IBM
Academy, and a Master Inventor. He is also leading research on healthcare sensors and diagnostics
in the Micro-System Technology and Solutions
Team, IBM Thomas J. Watson Research Center,
Yorktown Heights, NY, USA, with the goal of improved patient quality of
life using precision healthcare monitors and diagnostics. He has authored or
coauthored more than 300 patents and patent applications and has more than
85 technical articles, presentations, book chapters, and publications.
Dr. Knickerbocker has received 75 technical awards from IBM and Industry for his work and inventions. He has over 30 years’ participation with
industry technical societies across IEEE, ECTC, and ACerS.
JOHN JEREMY RICE (Member, IEEE) received
the B.S., M.S., and Ph.D. degrees in biomedical engineering from Johns Hopkins University,
Baltimore, MD, USA, in 1987, 1989, and 1997,
respectively.
In 2001, he joined the IBM Thomas J. Watson
Research Center, where he initiated the cardiac
modeling program and championed neuroscience
research efforts. He was a Senior Manager and a
Principal Research Staff Member in IBM’s Healthcare & Life Sciences Organization, where he conducted research in cardiac
modeling and multiscale modeling. In addition to his research, he was an
Adjunct Assistant Professor with the Johns Hopkins School of Biomedical
Engineering, contributed to The William J. Sacco Critical Thinking Foundation which mentors young people in STEM research. Recently, he led the
Blue Sky Project, using real-time data of medical sensors like nail sensors,
and accelerometers to monitor the progression of Parkinson’s disease in
patients.
He was an Active Member of the IBM Research Culture Club.
35468
VOLUME 8, 2020