Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors
<p>Experiment setup: Four BioKin<math display="inline"> <semantics> <msup> <mrow/> <mi>TM</mi> </msup> </semantics> </math> sensors are attached on the back, connected to computer via wifi.</p> "> Figure 2
<p>Experimental setup and analysis. Figure (<b>a</b>–<b>d</b>) demonstrates movement routine for pouring, pointing, walking and walking around a chair; the second row depicts angular rates in time domain from sensors at the upper and lower; the third row indicates frequency domain (FFT) representation for each of the movement routine. Here, upper sensor (red) and lower sensor (blue), and the dominant frequency band is indicated in a shaded area; the fourth row shows examples of power spectral density of the relevant band forming the feature vectors.</p> "> Figure 3
<p>Unstructured activity analysis for Parkinsonian features: Blue arrows represented the sequence of the upper sensor data processing; red arrows represented the sequence of the lower sensor data processing; and black arrows represented the common sequence of both upper and lower sensor processing.</p> "> Figure 4
<p>(<b>a</b>) Damping ratio (<math display="inline"> <semantics> <mi>ζ</mi> </semantics> </math>) and resonant frequency (<math display="inline"> <semantics> <msub> <mi>ω</mi> <mn>0</mn> </msub> </semantics> </math>) deduced from the pointing test; (<b>b</b>) distance between angular rate of each pair of sensors using dynamic time warping (DTW).</p> "> Figure 5
<p>Mean (<math display="inline"> <semantics> <mi>μ</mi> </semantics> </math>) ± standard variation (<math display="inline"> <semantics> <mi>σ</mi> </semantics> </math>) of the delay time between sensors: (<b>a</b>) Mean (<math display="inline"> <semantics> <mi>μ</mi> </semantics> </math>) ± standard variation (<math display="inline"> <semantics> <mi>σ</mi> </semantics> </math>) of the delay time between Sensor 2 and Sensor 3, (<b>b</b>) Mean (<math display="inline"> <semantics> <mi>μ</mi> </semantics> </math>) ± standard variation (<math display="inline"> <semantics> <mi>σ</mi> </semantics> </math>) of the delay time between Sensor 2 and Sensor 4.</p> "> Figure 6
<p>Frequency domain representation of angular rate from the upper sensor and formation of the feature vector with information from the lower sensor.</p> "> Figure 7
<p>Optimisation for parameters selection.</p> "> Figure 8
<p>Principal component description of the four movement routines ((<b>a</b>) pouring, (<b>b</b>) pointing, (<b>c</b>) walking, and (<b>d</b>) walking around a chair) from Sensor 2 and 3.</p> "> Figure 9
<p>Principal component description of the four movement routines ((<b>a</b>) pouring, (<b>b</b>) pointing, (<b>c</b>) walking, and (<b>d</b>) walking around a chair) from Sensor 2 and 4.</p> "> Figure 10
<p>Combination of pouring, pointing, walking, and walking around a chair from Sensor 2 vs. 3.</p> ">
Abstract
:1. Introduction
2. Experimental Setup
3. Activity Analysis
3.1. Resonant Frequency and Damping Coefficient with a Single Sensor
3.2. Truncal Flexibility Analysis with Two Sensors
3.2.1. Minimising the Number of Sensors
3.2.2. Flexibility Analysis with Time Delay Information
3.2.3. Principal Component Analysis with Optimised Feature Parameters
Feature Extraction
Numerical Optimisation of Feature Parameters Using Silhouette Coefficient
4. Experimental Results
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Jankovic, J.; McDermott, M.; Carter, J.; Gauthier, S.; Goetz, C.; Golbe, L.; Huber, S.; Koller, W.; Olanow, C.; Shoulson, I.; et al. Variable expression of Parkinson’s disease: A base-line analysis of the DATATOP cohort. Neurology 1990, 40, 1529–1534. [Google Scholar] [CrossRef] [PubMed]
- Lees, A.J.; Hardy, J.; Revesz, T. Parkinson’s disease. Lancet 2009, 373, 2055–2066. [Google Scholar] [CrossRef]
- Griffiths, R.I.; Kotschet, K.; Arfon, S.; Xu, Z.M.; Johnson, W.; Drago, J.; Evans, A.; Kempster, P.; Raghav, S.; Horne, M.K. Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J. Parkinson’s Dis. 2012, 2, 47–55. [Google Scholar]
- Tzallas, A.T.; Tsipouras, M.G.; Rigas, G.; Tsalikakis, D.G.; Karvounis, E.C.; Chondrogiorgi, M.; Psomadellis, F.; Cancela, J.; Pastorino, M.; Waldmeyer, M.T.A.; et al. PERFORM: A system for monitoring, assessment and management of patients with Parkinson’s disease. Sensors 2014, 14, 21329–21357. [Google Scholar] [CrossRef] [PubMed]
- Memedi, M.; Sadikov, A.; Groznik, V.; Žabkar, J.; Možina, M.; Bergquist, F.; Johansson, A.; Haubenberger, D.; Nyholm, D. Automatic spiral analysis for objective assessment of motor symptoms in Parkinson’s disease. Sensors 2015, 15, 23727–23744. [Google Scholar] [CrossRef] [PubMed]
- Maetzler, W.; Klucken, J.; Horne, M. A clinical view on the development of technology-based tools in managing Parkinson’s disease. Mov. Disord. 2016, 31, 1263–1271. [Google Scholar] [CrossRef] [PubMed]
- Buckley, C.; Galna, B.; Rochester, L.; Mazza, C. Quantification of upper body movements during gait in older adults and in those with Parkinson’s disease: Impact of acceleration realignment methodologies. Gait Posture 2017, 52, 265–271. [Google Scholar] [CrossRef] [PubMed]
- Buckley, C.; Galna, B.; Rochester, L.; Mazza, C. Attenuation of Upper Body Accelerations during Gait: Piloting an Innovative Assessment Tool for Parkinson’s Disease. Biomed. Res. Int. 2015, 2015, 865873. [Google Scholar] [CrossRef] [PubMed]
- Berardelli, A.; Rothwell, J.C.; Thompson, P.D.; Hallett, M. Pathophysiology of bradykinesia in Parkinson’s disease. Brain 2001, 124, 2131–2146. [Google Scholar] [CrossRef] [PubMed]
- Hoehn, M.M.; Yahr, M.D. Parkinsonism onset, progression, and mortality. Neurology 1967, 17, 427–442. [Google Scholar] [CrossRef] [PubMed]
- Ekanayake, S.W.; Morris, A.J.; Forrester, M.; Pathirana, P.N. Biokin: An ambulatory platform for gait kinematic and feature assessment. Healthc. Technol. Lett. 2015, 2, 40–45. [Google Scholar] [CrossRef] [PubMed]
- Steiger, M.J.; Thompson, P.D.; Marsden, C.D. Disordered axial movement in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 1996, 61, 645–648. [Google Scholar] [CrossRef] [PubMed]
- Lawrence, D.G.; Kuypers, H.G. The functional organization of the motor system in the monkey. I. The effects of bilateral pyramidal lesions. Brain 1968, 91, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Jankovic, J. Parkinson’s disease: Clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 2008, 79, 368–376. [Google Scholar] [CrossRef] [PubMed]
- Van Emmerik, R.E.A.; Wagenaar, R.C. Dynamics of movement coordination and tremor during gait in Parkinson’s disease. Hum. Mov. Sci. 1996, 15, 203–235. [Google Scholar] [CrossRef]
- Chou, P.Y.; Lee, S.C. Turning deficits in people with Parkinson’s disease. Tzu Chi Med. J. 2013, 25, 200–202. [Google Scholar] [CrossRef]
- Bergman, H.; Deuschl, G. Pathophysiology of Parkinson’s disease: From clinical neurology to basic neuroscience and back. Mov. Disord. 2002, 17, 28–40. [Google Scholar] [CrossRef] [PubMed]
- DeMichele, P.L.; Pollock, M.L.; Graves, J.E.; Foster, D.N.; Carpenter, D.; Garzarella, L.; Brechue, W.; Fulton, M. Isometric torso rotation strength: Effect of training frequency on its development. Arch. Phys. Med. Rehabil. 1997, 78, 64–69. [Google Scholar] [CrossRef]
- Johnson, S.G. Notes on FFT-Based Differentiation; MIT Applied Mathematics: Cambridge, MA, USA, 2011. [Google Scholar]
- Sakoe, H.; Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 1978, 26, 43–49. [Google Scholar] [CrossRef]
- Frigo, M.; Johnson, S.G. FFTW: An adaptive software architecture for the FFT. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, WA, USA, 15 May 1998; pp. 1381–1384. [Google Scholar]
- Campbell, M.C.; Markham, J.; Flores, H.; Hartlein, J.M.; Goate, A.M.; Cairns, N.J.; Videen, T.O.; Perlmutter, J.S. Principal component analysis of PiB distribution in Parkinson and Alzheimer diseases. Neurology 2013, 81, 520–527. [Google Scholar] [CrossRef] [PubMed]
- Dillmann, U.; Holzhoffer, C.; Johann, Y.; Bechtel, S.; Gräber, S.; Massing, C.; Spiegel, J.; Behnke, S.; Bürmann, J.; Louis, A.K. Principal Component Analysis of gait in Parkinson’s disease: Relevance of gait velocity. Gait Posture 2014, 39, 882–887. [Google Scholar] [CrossRef] [PubMed]
- Jolliffe, I.T. Principal Component Analysis; Springer: New York, NY, USA, 2002. [Google Scholar]
- Andò, B.; Baglio, S.; Marletta, V.; Pistorio, A.; Dibilio, V.; Mostile, G.; Nicoletti, A.; Zappia, M. A Wearable Device to Support the Pull Test for Postural Instability Assessment in Parkinson’s Disease. IEEE Trans. Instrum. Meas. 2018, 67, 218–228. [Google Scholar] [CrossRef]
Activity | Controls | Patients | Total | H&Y Score |
---|---|---|---|---|
Pouring | 8 | 15 | 23 | 1.67 ± 1.6 |
Pointing | 5 | 14 | 19 | 2 ± 1.3 |
Walking | 7 | 12 | 19 | 1.64 ± 1.3 |
Walking around a chair | 6 | 15 | 21 | 1.68 ± 1.4 |
Activity | Number of Bins | Cutoff | Window Size | Frequency Band | Silhouette Coefficient |
---|---|---|---|---|---|
Pouring | 19 | 4.5 | 0.5 | [4.5–5] | 0.27 > 0 |
Pointing | 10 | 6.4 | 1.2 | [6.4–7.6] | 0.47 > 0 |
Walking | 9 | 3.5 | 1.5 | [3.5–5] | 0.34 > 0 |
Walking around a chair | 3 | 0.2 | 2.4 | [0.2–2.6] | 0.34 > 0 |
Activity | Number of Bins | Cutoff | Window Size | Frequency Band | Silhouette Coefficient |
---|---|---|---|---|---|
Pouring | 24 | 2.25 | 7 | [2.25–9.25] | 0.33 > 0 |
Pointing | 23 | 0.6 | 2.6 | [0.6–3.2] | 0.43 > 0 |
Walking | 16 | 4.25 | 5 | [4.25–9.25] | 0.32 > 0 |
Walking around a chair | 11 | 0.2 | 2.2 | [0.2–2.4] | 0.31 > 0 |
Activity | Pearson Correlation | ||
---|---|---|---|
PC1 | PC2 | PC3 | |
Pouring | −0.21 | 0.78 | 0.002 |
Pointing | 0.62 | −0.94 | −0.13 |
Walking | −0.56 | 0.73 | −0.27 |
Walking around a chair | −0.75 | 0.61 | 0.93 |
Combination of all tests | −0.81 | −0.16 | 0.52 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Phan, D.; Horne, M.; Pathirana, P.N.; Farzanehfar, P. Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors. Sensors 2018, 18, 495. https://doi.org/10.3390/s18020495
Phan D, Horne M, Pathirana PN, Farzanehfar P. Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors. Sensors. 2018; 18(2):495. https://doi.org/10.3390/s18020495
Chicago/Turabian StylePhan, Dung, Malcolm Horne, Pubudu N. Pathirana, and Parisa Farzanehfar. 2018. "Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors" Sensors 18, no. 2: 495. https://doi.org/10.3390/s18020495