Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡
<p>Features of the skin conductance response (SCR). The skin conductance level (SCL) was computed as the average skin conductance (SC) value across a 64 s window, SCR_Dur is the time from the beginning of the SCR to the 50% amplitude level, and SCR_Amp is the amplitude from the minimum to the maximum of an SCR.</p> "> Figure 2
<p>The physiological signal, <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math>, has a sparse representation in the wavelet domain, <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mn>4</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, through the application of a multilevel discrete wavelet transformation. This technique was applied within the microcontroller to allow for effective signal compression. Each level of the transformation represents low-passed and band-passed versions of the original signal at different scales.</p> "> Figure 3
<p>System diagram of developed electrodermal activity sensor. <span class="html-italic">ADC</span> = analog-to-digital converter, <span class="html-italic">EDA</span> = electrodermal activity, <span class="html-italic">LPF</span> = low pass filter, <span class="html-italic">LVR</span> = linear voltage regulator, <span class="html-italic">FRAM</span> = ferroelectric random access memory, <span class="html-italic">UART</span> = universal asynchronous receiver-transmitter serial communication protocol, <span class="html-italic">X</span> = skin conductance samples.</p> "> Figure 4
<p>The complete EDA sensor assembly is shown above. Two, Ag/AgCl electrodes (1 cm diameter) were fastened along the inside of the wristband and measured electrodermal activity at the ventral wrist. Velcro strips were used to provide flexible sensor adjustment and to ensure a tight fit. The footprint of the printed circuit board (PCB) shown is 3.73 cm<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>. The system additionally has a three-axis accelerometer, skin temperature sensor, and event marker, although they were not used in this study.</p> "> Figure 5
<p>The collection of electrodermal activity signals used to evaluate the compression distortion. These 14 EDA signals were subdivided into 253 segments each representing 64 s of EDA.</p> "> Figure 6
<p>The distribution of wavelet coefficient values for the approximation vector, <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">4</mn> </msub> </semantics></math>, and detail coefficients, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">D</mi> <mn mathvariant="bold">4</mn> </msub> <mo>−</mo> <msub> <mi mathvariant="bold-italic">D</mi> <mn mathvariant="bold">1</mn> </msub> </mrow> </semantics></math>, that compose the 1D wavelet transformation, <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">W</mi> <mn mathvariant="bold">4</mn> </msub> </semantics></math>. A total of 253 EDA signals were transformed using the ML-DWT algorithm and, for each signal, the maximum magnitude of the wavelet coefficient was recorded. This distribution is used to define the maximum bits required to store the wavelet coefficients.</p> "> Figure 7
<p>An EDA signal is shown in blue along with a reconstructed EDA signal using the newly developed compression method in red. The previous compression method from Pope et al. 2018 [<a href="#B9-sensors-19-02450" class="html-bibr">9</a>] is shown in green for comparison. The original signal is compressed from a data rate of 64 bits/s to 3.75 bits/s in both methods (CR = 17.1×) (The input data rate in [<a href="#B9-sensors-19-02450" class="html-bibr">9</a>] used a signed 16-bit representation of skin conductance values instead of the more appropriate 32-bit float representation used here and in [<a href="#B10-sensors-19-02450" class="html-bibr">10</a>]. Therefore, the CRs reported in [<a href="#B9-sensors-19-02450" class="html-bibr">9</a>] should be doubled for comparison.) The developed method in red is able to encode 18 total <math display="inline"><semantics> <msub> <mi>W</mi> <mn>4</mn> </msub> </semantics></math> coefficients, whereas the previous method in green is only capable of encoding the top 14 coefficients leading to a 31.8% improvement in root mean square error (RMSErr). Both reconstructions are composed of four, 64-s compression/decompression cycles spliced together.</p> "> Figure 8
<p>The root mean square error (RMSErr) is shown in (<b>A</b>) of 253 EDA signals at a range of compression ratios. The mean RMSErr at each compression ratio is indicated by red triangles. The percent root mean square difference (PRD) distortion in (<b>B</b>) is minimal for CRs below 14.2× while the upper quartile range remains below 1% for CRs up to 19.7×.</p> "> Figure 9
<p>The absolute reconstruction errors of four EDA features computed on 253 EDA signals that were collected during in-laboratory stress tests. The SCL (EDA mean) and standard deviation are hardly effected by compression. While the low-passing filtering effect of compressing the 1D array of wavelet coefficients introduces larger error on the EDA maximum and minimum at higher compression ratios, error within the interquartile remains below 0.015 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>S for CRs up to CR = 23.3×.</p> "> Figure 10
<p>The relative error for the phasic EDA features—Sum of Area Under the Curve, Sum of SCR amplitudes, and the Sum Duration over 253 EDA signals. Reconstruction errors are increased significantly above compression ratios of 8.8×, due to the loss of low-amplitude SCRs not being during the compression process.</p> "> Figure 11
<p>Measured current consumption for the entire system across operational modes with a supply voltage of 2.8 V. The average current draw is 16.6 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>A during deep sleep mode, 232 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>A for sampling the EDA signal, and 280 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>A while processing the ML-DWT.</p> "> Figure 12
<p>The absolute error of the EDA sensor across a range of conductance values using the compression ratio (CR = 17.1×). Each error measurement consists of 100 measurements of a known, fixed resistor having a conductance equal to <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mi>R</mi> </mrow> </semantics></math>.</p> "> Figure A1
<p>EDA sensor amplifier.</p> "> Figure A2
<p>Anti-aliasing filter design.</p> ">
Abstract
:1. Introduction
- An optimized embedded EDA compression algorithm for 16-bit MCU architectures that improves upon the initial work from Pope and Halter [9].
- A compression performance comparison of this low-resource EDA compression method to a recent compressive sensing (CS) method from Chaspari et al. [10].
- Quantification of the compression distortion on common tonic and phasic EDA signal features frequently used in affective computing research.
- Demonstration of improved power performance of compressing and storing EDA signal within a single 16-bit microcontroller as compared to methods requiring external memory.
1.1. Electrodermal Activity
1.2. Wavelet Transformations
Data Compression
2. Materials and Methods
2.1. System Description
2.2. Analog Front End
2.3. Microcontroller
2.4. On-Chip Signal Compression
2.4.1. Wavelet Transformation of EDA Signal
Algorithm 1 ML-DWT Algorithm |
|
2.4.2. Sorting Wavelet Coefficients
Algorithm 2 ML-DWT Compression |
|
2.4.3. Encoding Wavelet Coefficients
2.5. Reconstruction
2.6. Evaluation and Performance Metrics
2.6.1. Compression Ratio
2.6.2. Compression Distortion
2.6.3. Energy Compaction
2.6.4. EDA Feature Reconstruction Errors
3. Results
3.1. Compression Performance
3.2. EDA Feature Performance
3.3. Sensor Performance
3.4. EDA Recording Experience
4. Discussion
5. Conclusions
6. Patents
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. EDA Sensor Circuitry
Appendix B. Analog Low Pass Filter Design
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Bit | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15 | 14 | 13 | 12 | 11 | 10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 0 |
... | ... | ||||||||||||||
: | : | : |
WT Level | Max Value | Max Base 2 | Bits Required | Sign Bit? | Bitwidth Selected |
---|---|---|---|---|---|
2399.53 | 11.2285 | 12 | No | 12 | |
123.881 | 6.95281 | 7 | Yes | 8 | |
73.2285 | 6.1943 | 7 | Yes | 8 | |
43.2475 | 5.43454 | 6 | Yes | 8 | |
21.7329 | 4.44181 | 5 | Yes | 8 | |
145 | 7.17991 | 8 | No | 8 |
WT Vector | Mean %Energy | Std |
---|---|---|
99.98% | 0.04453% | |
0.01125% | 0.02632% | |
0.006232% | 0.01582% | |
0.002031% | 0.006512% | |
0.0008842% | 0.004310% |
Compression Ratio (CR) | Recording Duration (hours) |
---|---|
0 | 0.60 |
4.20 | 2.52 |
8.80 | 5.28 |
14.20 | 8.52 |
17.10 | 10.26 |
19.70 | 11.82 |
23.30 | 13.98 |
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Pope, G.C.; Halter, R.J. Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡. Sensors 2019, 19, 2450. https://doi.org/10.3390/s19112450
Pope GC, Halter RJ. Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡. Sensors. 2019; 19(11):2450. https://doi.org/10.3390/s19112450
Chicago/Turabian StylePope, Gunnar C., and Ryan J. Halter. 2019. "Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡" Sensors 19, no. 11: 2450. https://doi.org/10.3390/s19112450