Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements
Abstract
:1. Introduction
2. Materials and Methods
2.1. Electrodes
2.2. sEMG Data Collection Over Six Hours
- Maximum voluntary isometric contractions (MVICs): While seated, the subject was asked to perform MVICs, contracting against a static support for each muscle. For the ECR and FCU, subjects were stabilized by the experimenter with their wrist at a 45° angle, and for the TRI and BIC, subjects were stabilized with their elbow at a 90° angle. Subjects were asked to rest for 5 s, contract against a support for 5 s, then rest for 5 s. Subjects were given loud verbal encouragement during contraction to ensure maximal effort.
- Dynamic range of motion test (Dynamic): Subjects were asked to choose between a 2, 3, and 5 lb weight and perform a dynamic range of motion test with verbal and visual cues. For ECR and FCU, subjects held the weight 45° below a horizontal plane with their forearms parallel to the ground for 5 s, then over 2 s lifted the weight to a 45° angle above the horizontal plane and held the weight at this angle for 6 s, concluding by slowly releasing the weight over 2 s back to 45° below horizontal (ECR: forearm pronated; FCU: forearm supinated). For TRI, subjects held the weight in a lunge position with their upper arm parallel to the ground for 5 s, then over 2 s extended their forearm without moving their upper arm and held the extension for 6 s and concluded by lowering the weight over 2 s. For BIC, subjects were asked to stand and hold the weight with their arm relaxed for 5 s, then with their elbow tucked into their side and forearm supinated to lift the weight to a 90° angle (parallel to the ground) over two s, hold the weight for 6 s, and to conclude by slowly lowering the weight back down over 2 s.
- Functional tests (Functional): For the Jebsen Taylor Hand Function Test [38], subjects were asked to complete the seven Jebsen Taylor tasks (writing, card flipping, small objects, simulated feeding, checkers, light objects, heavy objects) as quickly as possible with their dominant hand. The time taken to fully complete each task was recorded. For the Box & Block Test [39], subjects were asked to move wooden cubes (2.5 cm), one cube at a time from their dominant to non-dominant side over a 19 cm high partition as quickly as possible. Subjects were given a 15 s practice session, followed by a one-minute test moving the cubes. The number of cubes moved over the minute-long test was recorded. The sEMG signals taken from each trial were concatenated for analysis to provide an evaluation of the functional tests as a whole, instead of evaluating individual tasks (i.e., seven separate Jebsen Taylor tasks).
2.3. sEMG Analysis
- Pearson’s correlations: Correlations were calculated by taking the Pearson’s correlation for the linear envelope of the Delsys and ESS electrode. Correlation coefficients values between 0.7 and 1.0 were interpreted as a strong positive linear relationship. We defined a moderate correlation as values between 0.3 and 0.7, and low correlation as between 0.0 and 0.3.
- Signal-to-noise ratio (SNR): Noise was assumed to be any signal present in the upper 20% of the frequency range (above 400 Hz) [31]. The total power across all frequencies was divided by the power of the frequency range above 400 Hz to obtain the SNR. An sEMG signal simulated under ideal conditions with minimum influence of motion and noise artifacts would have an SNR of at least 50 dB, while the sEMG signal influenced by noise would have an SNR of 15 dB or below [31]. A linear regression model was constructed to examine the effect of time, type of muscle, and test on SNR for the Delsys and ESS electrodes. For this calculation, line noise was assumed to be filtered out by the Delsys amplifier for both electrodes.
- Signal-to-motion ratio (SMR): Two assumptions were made to calculate SMR: (1) that all motion noise is under 20 Hz, and (2) that the sEMG signal under 20 Hz is approximately linear [31]. As such, motion noise can be calculated by drawing a line drawn between 0 dB and the peak power between 50 Hz and 150 Hz, and calculating the power above the line from 0 to 20 Hz. The SMR is calculated by dividing the total power across all frequencies by the motion noise. A higher SMR indicates that the sEMG data are less affected by motion artifacts. A linear regression model was also constructed to examine the effect of time and type of muscle on SMR for the Delsys and ESS electrodes.
- Linear regression models: MATLAB was used to run forward stepwise multiple linear regressions (stepwiselm) to determine which variables (time, muscles, tests, and electrodes) were associated with Pearson’s correlation, SNR, and SMR measurements. Fisher transformations were performed on the correlation measurements to obtain normal distributions for analysis. We used the MATLAB function stepwiselm to determine which predictors were important in predicting the output variables to find an optimal linear regression model. Next, the data was fit to the chosen linear regression model using the MATLAB function fitlm to evaluate the magnitude of significant changes with time, muscles, tests, and/or electrodes. While overfitting is a concern to a small sample size of eight participants, the robustops option was used when fitting the linear regression model to make the model less sensitive to outliers that may arise with a small sample. In addition, assumptions of normality were evaluated by plotting and assessing residuals.
2.4. Stability Testing of ESS Electrode over Seven Hours
2.5. Stability Testing of ESS Electrode over Four Days
3. Results
3.1. Comparison of ESS and Delsys Electrodes
3.2. Signal Consistency of ESS Electrodes
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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MVIC | Dynamic | Functional | ||||||
---|---|---|---|---|---|---|---|---|
0 h | 2 h | 4 h | 6 h | 0 h | 6 h | 0 h | 6 h | |
BIC | 0.90 | 0.93 | 0.93 | 0.94 | 0.90 | 0.84 | 0.90 | 0.91 |
ECR | 0.95 | 0.96 | 0.95 | 0.95 | 0.88 | 0.84 | 0.90 | 0.85 |
FCU | 0.91 | 0.93 | 0.96 | 0.95 | 0.91 | 0.91 | 0.87 | 0.87 |
TRI | 0.91 | 0.87 | 0.89 | 0.87 | 0.84 | 0.83 | 0.82 | 0.81 |
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Yamagami, M.; Peters, K.M.; Milovanovic, I.; Kuang, I.; Yang, Z.; Lu, N.; Steele, K.M. Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements. Sensors 2018, 18, 1269. https://doi.org/10.3390/s18041269
Yamagami M, Peters KM, Milovanovic I, Kuang I, Yang Z, Lu N, Steele KM. Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements. Sensors. 2018; 18(4):1269. https://doi.org/10.3390/s18041269
Chicago/Turabian StyleYamagami, Momona, Keshia M. Peters, Ivana Milovanovic, Irene Kuang, Zeyu Yang, Nanshu Lu, and Katherine M. Steele. 2018. "Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements" Sensors 18, no. 4: 1269. https://doi.org/10.3390/s18041269
APA StyleYamagami, M., Peters, K. M., Milovanovic, I., Kuang, I., Yang, Z., Lu, N., & Steele, K. M. (2018). Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements. Sensors, 18(4), 1269. https://doi.org/10.3390/s18041269