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Review

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

1
M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA
2
Dept. of Computer Science, University of Vermont, Burlington, VT 05405, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5227; https://doi.org/10.3390/s19235227
Received: 31 October 2019 / Revised: 19 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
(This article belongs to the Section Physical Sensors)
Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption. View Full-Text
Keywords: machine learning; hybrid estimation; wearable sensors; electromyography; inertial sensor; regression; remote patient monitoring; joint mechanics machine learning; hybrid estimation; wearable sensors; electromyography; inertial sensor; regression; remote patient monitoring; joint mechanics
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MDPI and ACS Style

Gurchiek, R.D.; Cheney, N.; McGinnis, R.S. Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques. Sensors 2019, 19, 5227. https://doi.org/10.3390/s19235227

AMA Style

Gurchiek RD, Cheney N, McGinnis RS. Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques. Sensors. 2019; 19(23):5227. https://doi.org/10.3390/s19235227

Chicago/Turabian Style

Gurchiek, Reed D., Nick Cheney, and Ryan S. McGinnis 2019. "Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques" Sensors 19, no. 23: 5227. https://doi.org/10.3390/s19235227

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