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Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques
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 s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928851/ https://www.ncbi.nlm.nih.gov/pubmed/31795151 http://dx.doi.org/10.3390/s19235227 |
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author | Gurchiek, Reed D. Cheney, Nick McGinnis, Ryan S. |
author_facet | Gurchiek, Reed D. Cheney, Nick McGinnis, Ryan S. |
author_sort | Gurchiek, Reed D. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6928851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69288512019-12-26 Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques Gurchiek, Reed D. Cheney, Nick McGinnis, Ryan S. Sensors (Basel) Review 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. MDPI 2019-11-28 /pmc/articles/PMC6928851/ /pubmed/31795151 http://dx.doi.org/10.3390/s19235227 Text en © 2019 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/). |
spellingShingle | Review Gurchiek, Reed D. Cheney, Nick McGinnis, Ryan S. Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques |
title | Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques |
title_full | Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques |
title_fullStr | Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques |
title_full_unstemmed | Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques |
title_short | Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques |
title_sort | estimating biomechanical time-series with wearable sensors: a systematic review of machine learning techniques |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928851/ https://www.ncbi.nlm.nih.gov/pubmed/31795151 http://dx.doi.org/10.3390/s19235227 |
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