Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke
Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated b...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.877563/full |
| _version_ | 1811234034935660544 |
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| author | Charlotte Werner Charlotte Werner Josef G. Schönhammer Marianne K. Steitz Olivier Lambercy Olivier Lambercy Andreas R. Luft Andreas R. Luft László Demkó Chris Awai Easthope |
| author_facet | Charlotte Werner Charlotte Werner Josef G. Schönhammer Marianne K. Steitz Olivier Lambercy Olivier Lambercy Andreas R. Luft Andreas R. Luft László Demkó Chris Awai Easthope |
| author_sort | Charlotte Werner |
| collection | DOAJ |
| description | Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our study, 21 patients performed movement tasks of the Action Research Arm Test (ARAT), one of the most widely used clinical tests of upper limb motor functioning, while trained evaluators scored each task on pre-defined criteria. We collected data with just two wrist-worn inertial sensors to guarantee applicability across the continuum of care and used machine learning algorithms to estimate the ARAT task scores from sensor-derived features. Tasks scores were classified with approximately 80% accuracy. Linear regression between summed clinical task scores (across all tasks per patient) and estimates of sum task scores yielded a good fit (R2 = 0.93; range reported in previous studies: 0.61–0.97). Estimates of the sum scores showed a mean absolute error of 2.9 points, 5.1% of the total score, which is smaller than the minimally detectable change and minimally clinically important difference of the ARAT when rated by a trained evaluator. We conclude that it is feasible to obtain accurate estimates of ARAT scores with just two wrist worn sensors. The approach enables administration of the ARAT in an objective, minimally supervised or remote fashion and provides the basis for a widespread use of wearable sensors in neurorehabilitation. |
| first_indexed | 2024-04-12T11:29:47Z |
| format | Article |
| id | doaj.art-61df883b81464f0b82697487aeba694a |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-12T11:29:47Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-61df883b81464f0b82697487aeba694a2022-12-22T03:35:02ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-05-011310.3389/fphys.2022.877563877563Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in StrokeCharlotte Werner0Charlotte Werner1Josef G. Schönhammer2Marianne K. Steitz3Olivier Lambercy4Olivier Lambercy5Andreas R. Luft6Andreas R. Luft7László Demkó8Chris Awai Easthope9Spinal Cord Injury Research Center, University Hospital Balgrist, Zurich, SwitzerlandRehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, SwitzerlandCereneo Foundation, Center for Interdisciplinary Research (CEFIR), Vitznau, SwitzerlandDivision of Vascular Neurology and Neurorehabilitation, Department of Neurology and Clinical Neuroscience Center, University of Zurich and University Hospital Zurich, Zurich, SwitzerlandRehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, SwitzerlandFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Zurich, SingaporeDivision of Vascular Neurology and Neurorehabilitation, Department of Neurology and Clinical Neuroscience Center, University of Zurich and University Hospital Zurich, Zurich, SwitzerlandCereneo, Center for Neurology and Rehabilitation, Vitznau, SwitzerlandSpinal Cord Injury Research Center, University Hospital Balgrist, Zurich, SwitzerlandCereneo Foundation, Center for Interdisciplinary Research (CEFIR), Vitznau, SwitzerlandNeurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our study, 21 patients performed movement tasks of the Action Research Arm Test (ARAT), one of the most widely used clinical tests of upper limb motor functioning, while trained evaluators scored each task on pre-defined criteria. We collected data with just two wrist-worn inertial sensors to guarantee applicability across the continuum of care and used machine learning algorithms to estimate the ARAT task scores from sensor-derived features. Tasks scores were classified with approximately 80% accuracy. Linear regression between summed clinical task scores (across all tasks per patient) and estimates of sum task scores yielded a good fit (R2 = 0.93; range reported in previous studies: 0.61–0.97). Estimates of the sum scores showed a mean absolute error of 2.9 points, 5.1% of the total score, which is smaller than the minimally detectable change and minimally clinically important difference of the ARAT when rated by a trained evaluator. We conclude that it is feasible to obtain accurate estimates of ARAT scores with just two wrist worn sensors. The approach enables administration of the ARAT in an objective, minimally supervised or remote fashion and provides the basis for a widespread use of wearable sensors in neurorehabilitation.https://www.frontiersin.org/articles/10.3389/fphys.2022.877563/fullinertial sensorrehabilitationwearablesclinical assessmentstrokeARAT |
| spellingShingle | Charlotte Werner Charlotte Werner Josef G. Schönhammer Marianne K. Steitz Olivier Lambercy Olivier Lambercy Andreas R. Luft Andreas R. Luft László Demkó Chris Awai Easthope Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke Frontiers in Physiology inertial sensor rehabilitation wearables clinical assessment stroke ARAT |
| title | Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke |
| title_full | Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke |
| title_fullStr | Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke |
| title_full_unstemmed | Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke |
| title_short | Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke |
| title_sort | using wearable inertial sensors to estimate clinical scores of upper limb movement quality in stroke |
| topic | inertial sensor rehabilitation wearables clinical assessment stroke ARAT |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.877563/full |
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