Automatic detection of Spasticity from flexible wearable sensors

Luca Lonini; Nicholas Shawen; Roozbeh Ghaffari; John Rogers; Arun Jayarman

doi:10.1145/3123024.3123098

Automatic detection of Spasticity from flexible wearable sensors

Luca Lonini, Nicholas Shawen, Roozbeh Ghaffari, John Rogers, Arun Jayarman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

Spasticity is a condition that impairs voluntary muscle movements and physically debilitates persons across several neurological disorders, including stroke, multiple sclerosis and cerebral palsy. Assessing the progression of spasticity during clinical interventions and at home is key to rehabilitation efficacy and care management. Here we present electromyography (EMG) and motion data using skin-mounted, flexible and wireless sensors in a cohort of 13 individuals with stroke. We compute a set of 15 features from the EMG data and use machine learning to infer whether spasticity is present during movements of the knee and ankle joints. Using a Linear Discriminant Analysis (LDA) classifier, we show that our approach successfully discriminates voluntary contractions from spastic muscle contractions (AUC=0.94). These results show that continuous and non-invasive monitoring of spasticity symptoms could be applied to optimize and personalize rehabilitation regimens.

Original language	English (US)
Title of host publication	UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
Publisher	Association for Computing Machinery, Inc
Pages	133-136
Number of pages	4
ISBN (Electronic)	9781450351904
DOIs	https://doi.org/10.1145/3123024.3123098
State	Published - Sep 11 2017
Event	2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017 - Maui, United States Duration: Sep 11 2017 → Sep 15 2017

Publication series

Name	UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

Other

Other	2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017
Country/Territory	United States
City	Maui
Period	9/11/17 → 9/15/17

Funding

This work was funded by the Max Näder Lab.

Keywords

Flexible electronics
Machine learning
Rehabilitation

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Networks and Communications

Access to Document

10.1145/3123024.3123098

Cite this

Lonini, L., Shawen, N., Ghaffari, R., Rogers, J., & Jayarman, A. (2017). Automatic detection of Spasticity from flexible wearable sensors. In UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 133-136). (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123024.3123098

Lonini, Luca ; Shawen, Nicholas ; Ghaffari, Roozbeh et al. / Automatic detection of Spasticity from flexible wearable sensors. UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc, 2017. pp. 133-136 (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers).

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title = "Automatic detection of Spasticity from flexible wearable sensors",

abstract = "Spasticity is a condition that impairs voluntary muscle movements and physically debilitates persons across several neurological disorders, including stroke, multiple sclerosis and cerebral palsy. Assessing the progression of spasticity during clinical interventions and at home is key to rehabilitation efficacy and care management. Here we present electromyography (EMG) and motion data using skin-mounted, flexible and wireless sensors in a cohort of 13 individuals with stroke. We compute a set of 15 features from the EMG data and use machine learning to infer whether spasticity is present during movements of the knee and ankle joints. Using a Linear Discriminant Analysis (LDA) classifier, we show that our approach successfully discriminates voluntary contractions from spastic muscle contractions (AUC=0.94). These results show that continuous and non-invasive monitoring of spasticity symptoms could be applied to optimize and personalize rehabilitation regimens.",

keywords = "Flexible electronics, Machine learning, Rehabilitation",

author = "Luca Lonini and Nicholas Shawen and Roozbeh Ghaffari and John Rogers and Arun Jayarman",

note = "Funding Information: This work was funded by the Max N{\"a}der Lab. Publisher Copyright: Copyright {\textcopyright} 2017 ACM.; 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017 ; Conference date: 11-09-2017 Through 15-09-2017",

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Lonini, L, Shawen, N, Ghaffari, R , Rogers, J & Jayarman, A 2017, Automatic detection of Spasticity from flexible wearable sensors. in UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, Association for Computing Machinery, Inc, pp. 133-136, 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017, Maui, United States, 9/11/17. https://doi.org/10.1145/3123024.3123098

Automatic detection of Spasticity from flexible wearable sensors. / Lonini, Luca; Shawen, Nicholas; Ghaffari, Roozbeh et al.
UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc, 2017. p. 133-136 (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Lonini, Luca

AU - Shawen, Nicholas

AU - Ghaffari, Roozbeh

AU - Rogers, John

AU - Jayarman, Arun

PY - 2017/9/11

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N2 - Spasticity is a condition that impairs voluntary muscle movements and physically debilitates persons across several neurological disorders, including stroke, multiple sclerosis and cerebral palsy. Assessing the progression of spasticity during clinical interventions and at home is key to rehabilitation efficacy and care management. Here we present electromyography (EMG) and motion data using skin-mounted, flexible and wireless sensors in a cohort of 13 individuals with stroke. We compute a set of 15 features from the EMG data and use machine learning to infer whether spasticity is present during movements of the knee and ankle joints. Using a Linear Discriminant Analysis (LDA) classifier, we show that our approach successfully discriminates voluntary contractions from spastic muscle contractions (AUC=0.94). These results show that continuous and non-invasive monitoring of spasticity symptoms could be applied to optimize and personalize rehabilitation regimens.

AB - Spasticity is a condition that impairs voluntary muscle movements and physically debilitates persons across several neurological disorders, including stroke, multiple sclerosis and cerebral palsy. Assessing the progression of spasticity during clinical interventions and at home is key to rehabilitation efficacy and care management. Here we present electromyography (EMG) and motion data using skin-mounted, flexible and wireless sensors in a cohort of 13 individuals with stroke. We compute a set of 15 features from the EMG data and use machine learning to infer whether spasticity is present during movements of the knee and ankle joints. Using a Linear Discriminant Analysis (LDA) classifier, we show that our approach successfully discriminates voluntary contractions from spastic muscle contractions (AUC=0.94). These results show that continuous and non-invasive monitoring of spasticity symptoms could be applied to optimize and personalize rehabilitation regimens.

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M3 - Conference contribution

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Lonini L, Shawen N, Ghaffari R , Rogers J, Jayarman A. Automatic detection of Spasticity from flexible wearable sensors. In UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc. 2017. p. 133-136. (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers). doi: 10.1145/3123024.3123098

Automatic detection of Spasticity from flexible wearable sensors

Abstract

Publication series

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Funding

Keywords

ASJC Scopus subject areas

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