Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited r...

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Autores principales:	Pascale Juneau, Natalie Baddour, Helena Burger, Andrej Bavec, Edward D. Lemaire
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	6MWT foot strike detection amputee stride parameters machine learning decision tree Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8a5a901a3cba4a58b911f86b09e045ee
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spelling	oai:doaj.org-article:8a5a901a3cba4a58b911f86b09e045ee2021-11-11T19:01:22ZComparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations10.3390/s212169741424-8220https://doaj.org/article/8a5a901a3cba4a58b911f86b09e045ee2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6974https://doaj.org/toc/1424-8220Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.Pascale JuneauNatalie BaddourHelena BurgerAndrej BavecEdward D. LemaireMDPI AGarticle6MWTfoot strike detectionamputeestride parametersmachine learningdecision treeChemical technologyTP1-1185ENSensors, Vol 21, Iss 6974, p 6974 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	6MWT foot strike detection amputee stride parameters machine learning decision tree Chemical technology TP1-1185
spellingShingle	6MWT foot strike detection amputee stride parameters machine learning decision tree Chemical technology TP1-1185 Pascale Juneau Natalie Baddour Helena Burger Andrej Bavec Edward D. Lemaire Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
description	Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.
format	article
author	Pascale Juneau Natalie Baddour Helena Burger Andrej Bavec Edward D. Lemaire
author_facet	Pascale Juneau Natalie Baddour Helena Burger Andrej Bavec Edward D. Lemaire
author_sort	Pascale Juneau
title	Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
title_short	Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
title_full	Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
title_fullStr	Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
title_full_unstemmed	Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
title_sort	comparison of decision tree and long short-term memory approaches for automated foot strike detection in lower extremity amputee populations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8a5a901a3cba4a58b911f86b09e045ee
work_keys_str_mv	AT pascalejuneau comparisonofdecisiontreeandlongshorttermmemoryapproachesforautomatedfootstrikedetectioninlowerextremityamputeepopulations AT nataliebaddour comparisonofdecisiontreeandlongshorttermmemoryapproachesforautomatedfootstrikedetectioninlowerextremityamputeepopulations AT helenaburger comparisonofdecisiontreeandlongshorttermmemoryapproachesforautomatedfootstrikedetectioninlowerextremityamputeepopulations AT andrejbavec comparisonofdecisiontreeandlongshorttermmemoryapproachesforautomatedfootstrikedetectioninlowerextremityamputeepopulations AT edwarddlemaire comparisonofdecisiontreeandlongshorttermmemoryapproachesforautomatedfootstrikedetectioninlowerextremityamputeepopulations
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Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

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