Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine le...

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Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 12; pp. 2933 - 2943
Main Authors:	Perez-Ibarra, Juan C., Siqueira, Adriano A. G., Krebs, Hermano I.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive algorithms Algorithms Automatic control Foot Gait Gait analysis hidden Markov model Hidden Markov models human biomechanics Inertial sensing devices Learning algorithms Legged locomotion Machine learning Mechanical engineering Movement disorders Neurodegenerative diseases Parameter modification Parkinson's disease Pathology Real time Real-time systems Resistors robotic rehabilitation Sensors Training Treadmills Unsupervised learning Walking wearable sensors Wearable technology
ISSN:	1534-4320, 1558-0210, 1558-0210
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Abstract	Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy (F 1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.
AbstractList	Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson’s disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ([Formula Omitted]-score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages. Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages. Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages. Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy (F 1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.
Author	Siqueira, Adriano A. G. Perez-Ibarra, Juan C. Krebs, Hermano I.
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SubjectTerms	Adaptive algorithms Algorithms Automatic control Foot Gait Gait analysis hidden Markov model Hidden Markov models human biomechanics Inertial sensing devices Learning algorithms Legged locomotion Machine learning Mechanical engineering Movement disorders Neurodegenerative diseases Parameter modification Parkinson's disease Pathology Real time Real-time systems Resistors robotic rehabilitation Sensors Training Treadmills Unsupervised learning Walking wearable sensors Wearable technology
Title	Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach
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