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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Veröffentlicht in:IEEE transactions on neural systems and rehabilitation engineering Jg. 28; H. 12; S. 2933 - 2943
Hauptverfasser: Perez-Ibarra, Juan C., Siqueira, Adriano A. G., Krebs, Hermano I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2020.3039999