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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Perez-Ibarra, Juan C. [1, 2] ; Siqueira, Adriano A. G. [2, 3, 4] ; Krebs, Hermano I. [1, 5, 6, 7, 8, 9, 10]
Total Authors: 3
Affiliation:
[1] MIT, Dept Mech Engn, Cambridge, MA 02139 - USA
[2] Univ Sao Paulo, Dept Mech Engn, BR-13566590 Sao Carlos - Brazil
[3] Univ Sao Paulo, Ctr Adv Studies Rehabil, BR-13566590 Sao Carlos - Brazil
[4] Univ Sao Paulo, Ctr Robot Sao Carlos, BR-13566590 Sao Carlos - Brazil
[5] Univ Maryland, Sch Med, Dept Neurol, Baltimore, MD 21201 - USA
[6] Fujita Hlth Univ, Sch Med, Dept Rehabil Med 1, Toyoake, Aichi 4701192 - Japan
[7] Newcastle Univ, Inst Neurosci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear - England
[8] Osaka Univ, Dept Mech Sci & Bioengn, Osaka 5650871 - Japan
[9] Wolfson Sch Mech Elect & Mfg, Loughborough LE11 3TU, Leics - England
[10] Sogang Univ, Coll Engn, Seoul 04107 - South Korea
Total Affiliations: 10
Document type: Journal article
Source: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING; v. 28, n. 12, p. 2933-2943, DEC 2020.
Web of Science Citations: 0
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 algorithmsdemonstrated the potential to learn optimal parameters for a particular participant and for detecting gait eventswithout additional sensors, external labeling, or long training stages. (AU)

FAPESP's process: 15/50376-2 - Adaptive variable impedance applied to robotic rehabilitation of walking
Grantee:Adriano Almeida Gonçalves Siqueira
Support type: Regular Research Grants
FAPESP's process: 13/14756-0 - Adaptive variable impedance applied to robotic rehabilitation of walking
Grantee:Adriano Almeida Gonçalves Siqueira
Support type: Regular Research Grants