Identification of Gait Events in Healthy and Parkinson's Disease Subjects Using Inertial Sensors: A Supervised Learning Approach

Autor(es)

Perez-Ibarra, Juan C. ^{[1, 2, 3]} ; Siqueira, Adriano A. G. ^{[3, 4, 5]} ; Krebs, Hermano I. ^{[2, 6, 7, 8, 9, 10, 11]}

Afiliação do(s) autor(es):Mostrar menos -

^[1] Univ Sao Paulo, Dept Elect Engn, BR-13566590 Sao Carlos - Brazil

^[2] MIT, Dept Mech Engn, Cambridge, MA 02139 - USA

^[3] Univ Sao Paulo, Dept Mech Engn, BR-13566590 Sao Carlos - Brazil

^[4] Univ Sao Paulo, Ctr Adv Studies Rehabil, BR-13566590 Sao Carlos - Brazil

^[5] Univ Sao Paulo, Ctr Robot Sao Carlos, BR-13566590 Sao Carlos - Brazil

^[6] Univ Maryland, Sch Med, Dept Neurol, Baltimore, MD 21201 - USA

^[7] Fujita Hlth Univ, Sch Med, Dept Rehabil Med 1, Toyoake, Aichi 4701192 - Japan

^[8] Univ Newcastle, Inst Neurosci, Newcastle Upon Tyne NE2 4HH, Tyne & Wear - England

^[9] Osaka Univ, Dept Mech Sci & Bioengn, Osaka 5608531 - Japan

^[10] Sogang Univ, Coll Engn, Seoul 121742 - South Korea

^[11] Wolfson Sch Mech Elect & Mfg Engn, Loughborough LE11 3TU, Leics - England

• Tipo de documento: Artigo Científico
• Fonte: IEEE SENSORS JOURNAL; v. 20, n. 24, p. 14984-14993, DEC 15 2020.
• Citações Web of Science: 0

Resumo

Automatic detection of gait phases through supervised learning is a feasible approach that takes advantage of the consistency of the gait cycle among healthy subjects. However, gait patterns among subjects with impairments are not as consistent and most of the existing algorithms have limited performance detecting phases during impaired gait. In this paper, we proposed one algorithm that used linear classifiers to detect in real-time the transition between consecutive gait phases. Our approach is a generalization of the rule- and threshold-based algorithms for event detection. Linear classifiers are parametric models that require appropriate values in order to perform correct classification of the gait phases. We introduced a modified Support Vector Machine (SVM) to compute such sub-optimal combinations of those values, and a further optimization with a hybrid meta-heuristic approach that integrates a Genetic and a Simulated Annealing Algorithm. We tested our approach on data collected by a single-IMU foot-mounted wearable device during overground and treadmill walking for two groups: one with healthy and one with Parkinson's Disease subjects. The F-1-scores were 0.987 and 0.953 for the two groups, which were comparable with our previously developed threshold-based method, which obtained 0.988 and 0.974, respectively. Our proposed approach achieved similar performance as the threshold-based scheme, with the advantage of not relying on any prior knowledge of specific features for any particular inertial signal. (AU)

• Processo FAPESP: 19/05937-7 - Estratégias adaptativas híbridas para exoesqueletos de membros inferiores
• Beneficiário: Adriano Almeida Gonçalves Siqueira
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/50376-2 - Adaptive variable impedance applied to robotic rehabilitation of walking
• Beneficiário: Adriano Almeida Gonçalves Siqueira
• Modalidade de apoio: Auxílio à Pesquisa - Regular