Machine learning based human gait segmentation with wearable sensor platform
Deutscher übersetzter Titel: | Auf maschinellem Lernen basierende Segmentierung des menschlichen Gangs mit einer tragbaren Sensorplattform |
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Autor: | Potluri, Sasanka; Ravuri, Srinivas; Diedrich, Christian; Schega, Lutz |
Erschienen in: | Biomedical engineering ranging from wellness to intensive care : 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) : 41st EMB Conference ; July 23-27, Berlin |
Veröffentlicht: | Piscataway (N.J.): Institute of Electrical and Electronics Engineers (Verlag), 2019, S. 588-594, Lit. |
Beteiligte Körperschaft: | IEEE Engineering in Medicine and Biology Society. Annual International Conference (41. : 2019 : Berlin) |
Format: | Literatur (SPOLIT) |
Publikationstyp: | Sammelwerksbeitrag |
Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
Sprache: | Englisch |
DOI: | 10.1109/EMBC.2019.8857509 |
Schlagworte: | |
Online Zugang: | |
Erfassungsnummer: | PU202010008247 |
Quelle: | BISp |
Abstract des Autors
Supervised and unsupervised machine learning algorithms were explored for gait segmentation using wearable sensor platform. Multiple wearable sensors modules were placed at key locations: Four Inertial Measurement Units (IMUs) were attached to the thigh and shank of each leg and a plantar pressure measuring foot insoles were implanted in the shoes. The gait data has been collected from 10 people wirelessly via TCI-IP protocol, which is later anonymized. Further, the Ranchos Los Amigos (RLA) gait nomenclature-based data preprocessing and peak/valley detector based annotation steps are performed on the acquired data followed by implementation of machine learning techniques on the labeled datasets. The methods explored for phase and sub-phase classification includes the Unsupervised methods such as K-Means clustering and supervised methods like the Support Vector Machine (SVM) and Artificial Neural Network (ANN).