Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

• DOI: 10.1017/wtc.2020.11
• Publication Date: 2021-03-25T08:24:30Z
• Authors: Philipp Arens; Christopher Siviy; Jaehyun Bae; Dabin K. Choe; Nikos Karavas; Teresa Baker; Terry D. Ellis; Louis N. Awad; Conor J. Walsh
• Keywords: STRIDE; Gait training; Powered exoskeleton; Motion Capture

Abstract Hemiparetic walking after stroke is typically slow, asymmetric, and inefficient, significantly impacting activities of daily living. Extensive research shows that functional, intensive, task-specific gait training instrumental for effective rehabilitation, characteristics our group aims to encourage with soft robotic exosuits. However, standard clinical assessments may lack the precision frequency detect subtle changes in intervention efficacy during both conventional exosuit-assisted training, potentially impeding targeted therapy regimes. In this paper, we use exosuit-integrated inertial sensors reconstruct three clinically meaningful metrics related circumduction, foot clearance, stride length. Our method corrects sensor drift using instantaneous information from sides body. This approach makes robust irregular conditions poststroke as well usable real-time applications, such movement monitoring, exosuit assistance control, biofeedback. We validate algorithm eight people comparison lab-based optical motion capture. Mean errors were below 0.2 cm (9.9%) −0.6 (−3.5%) 3.8 (3.6%) A single-participant case study technique’s promise daily-living environments by detecting exosuit-induced while a busy outdoor plaza.