https://scholars.lib.ntu.edu.tw/handle/123456789/625398
Title: | Gait Training for Hemiplegic Stroke Patients: Employing an Automatic Neural Development Treatment Trainer with Real Time Detection | Authors: | FU-CHENG WANG Chen S.-F Li Y.-C Shih C.-J Lin A.-C Lin T.-T. |
Keywords: | Deep learning; Gait; Inertial measurement unit; NDT; Neural network; Rehabilitation; Stroke; Trainer | Issue Date: | 2022 | Journal Volume: | 12 | Journal Issue: | 5 | Source: | Applied Sciences (Switzerland) | Abstract: | This paper presents a clinical rehabilitation protocol for stroke patients using a movable trainer, which can automatically execute a neurodevelopmental treatment (NDT) intervention based on key gait events. The trainer consists of gait detection and motor control systems. The gait detection system applied recurrent neural networks (RNNs) to recognize important gait events in real time to trigger the motor control system to repeat the NDT intervention. This paper proposes a modified intervention method that simultaneously improves the user’s gait symmetry and pelvic rotation. We recruited ten healthy subjects and had them wear a rehabilitation gaiter on one knee joint to mimic stroke gaits for verification of the effectiveness of the trainer. We used the RNN model and a modified intervention method to increase the trainer’s effectiveness in improving gait symmetry and pelvic rotation. We then invited ten stroke patients to participate in the experiments, and we found improvement in gait symmetry in 80% and 90% of the patients during and after the training, respectively. Similarly, pelvic rotation improved in 80% of the patients during and after the training. These findings confirmed that the movable NDT trainer could improve gait performance for the rehabilitation of stroke patients. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126268702&doi=10.3390%2fapp12052719&partnerID=40&md5=0aab017e53c86fc6083c7c7e6dc63e2c https://scholars.lib.ntu.edu.tw/handle/123456789/625398 |
ISSN: | 20763417 | DOI: | 10.3390/app12052719 |
Appears in Collections: | 機械工程學系 |
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