tailieunhanh - Báo cáo hóa học: " Feasibility of the adaptive and automatic presentation of tasks (ADAPT) system for rehabilitation of upper extremity function poststroke"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Feasibility of the adaptive and automatic presentation of tasks (ADAPT) system for rehabilitation of upper extremity function poststroke | Choi et al. Journal of NeuroEngineering and Rehabilitation 2011 8 42 http content 8 1 42 Iril JOURNAL OF NEUROENGINEERING NCR AND REHABILITATION RESEARCH Open Access Feasibility of the adaptive and automatic presentation of tasks ADAPT system for rehabilitation of upper extremity function poststroke Younggeun Choi1 2 James Gordon1 Hyeshin Park1 and Nicolas Schweighofer1 Abstract Background Current guidelines for rehabilitation of arm and hand function after stroke recommend that motor training focus on realistic tasks that require reaching and manipulation and engage the patient intensively actively and adaptively. Here we investigated the feasibility of a novel robotic task-practice system ADAPT designed in accordance with such guidelines. At each trial ADAPT selects a functional task according to a training schedule and with difficulty based on previous performance. Once the task is selected the robot picks up and presents the corresponding tool simulates the dynamics of the tasks and the patient interacts with the tool to perform the task. Methods Five participants with chronic stroke with mild to moderate impairments 9 months post-stroke Fugl-Meyer arm score practiced four functional tasks selected out of six in a pre-test with ADAPT for about one and half hour and 144 trials in a pseudo-random schedule of 3-trial blocks per task. Results No adverse events occurred and ADAPT successfully presented the six functional tasks without human intervention for a total of 900 trials. Qualitative analysis of trajectories showed that ADAPT simulated the desired task dynamics adequately and participants reported good although not excellent task fidelity. During training the adaptive difficulty algorithm progressively increased task difficulty leading towards an optimal challenge point based on performance difficulty was then continuously adjusted to keep performance around the challenge point. Furthermore the time to complete all trained .