Regulatory clearance of a rehabilitation system for individuals with upper limb loss

上肢丧失患者康复系统的监管许可

• 批准号: 10710335
• 负责人: Rahul Reddy Kaliki
• 金额: $ 123.73万
• 依托单位: INFINITE BIOMEDICAL TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710335
• 关键词: Amputation; Amputees; Businesses; Caring; Certification; Classification; Clinical; Clinical Research; Computer software; Contracts; Development; Devices; Documentation; Elbow; Electrodes; Electromagnetics; Elements; Hand; Individual; Learning; Limb structure; Lithium; Marketing; Measures; Methodology; Methods; Mission; Modeling; Motor; Muscle; Notification; Outcome; Outcome Measure; Patients; Pattern Recognition; Phase; Polymers; Process; Prosthesis; Radial; Rehabilitation therapy; Residual state; Safety; Signal Transduction; Surface; System; Technology; Testing; Time; Training; Transference; Upper Extremity; Validation; Work; Wrist; clinical care; design; efficacy evaluation; empowerment; experience; fabrication; flexibility; functional outcomes; functional restoration; improved; interest; limb loss; meetings; prospective; skills; verification and validation; virtual; visual feedback; wireless

ABSTRACT Our work to date has resulted in the development of several technologies focused on improving the lives of individuals with upper-limb loss (ULL). First, our Sense pattern recognition control system (and associated Element low-profile surface EMG electrodes and FlexCell flexible lithium-polymer batteries) can interface with most myoelectric hands, wrists, and elbows on the market. Second, our MyoTrain virtual-limb pre-prosthetic training system has been demonstrated to help individuals with ULL learn to use pattern recognition. This system is valuable in the “golden window” time period after amputation but before it is feasible to fit the patient with a definitive prosthesis. Based on our clinical experience, we identified three ways in which we could deepen our clinical impact, particularly as related to the MyoTrain system. First, we noted that EMG classification accuracy could be improved by decreasing intra-class scatter (improving repeatability) and/or increasing inter-class separation (improving separability). How individuals incorporate these methods is user specific and they could benefit from the incorporation of visual feedback – something that we call “Active Coaching”. Second, our business model allows us to provide MyoTrain to the patient before reimbursement is available, which offers significant clinical advantage. However, this currently still requires the prosthetist to fabricate a check socket for use with each individual. We now propose to integrate a reusable wireless armband which would eliminate this issue. Third, we plan to evaluate the efficacy of MyoTrain with a prospective trial of our technology suite and associated clinical methodology. Specifically, we will test three hypotheses: 1) use of MyoTrain results in skills transference to control of the final prosthesis; 2) the virtual outcome measures in MyoTrain are correlated with real-world functional outcome measures; and 3) use of MyoTrain results in improved clinical outcomes as measured by functional, subjective and usage metrics. The capstone of this project is the submission of a 510(k) premarket notification application for the MyoTrain system.

摘要