Wearable-Sensor System for Monitoring Motor Function

用于监测运动功能的可穿戴传感器系统

• 批准号: 7482475
• 负责人: Carlo J De Luca
• 金额: $ 63.78万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-06 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7482475
• 关键词: Activities of Daily Living; Age; Algorithms; Artificial Intelligence; Body Surface; Bradykinesia; Cardiac; Classification; Clinical; Communities; Complex; Computer Systems Development; Computer software; Condition; Daily; Data; Data Collection; Development; Devices; Disease; Dyskinetic syndrome; Effectiveness; Essential Tremor; Exertion; Exhibits; Fingers; Foundations; Freezing; Future; Gilles de la Tourette syndrome; Holter Electrocardiography; Home environment; Huntington Disease; Hybrids; Inherited Spinocerebellar Degenerations; Interview; Investigation; Kinetics; Levodopa; Limb structure; Lower Extremity; Massachusetts; Measurement; Medical; Medication Management; Methods; Monitor; Motion; Motor; Motor Activity; Movement Disorders; Muscle; Nature; Neurologist; Neuromuscular Diseases; Nose; Numbers; Operative Surgical Procedures; Outcome Measure; Parkinson Disease; Patient Monitoring; Patient Self-Report; Patients; Pattern; Performance; Pharmaceutical Preparations; Phase; Phase II Clinical Trials; Population; Posture; Principal Investigator; Purpose; Questionnaires; Recording of previous events; Research Personnel; Sampling; Severities; Signal Transduction; Simulate; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Specialist; Staging; Standards of Weights and Measures; System; Tardive Dyskinesia; Technology; Testing; Text; Time; Tremor; Upper Extremity; Walking; Week; Wireless Technology; Work; base; clinically significant; computerized data processing; day; design; diaries; improved; kinematics; knowledge base; miniaturize; monitoring device; motor disorder; nervous system disorder; novel; programs; prototype; research study; response; sensor; statistics; success; technology development; tool

DESCRIPTION (provided by applicant): We propose to develop a wearable Personal Status Monitor for improving the medication management of Parkinson's Disease patients by monitoring the effects of the medication continuously during the day. New outcome measures are needed to supplement the self-reports currently in use that are ineffective in managing the complex and unpredictable nature of movement disorders in this population. The device we propose to develop can be worn unobtrusively by patients in their home to automatically provide the following clinically significant information: 1) the presence and severity of specific primary and secondary movement disorders associated with the disease, 2) the status of On-Off motor fluctuation in response to anti-Parkinson's medication, and 3) the mobility status of the patient. The patient will be monitored by specially designed electromyographic (EMG) and accelerometric (ACC) body-worn sensors. Their signals will be analyzed by a novel Artificial Intelligence knowledge-based signal processing method developed by our group specifically for this purpose. Success of the system will be based on classification accuracy compared to observation by experts. The proposal is composed of two projects. The first and dominant project will develop the underlying technological requirements for the PSM system's application to Parkinson's disease. The aims will include acquisition hardware development in the form of hybrid EMG/ACC sensors. However, the emphasis will be on the development of the knowledge-based algorithms and their software implementation. The second project is designed to acquire the knowledge base needed in Project 1 through data collection experiments from control subjects and patients with Parkinson's disease. The experiments are designed to advance the algorithm development in a hierarchical manner starting from highly standardized activities to free-form activities which approximate real-world conditions. The development of the system will be constructed so that future versions can be adapted to other movement disorders. The successful development of this technology will be transferred for commercial development with the financial assistance of the Massachusetts Community Technology Foundation.

描述（由申请人提供）：我们建议开发一种可穿戴个人状态监测器，通过在白天连续监测药物的效果来改善帕金森病患者的药物管理。需要新的结果测量来补充目前使用的自我报告，这些报告对于管理该人群运动障碍的复杂性和不可预测性是无效的。我们建议开发的设备可以让患者在家中不引人注目地佩戴，自动提供以下临床重要信息：1）与疾病相关的特定原发性和继发性运动障碍的存在和严重程度，2）开关状态抗帕金森病药物引起的运动波动，以及 3) 患者的活动状态。患者将通过专门设计的肌电图 (EMG) 和加速度 (ACC) 穿戴式传感器进行监测。他们的信号将通过我们团队专门为此目的开发的基于人工智能知识的信号处理方法进行分析。该系统的成功将取决于与专家观察相比的分类准确性。该提案由两个项目组成。第一个也是主导项目将开发 PSM 系统应用于帕金森病的基本技术要求。目标包括开发混合 EMG/ACC 传感器形式的采集硬件。然而，重点将放在基于知识的算法及其软件实现的开发上。第二个项目旨在通过对照受试者和帕金森病患者的数据收集实验来获取项目 1 所需的知识库。这些实验旨在以分层方式推进算法开发，从高度标准化的活动到接近真实世界条件的自由形式活动。该系统的开发将使得未来的版本能够适应其他运动障碍。该技术的成功开发将在马萨诸塞州社区技术基金会的财政援助下转用于商业开发。