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: 7922046
• 负责人: Carlo J De Luca
• 金额: $ 65.5万
• 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 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7922046
• 关键词: Activities of Daily Living; Age; Algorithms; Artificial Intelligence; Body Surface; Bradykinesia; Cardiac; Classification; Clinical; Communities; Complex; Computer Systems Development; Computer software; 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; 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; Questionnaires; Recording of previous events; Research Personnel; Sampling; Severities; Signal Transduction; Simulate; Small Business Innovation Research Grant; Specialist; Staging; System; Tardive Dyskinesia; Technology; Testing; Text; Time; Tremor; Upper Extremity; Walking; Wireless Technology; Work; base; clinically significant; computerized data processing; 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）：我们建议开发可穿戴的个人状况监测仪，以通过在白天连续监测药物的影响来改善帕金森氏病患者的药物管理。需要采取新的结果措施来补充目前使用的自我报告，这些报告在管理该人群中运动障碍的复杂和不可预测的本质方面无效。我们建议开发的设备可以由患者在家中无效地佩戴，以自动提供以下临床上重要的信息：1）与该疾病相关的特定原发性和继发性运动障碍的存在和严重性，2）响应抗Parkinson疾病的开关运动波动的状态，以及3）患者的流动性状态。该患者将通过特殊设计的肌电图（EMG）和加速度计量学（ACC）染色的传感器来监测。它们的信号将通过我们小组专门为此目的开发的新型人工智能知识信号处理方法来分析它们的信号。与专家的观察相比，该系统的成功将基于分类精度。该提案由两个项目组成。第一个和主导的项目将对PSM系统应用于帕金森氏病的潜在技术要求。目的将包括以混合EMG/ACC传感器形式的采集硬件开发。但是，重点将是基于知识的算法及其软件实施的开发。第二个项目旨在通过控制受试者和帕金森氏病患者的数据收集实验获得项目1中所需的知识库。该实验旨在以分层的方式从高度标准化的活动到近似现实世界中的自由形式活动开始，以层次的方式推进算法开发。将构建系统的开发，以便将来版本可以适应其他运动障碍。在马萨诸塞州社区技术基金会的财政援助下，将成功开发该技术以进行商业开发。

项目成果

What is IPUS and how does it help resolve biosignal complexity?

什么是 IPUS？它如何帮助解决生物信号的复杂性？

• DOI: 10.1109/iembs.2011.6091199
• 发表时间: 2011
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Nawab,SHamid;Cole,BryanT
• 通讯作者: Cole,BryanT

Resolving signal complexities for ambulatory monitoring of motor function in Parkinson's disease.

解决帕金森病运动功能动态监测的信号复杂性。

• DOI: 10.1109/iembs.2011.6091197
• 发表时间: 2011
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Roy,SergeH;Cole,BryanT;Gilmore,LDonald;DeLuca,CarloJ;Nawab,SHamid
• 通讯作者: Nawab,SHamid