A Software Platform for Sensor-based Movement Disorder Recognition

基于传感器的运动障碍识别软件平台

• 批准号: 9321913
• 负责人: Gianluca De Luca
• 金额: $ 56.65万
• 依托单位: ALTEC, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9321913
• 关键词: Accelerometer; Activities of Daily Living; Affect; Algorithms; American; Bradykinesia; Calibration; Cerebral Palsy; Clinical; Complement; Complex; Computer software; Data; Databases; Development; Devices; Disease; Dyskinetic syndrome; Dystonia; Effectiveness; Enhancement Technology; Essential Tremor; Freezing; Future; Gait; Gait abnormality; Generic Drugs; Goals; Guidelines; Health Care Sector; Home environment; Hybrids; Individual; Involuntary Movements; Kinetics; Laboratories; Lead; Left; Limb structure; Measurement; Miniaturization; Modality; Monitor; Morphologic artifacts; Motor; Movement; Movement Disorders; Muscle; Nature; Neurologic; Paper; Parkinson Disease; Patient Self-Report; Patients; Phase; Population; Posture; Process; Productivity; Reporting; Research; Research Personnel; Resolution; Restless Legs Syndrome; Risk; Rotation; Scientist; Severities; Surface; System; Tablets; Techniques; Technology; Testing; Time; Training; Tremor; Update; Walking; Wireless Technology; base; brain behavior; commercialization; data acquisition; data management; design; handheld mobile device; human subject; improved; instrument; motor symptom; nervous system disorder; new technology; novel; patient monitoring device; portability; prospective; protocol development; prototype; public health relevance; sensor; signal processing; soft tissue; systems research; tool; usability

 DESCRIPTION (provided by applicant): The goal of this Phase II is to enhance the availability of advanced brain and behavior research tools [PA-14-250] by developing an automated sensor-based means of tracking the presence and severity of a broad spectrum of movement disorders during unscripted activities of daily living. The continuously updated and interpreted information from body-worn sensors will provide accurate, objective, and high resolution (1 s.) measurement of motor symptom severity of tremor, dyskinesia, bradykinesia, freezing and gait disorders in Parkinson's disease and postural/kinetic tremor in essential tremor. It will allow researchers to assess the oftentimes complex and dynamic nature of movement disorders, which is poorly captured by the current standard of self-reports and pencil-and-paper instruments. Advances in wearable sensor technology have facilitated such a solution, but there are currently no movement disorder recognition devices capable of interpreting sensor data from non- scripted activity in an effective manner for the more than 45 million people in the U.S. with movement disorders. Our approach is unique in that we are developing a generic Application Generator (AG) software platform containing signal processing modules that can be readily configured to provide automated recognition for different disorders without the need to prepare separate algorithms from scratch for each. Phase I established a proof of concept by developing a rudimentary AG platform that achieved automatic recognition of tremor, dyskinesia and freezing-of-gait in patients with Parkinson's disease (PD) from novel hybrid sensors that provided both muscle activity and movement data through surface electromyographic (sEMG) and accelerometer recordings. Phase II will continue the development to include a broader range of PD movement disorders, as well as other neurological conditions. Aim 1 will create an enhanced AG Platform by incorporating combined sEMG and inertial measurement unit (IMU) sensors to more completely describe involuntary movements and reduce the risk when tracking additional disorders. Human subject testing will provide a sensor database for testing IMU sensor accuracy and minimizing soft tissue artifacts. The Phase I recognition algorithms will be updated using the enhanced platform. Aim 2 will use the enhanced platform to develop new recognition applications that track bradykinesia and gait disorders in PD, and postural and kinetic tremors in patients with essential tremor. Our goal is to achieve error rates < 5% during unconstrained monitoring conditions with user- independent algorithms. Aim 3 will deliver a portable pre-commercial device with the requisite hardware, software, user interface, and report generator to effectively monitor PD, essential tremor, and sitting/standing/walking activity. The system will collect and process sEMG/IMU data using a tablet PC to enhance usability. Movement disorder experts and prospective end-users will guide the Phase II development and assist us with future commercialization plans for other neurological conditions such as cerebral palsy, dystonia, ALS, and restless leg syndrome. It will also form the basis for a patient-operable device for clinical use.