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

基本信息

  • 批准号:
    9046217
  • 负责人:
  • 金额:
    $ 57.49万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

 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.
 描述(由申请方提供):本II期的目标是通过开发一种基于传感器的自动化方法来跟踪日常生活中无脚本活动期间广泛运动障碍的存在和严重程度,从而提高高级大脑和行为研究工具的可用性[PA-14-250]。持续更新和解释的信息从身体佩戴传感器将提供准确,客观,高分辨率(1秒)。帕金森病中震颤、运动障碍、运动迟缓、冻结和步态障碍的运动症状严重程度的测量以及特发性震颤中姿势性/运动性震颤的测量。它将使研究人员能够评估运动障碍的复杂性和动态性,这是目前自我报告和纸和纸工具的标准所无法捕捉的。可穿戴传感器技术的进步已经促进了这样的解决方案,但是目前没有运动障碍识别设备能够针对美国超过4500万患有运动障碍的人以有效的方式解释来自非脚本活动的传感器数据。我们的方法是独特的,因为我们正在开发一个通用的应用程序生成器(AG)软件平台,其中包含信号处理模块,可以很容易地配置为提供自动识别不同的疾病,而不需要从头开始为每个准备单独的算法。第一阶段通过开发一个基本的AG平台建立了概念验证,该平台通过新型混合传感器实现了帕金森病(PD)患者震颤,运动障碍和步态冻结的自动识别,该传感器通过表面肌电图(sEMG)和加速度计记录提供肌肉活动和运动数据。第二阶段将继续发展,包括更广泛的PD运动障碍,以及其他神经系统疾病。Aim 1将通过结合sEMG和惯性测量单元(IMU)传感器创建一个增强的AG平台,以更完整地描述非自主运动,并在跟踪其他疾病时降低风险。人类受试者测试将提供传感器数据库,用于测试IMU传感器准确性并最大限度地减少软组织伪影。第一阶段的识别算法将使用增强的平台进行更新。Aim 2将使用增强的平台开发新的识别应用程序,跟踪PD中的运动迟缓和步态障碍,以及原发性震颤患者的姿势和动力震颤。我们的目标是在不受约束的监测条件下,使用用户独立的算法实现错误率< 5%。Aim 3将提供一种便携式预商用设备,配备必要的硬件、软件、用户界面和报告生成器,以有效监测PD、特发性震颤和坐/站/步行活动。该系统将使用平板电脑收集和处理sEMG/IMU数据,以增强可用性。运动障碍专家和潜在的最终用户将指导II期开发,并协助我们制定其他神经系统疾病的未来商业化计划,如脑瘫,肌张力障碍,ALS和不宁腿综合征。它也将成为患者可操作的基础。 临床使用的器械。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Gianluca De Luca其他文献

Gianluca De Luca的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Gianluca De Luca', 18)}}的其他基金

SpeechSense: An Interactive Sensor Platform for Speech Therapy
SpeechSense:用于言语治疗的交互式传感器平台
  • 批准号:
    10256832
  • 财政年份:
    2022
  • 资助金额:
    $ 57.49万
  • 项目类别:
Adaptive & Individualized AAC
自适应
  • 批准号:
    10600065
  • 财政年份:
    2019
  • 资助金额:
    $ 57.49万
  • 项目类别:
EMG Voice Restoration
肌电图语音恢复
  • 批准号:
    10009728
  • 财政年份:
    2018
  • 资助金额:
    $ 57.49万
  • 项目类别:
EMG Voice Restoration
肌电图语音恢复
  • 批准号:
    10376786
  • 财政年份:
    2018
  • 资助金额:
    $ 57.49万
  • 项目类别:
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
  • 财政年份:
    2015
  • 资助金额:
    $ 57.49万
  • 项目类别:
Subvocal Speech for Augmentative and Alternative Communication
用于增强性和替代性交流的默声语音
  • 批准号:
    9130174
  • 财政年份:
    2015
  • 资助金额:
    $ 57.49万
  • 项目类别:
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:
    8734495
  • 财政年份:
    2013
  • 资助金额:
    $ 57.49万
  • 项目类别:
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:
    8521782
  • 财政年份:
    2013
  • 资助金额:
    $ 57.49万
  • 项目类别:
A Wireless-Sensor System for Reliable Recordings during Vigorous Muscle Activitie
无线传感器系统可在剧烈肌肉活动期间进行可靠记录
  • 批准号:
    8392830
  • 财政年份:
    2012
  • 资助金额:
    $ 57.49万
  • 项目类别:
A Wireless Sensor System for Reliable Recordings During Exercise
用于运动期间可靠记录的无线传感器系统
  • 批准号:
    8978255
  • 财政年份:
    2012
  • 资助金额:
    $ 57.49万
  • 项目类别:

相似海外基金

RII Track-4:NSF: From the Ground Up to the Air Above Coastal Dunes: How Groundwater and Evaporation Affect the Mechanism of Wind Erosion
RII Track-4:NSF:从地面到沿海沙丘上方的空气:地下水和蒸发如何影响风蚀机制
  • 批准号:
    2327346
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Standard Grant
BRC-BIO: Establishing Astrangia poculata as a study system to understand how multi-partner symbiotic interactions affect pathogen response in cnidarians
BRC-BIO:建立 Astrangia poculata 作为研究系统,以了解多伙伴共生相互作用如何影响刺胞动物的病原体反应
  • 批准号:
    2312555
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Standard Grant
How Does Particle Material Properties Insoluble and Partially Soluble Affect Sensory Perception Of Fat based Products
不溶性和部分可溶的颗粒材料特性如何影响脂肪基产品的感官知觉
  • 批准号:
    BB/Z514391/1
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Training Grant
Graduating in Austerity: Do Welfare Cuts Affect the Career Path of University Students?
紧缩毕业:福利削减会影响大学生的职业道路吗?
  • 批准号:
    ES/Z502595/1
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Fellowship
Insecure lives and the policy disconnect: How multiple insecurities affect Levelling Up and what joined-up policy can do to help
不安全的生活和政策脱节:多种不安全因素如何影响升级以及联合政策可以提供哪些帮助
  • 批准号:
    ES/Z000149/1
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Research Grant
感性個人差指標 Affect-X の構築とビスポークAIサービスの基盤確立
建立个人敏感度指数 Affect-X 并为定制人工智能服务奠定基础
  • 批准号:
    23K24936
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
How does metal binding affect the function of proteins targeted by a devastating pathogen of cereal crops?
金属结合如何影响谷类作物毁灭性病原体靶向的蛋白质的功能?
  • 批准号:
    2901648
  • 财政年份:
    2024
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Studentship
ERI: Developing a Trust-supporting Design Framework with Affect for Human-AI Collaboration
ERI:开发一个支持信任的设计框架,影响人类与人工智能的协作
  • 批准号:
    2301846
  • 财政年份:
    2023
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Standard Grant
Investigating how double-negative T cells affect anti-leukemic and GvHD-inducing activities of conventional T cells
研究双阴性 T 细胞如何影响传统 T 细胞的抗白血病和 GvHD 诱导活性
  • 批准号:
    488039
  • 财政年份:
    2023
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Operating Grants
How motor impairments due to neurodegenerative diseases affect masticatory movements
神经退行性疾病引起的运动障碍如何影响咀嚼运动
  • 批准号:
    23K16076
  • 财政年份:
    2023
  • 资助金额:
    $ 57.49万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了