3D force sensing insoles for wearable, AI empowered, high-fidelity gait monitoring

3D 力传感鞋垫，用于可穿戴、人工智能支持的高保真步态监控

• 批准号: 10688715
• 负责人: Jonathan Daniel Miller
• 金额: $ 25.03万
• 依托单位: AXIOFORCE INC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-23 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10688715
• 关键词: 3-Dimensional; Activities of Daily Living; Address; Adoption; Adult; Aging; Algorithms; Alzheimer' s disease diagnosis; American; Area; Artificial Intelligence; Automation; Cessation of life; Classification; Communication; Communities; Computer software; Custom; Dangerousness; Data; Data Analyses; Data Collection; Detection; Development; Devices; Early Diagnosis; Early Intervention; Elements; Equipment; Event; Gait; Gait abnormality; Generations; Goals; Grant; Health Personnel; Home; Hour; Human Resources; Impairment; Individual; Institutionalization; Knowledge; Laboratories; Life; Marketing; Measures; Medical Care Costs; Medical Device; Methods; Modeling; Monitor; Motion; Nursing Homes; Pathologic; Pathology; Patients; Pattern; Performance; Phase; Physical Performance; Pilot Projects; Population; Process; Protocols documentation; Reach, Effectiveness, Adoption, Implementation, and Maintenance; Reaction; Research Personnel; Risk; Running; Sampling; Shoes; Stroke; System; Technology; Testing; Time; Training; Work; age related; aged; artificial intelligence algorithm; battery life; classification algorithm; clinical care; cloud platform; commercial application; cost; data handling; data quality; design; digital treatment; empowerment; falls; field study; foot; functional decline; functional loss; gait examination; health empowerment; human old age (65+); improved; insight; mortality; next generation; portability; power consumption; prevent; prototype; screening; sensor; software systems; technological innovation; tool; wearable device; wearable monitor

PROJECT SUMMARY Loss of functional mobility associated with aging is the leading cause of dangerous falls and loss of living independence. Approximately 60% of community-residing individuals >80 years-old have a gait disorder, and abnormal gait patterns are associated with a greater than two-fold increased risk of institutionalization and death in comparison to age-related adults without gait impairments. Through analysis of temporospatial gait parameters of healthy and pathologic populations, gait function can be measured, quantified, and monitored. Three-dimensional (3D) force plates and motion capture technologies are the current gold standard for analysis, but they are limited by their cost, confinement to laboratory settings, and inability to measure large areas. In-the- field tests of physical performance can be conducted by trained personnel to screen for functional mobility and gait impairments, but the resulting data can only be used in comparison gait lab assessments. Other technologies on the market lack data fidelity and require complicated data analysis, which makes them unacceptable to healthcare providers and patients alike. To solve these problems, Axioforce is developing a noninvasive wearable technology that provides near-real time automated gait insights. Axioforce's 3D-force sensing shoe insole, Axiostride, enables artificial intelligence (AI) empowered at-home gait monitoring for aging individuals at- risk of functional mobility decline. This will be the first product to measure 3D ground reaction forces via a shoe insole that can fit within any normal shoe, making it suitable for long term daily use. It will empower clinicians as an easy tool for early detection of gait disorders and declining functional mobility to help prevent further functional decline, falls, and loss of independence. This transition Fast-Track grant will support the development and testing of the sensing insole prototype and accompanying software. In Phase I, the prototype's circuitry will be custom designed to maximize sampling rate and battery life for continuous at-home use, and the most effective arrangement of the sensors within the insole will be determined and validated against a standard 3D force plate, as well as development and testing of an automated data collection and cloud uploading process. In Phase II, an AI algorithm, trained on collected insole data from normal and pathologic gait cycles in aged individuals, will be used to classify individuals above and below important thresholds in functional mobility tests. Secondly, a one-month pilot study will be performed to determine capabilities of the AI empowered Axiostride for unsupervised classification of functional mobility and analyze the product’s acceptability and adoption. Thus, Axioforce aims to further improve its insole prototype and develop and test the accuracy of the accompanying AI algorithm.

项目摘要 与衰老相关的功能流动性丧失是危险跌倒和失去生活的主要原因 独立。大约60％的社区保留个人> 80岁患有战斗障碍，并且 异常步态模式与制度化和死亡的风险增加了两倍以上 与没有步态障碍的与年龄相关的成年人相比。通过分析临时步态 可以测量，量化和监测健康和病理种群，步态功能的参数。 三维（3D）力板和运动捕获技术是当前用于分析的金标准， 但是它们受到成本，对实验室环境的关注以及无法衡量大面积的限制。内 训练有素的人员可以进行身体表现的现场测试，以筛选功能活动性和 步态障碍，但所得数据只能用于比较步态实验室评估。其他技术 在市场上缺乏数据保真度，需要复杂的数据分析，这使得它们无法接受 医疗保健提供者和患者。为了解决这些问题，Axioforce正在发展无创 可穿戴技术可提供近乎真实的自动化收集见解。 Axioforce的3D-Force传感器鞋 鞋垫，AxioStride，使人工智能（AI）授权在家收集对老龄化个人的收集监控 - 功能流动性下降的风险。这将是第一个通过鞋子测量3D地面反作用力的产品 可以适合任何普通鞋子的鞋垫，使其适合长期日常使用。它将授权临床医生作为 一种简单的工具，用于早期检测焦油疾病和功能动机下降，以防止进一步的功能 下降，跌倒和独立丧失。这种过渡快速拨款将支持开发和测试 感应鞋垫原型和参与软件的。在第一阶段，原型电路将是自定义的 旨在最大化采样率和电池寿命，以持续使用，最有效 将确定并针对标准的3D力板确定传感器的布置，并验证 以及自动数据收集和云上传过程的开发和测试。在第二阶段， AI算法接受了正常和病理遇到年龄个体周期的收集的鞋垫数据的AI算法，将 用于在功能移动测试中对个人进行分类。其次，a 将进行一个月的试点研究，以确定AI授权AxioStride的功能 无监督的功能流动性分类和分析产品的可接受性和采用。那， Axioforce旨在进一步改善其鞋底原型，并发展和测试累积的准确性 AI算法。