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.

项目总结 与衰老相关的功能活动能力丧失是导致危险跌倒和生命丧失的主要原因 独立。大约60%居住在社区的80岁老人有步态障碍，并且 步态异常与住院和死亡风险增加两倍以上有关。 与没有步态障碍的与年龄相关的成年人相比。通过对时空步态的分析 健康和病理人群的参数、步态功能可以被测量、量化和监测。 三维(3D)测力板和运动捕捉技术是当前分析的黄金标准， 但它们受到成本、受限于实验室环境以及无法测量大范围面积的限制。在-在- 现场体能测试可由训练有素的人员进行，以筛选功能移动性和 步态损伤，但产生的数据只能用于比较步态实验室评估。其他技术 市场上缺乏数据保真度，需要复杂的数据分析，这使得他们无法接受 医疗保健提供者和患者都是如此。为了解决这些问题，Axioforce正在开发一种非侵入性 提供近乎实时的自动步态洞察的可穿戴技术。Axioforce的三维力传感鞋 Axiostride鞋垫使人工智能(AI)能够在家中监控老年人的步态- 功能流动性风险下降。这将是第一个通过鞋测量3D地面反作用力的产品 鞋垫，可以放在任何普通的鞋子里，适合长期日常使用。它将使临床医生能够 一种早期检测步态障碍和功能活动度下降的简单工具，有助于防止进一步的功能性 衰落、堕落和丧失独立性。这项过渡快速通道拨款将支持开发和测试 感应型鞋垫原型和配套软件。在第一阶段，原型的电路将是定制的 旨在最大限度地提高采样率和电池寿命，以便在家中连续使用，并且是最有效的 将对照标准3D测力板确定和验证鞋垫内传感器的布置， 以及开发和测试自动数据收集和云端上传过程。在第二阶段， 一种人工智能算法，根据从老年人正常和病理步态周期收集的鞋垫数据进行训练，将 用于在功能活动能力测试中对高于和低于重要阈值的个体进行分类。其次，a 将进行为期一个月的试点研究，以确定AI授权的AxioStride的能力 对功能移动性进行非监督分类，并分析产品的可接受性和采用率。因此， Axioforce的目标是进一步改进其鞋垫原型，并开发和测试随附的 人工智能算法。