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.

项目概要 与衰老相关的功能性活动能力丧失是危险跌倒和丧失生命的主要原因 大约 60% 的 80 岁以上的社区居民患有步态障碍，并且 异常的步态模式与住院和死亡风险增加两倍以上相关 通过对时空步态的分析，与没有步态障碍的年龄相关成年人进行比较。 可以测量、量化和监测健康和病理人群的参数、步态功能。 三维 (3D) 测力台和动作捕捉技术是当前分析的黄金标准， 但它们受到成本、实验室环境的限制以及无法测量大面积的限制。 可以由经过培训的人员进行身体表现的现场测试，以筛选功能活动能力和 步态障碍，但所得数据只能用于比较步态实验室评估。 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 为了解决这些问题，Axioforce 正在开发一种无创疗法。 可提供近实时自动步态洞察的可穿戴技术 Axioforce 的 3D 力传感鞋。 鞋垫 Axiostride 能够为老年人提供人工智能 (AI) 赋能的居家步态监测—— 这将是第一个通过鞋子测量 3D 地面反作用力的产品。 鞋垫可适合任何普通鞋子，适合长期日常使用。 一种简单的工具，可及早发现步态障碍和功能活动能力下降，以帮助预防进一步的功能障碍 衰退、跌倒和失去独立性。这项过渡快速通道拨款将支持开发和测试。 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 鞋垫内传感器的布置将根据标准 3D 测力台确定和验证， 以及自动数据收集和云上传过程的开发和测试。 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 用于对功能移动测试中高于和低于重要阈值的个体进行分类。 将进行为期一个月的试点研究，以确定人工智能驱动的 Axiosride 的能力 对功能移动性进行无监督分类并分析产品的可接受性和采用率。 Axioforce 旨在进一步改进其鞋垫原型并开发和测试随附的准确性 人工智能算法。