Field-Based Gait Monitoring System for the Elderly

描述（由申请人提供）：老年人的健康管理可以通过方便和客观的评估他们的步态得到改善。虽然一些定量的步行测量，如所采取的步骤的可变性，已被证明可以预测福尔斯的风险，这些测量是很难获得实验室外。这将有助于在诊所甚至在日常生活的正常活动中获得准确和客观的测量。长期监测甚至可以用来跟踪康复计划的依从性，或者确定何时应该调整药物剂量，所有这些都不需要经常去诊所。不幸的是，目前用于长期监测的设备限于简单的数据，例如步数，并且没有方便的手段来准确地测量最能指示移动性、平衡和跌倒风险的步态变量。微型传感器在精度和功率经济性方面正在迅速提高，为基于现场的活动评估提供了巨大的潜力。简单的基于加速度计的设备已经小型化，无线系统可以提供粗略的跑步速度和距离估计，精确到足以供休闲使用，同时保持足够的不显眼，便于长时间佩戴。新的微芯片技术使高精度惯性传感器系统能够以类似的方式包装以供日常使用。步态测量的一个主要障碍是需要消除这些设备在估计步长和其他与移动性相关的量时出现的漂移误差。该项目旨在为此目的开发传感器融合算法，并将其与可穿戴惯性测量传感器集成。该项目的具体目标是：1。实施漂移减少算法和软件，用于整合惯性传感器数据，以准确估计步态参数。2.开发适合现场步态监测的无线传感器和接收器单元硬件，采用适合长期使用的微型封装。3.对年轻人和老年人进行准确性和可用性测试，以表征现实环境中的器械规格。 公共卫生相关性：跌倒和相关伤害极大地限制了老年人的活动能力，通过锻炼、康复和其他干预措施可以大大降低其风险。然而，目前很难在家中长时间监测流动性，以评估对干预措施的依从性或确定药物剂量。该提案旨在开发新技术，以实现对家中步态和移动性的准确，长期测量。

DESCRIPTION (provided by applicant): Health management of older adults can be improved through convenient and objective assessment of their gait. Although a number of quantitative measures of walking, such as the variability of steps taken, have been shown to predict the risk of falls, these measurements are difficult to obtain outside the laboratory. It would be helpful to obtain accurate and objective measures in the clinic or even in the home during normal activities of daily living. Long-term monitoring could even be used to track compliance to a rehabilitation program or to determine when the dosage of medicines should be adjusted, all without requiring frequent visits to the clinic. Unfortunately, current devices for long-term monitoring are limited to simplistic data such as step count, and there is no convenient means to accurately measure the gait variables most indicative of mobility, balance, and fall risk. Miniature sensors are rapidly improving in accuracy and power economy, offering great potential for field-based activity assessment. Simple accelerometer-based devices have been miniaturized, and wireless systems can provide rough estimates of running speed and distance accurate enough for casual use, while remaining unobtrusive enough to wear conveniently for long durations. New microchip technologies make it feasible for highly accurate, inertial sensor systems to be packaged similarly for daily use. A major barrier for gait measurements is the need to eliminate drift errors that occur with these devices when estimating stride length and other quantities related to mobility. This project seeks to develop sensor fusion algorithms for this purpose, and integrate them with wearable inertial measurement sensors. The Specific Aims of this project are to: 1. Implement drift reduction algorithms and software for integrating inertial sensor data to accurately estimate gait parameters. 2. Develop wireless sensor and receiver unit hardware for field-based gait monitoring, with miniature packages suitable for long-term use. 3. Perform accuracy and usability testing on younger and older adults, to characterize device specifications in realistic environments. PUBLIC HEALTH RELEVANCE: Falling and related injuries greatly limit mobility in older adults, and their risk can be reduced substantially with exercise, rehabilitation, and other interventions. It is currently difficult, however, to monitor mobility in the home and for long durations, either to assess compliance to an intervention or to determine dosage of medicine. This proposal seeks to develop new technology to enable accurate, long-term measurements of gait and mobility in the home.