Field-Based Gait Monitoring System for the Elderly

描述(由申请人提供)：通过方便和客观地评估老年人的步态，可以改善老年人的健康管理。尽管一些步行的定量测量，如步数的可变性，已经被证明可以预测跌倒的风险，但这些测量在实验室外很难获得。这将有助于在临床甚至在日常生活活动中获得准确和客观的措施。长期监测甚至可以用来跟踪康复计划的遵守情况，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仅限于简单的数据，如步数，并且没有方便的方法来准确地测量最能指示机动性、平衡性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，为现场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，无线系统可以提供对运行速度和距离的粗略估计，足够准确，供休闲使用，同时保持足够的低调，便于长时间佩戴。新的微芯片技术使高精度的惯性传感器系统能够以类似的方式封装在日常使用中。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，并将其与可穿戴式惯性测量传感器相结合。本项目的具体目标是：1.实现惯性传感器数据融合的漂移减小算法和软件，以准确估计步态参数。2.研制适合野战步态监测的无线传感器和接收单元硬件，并配有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设备规格。 公共卫生相关性：跌倒和相关伤害极大地限制了老年人的行动能力，通过锻炼、康复和其他干预措施可以大幅降低他们的风险。然而，目前很难监测家庭中的活动情况并持续很长时间，无论是评估干预的依从性还是确定药物剂量。这项提议旨在开发新技术，以便能够准确、长期地测量家庭中的步态和机动性。

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.