Electrotechnical Department, Polytechnic University of Leiria, 2411-901 Leiria, Portugal.
Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3004-011 Coimbra, Portugal.
Sensors (Basel). 2024 Feb 17;24(4):1301. doi: 10.3390/s24041301.
The functional reach test (FRT) is a clinical tool used to evaluate dynamic balance and fall risk in older adults and those with certain neurological diseases. It provides crucial information for developing rehabilitation programs to improve balance and reduce fall risk. This paper aims to describe a new tool to gather and analyze the data from inertial sensors to allow automation and increased reliability in the future by removing practitioner bias and facilitating the FRT procedure. A new tool for gathering and analyzing data from inertial sensors has been developed to remove practitioner bias and streamline the FRT procedure. The study involved 54 senior citizens using smartphones with sensors to execute FRT. The methods included using a mobile app to gather data, using sensor-fusion algorithms like the Madgwick algorithm to estimate orientation, and attempting to estimate location by twice integrating accelerometer data. However, accurate position estimation was difficult, highlighting the need for more research and development. The study highlights the benefits and drawbacks of automated balance assessment testing with mobile device sensors, highlighting the potential of technology to enhance conventional health evaluations.
功能性伸展测试(FRT)是一种用于评估老年人和某些神经疾病患者动态平衡和跌倒风险的临床工具。它为制定康复计划提供了关键信息,以改善平衡和降低跌倒风险。本文旨在描述一种新的工具,通过消除操作者偏差和简化 FRT 程序,利用惯性传感器收集和分析数据,以实现自动化并提高可靠性。已经开发了一种新的工具来收集和分析来自惯性传感器的数据,以消除操作者偏差并简化 FRT 程序。该研究涉及 54 名使用带传感器的智能手机执行 FRT 的老年人。方法包括使用移动应用程序来收集数据,使用传感器融合算法(如 Madgwick 算法)来估计方向,并尝试通过两次积分加速度计数据来估计位置。然而,准确的位置估计很困难,这凸显了需要进一步的研究和开发。该研究强调了使用移动设备传感器进行自动平衡评估测试的优缺点,突出了技术在增强传统健康评估方面的潜力。