School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China.
School of Design, The Hong Kong Polytechnic University, Hong Kong, China.
Sensors (Basel). 2023 Sep 21;23(18):8008. doi: 10.3390/s23188008.
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
常规的步态和平衡评估已被公认为是一种通过发出预警和实施适当干预来预防跌倒的有效方法。然而，当前有限的公共医疗资源无法满足对步态和平衡恶化进行持续监测的需求。本研究的目的是开发和评估一种基于传感器技术和多源异构数据分析的原型替代系统，用于步态和平衡评估和监测。该系统旨在分析用户通过惯性传感器和深度相机收集的多模式数据流，在进行 3 米计时起立行走测试、五次坐站测试和 Romberg 测试时，预测理疗师对临床测量的评分。通过广义回归传感器数据，为步态和平衡估计建立预测模型。使用有序逻辑回归分析与用户接受行为相关的人口统计学相关性。在这项初步研究中，招募了 44 名老年人（38 名女性）（平均年龄=78.5 岁，标准差[SD]=6.2 岁）。参与者认为使用该系统进行步态和平衡监测是一个好主意（平均值=5.45，SD=0.76），并且易于使用（平均值=4.95，SD=1.09），该系统有助于改善他们的健康（平均值=5.32，SD=0.83），值得信赖（平均值=5.04，SD=0.88），并且任务与技术之间具有良好的适配性（平均值=4.97，SD=0.84）。总体而言，参与者对在步态和平衡管理中使用拟议系统表现出积极的使用意向（平均值=5.22，SD=1.10）。讨论了与用户接受度相关的人口统计学相关性。本研究提供了使用传感器技术增强系统管理社区居住老年人步态和平衡的可行性的初步证据。该干预措施被验证为可接受、可行和有价值的。
