Validation and application of wearable sensors for capturing kinematic responses to real-world losses of balance among balance-impaired older adults

这个应用程序的广泛目标是建立一种测量运动学(即 身体运动)在日常生活中对现实世界中失去平衡(LOB)的反应。 跌倒是老年人受伤和与伤害相关的死亡的主要原因，其中绊倒 据估计，滑行造成了60%的跌倒。众多实验室 研究表明，这些下降通常是与年龄相关的平衡能力下降的结果。 对这些LOB的恢复响应。不幸的是，技术挑战限制了我们的能力 评估实验室外的LOB响应，因此允许持续断开连接 在实验室对绊倒和滑倒的研究和在现实世界中真正的绊倒和滑倒之间。我们最近 开发了一种新技术来捕获真实LOB及其上下文的运动学 使用可穿戴传感器和录音机。此应用程序的目标1将调查 通过询问平衡障碍社区住宅来探讨这项技术推广使用的可行性 老年人在三周内每天清醒时佩戴该系统。目标2将 通过在AIM 1之间引发绊倒和滑倒来对这项技术进行实验室验证 参与者使用可穿戴传感器同时测量LOB响应运动学 系统和黄金标准的光电动作捕捉系统。AIM 3将使用可穿戴设备 来自目标1和目标2的传感器数据，以开始探索现实世界和 实验室LOB响应。能够捕获LOB响应的详细运动学，并 他们在现实世界中的背景将对秋季产生深远和根本性的影响 预防工作。首先，它将澄清实验室和现实世界之间的任何区别 LOBS，以增强实验室研究在现实世界中的普适性。其次，它会 克服在评估频率时使用记忆召回的众所周知的限制 真实世界瀑布的特征。第三，它将大大提高对Fall的批判性评价 预防干预及其机制，以最大限度地提高培训效益。第四，它会 协助研究人员开发自动检测真实世界中的LOB的算法 使用可穿戴传感器的个人。

The broad objective of this application is to establish a methodology to measure kinematic (i.e. bodily movement) responses to losses of balance (LOBs) in the real-world during daily life. Falls are the leading cause of injuries and injury-related deaths among older adults, with tripping and slipping being responsible for an estimated 60% of these falls. Numerous laboratory studies have shown that these falls generally result from an age-related decline in balance recovery responses to these LOBs. Unfortunately, technical challenges have limited our ability to assess LOB responses outside the lab, and therefore has allowed a disconnect to persist between lab studies of trips and slips and actual trips and slips in the real-world. We recently developed a novel technique to capture the kinematics of real-world LOBs and their context using wearable sensors and voice recorders. Aim 1 of this application will investigate the feasibility of this technique for extended use by asking balance-impaired community-dwelling older adults to wear the system daily during their waking hours for three weeks. Aim 2 will involve a laboratory validation of this technique by inducing trips and slips among Aim 1 participants while measuring LOB response kinematics simultaneously with the wearable sensor system and a gold-standard optoelectronic motion capture system. Aim 3 will use wearable sensor data from Aims 1 and 2 to begin to explore differences between real-world and laboratory LOB responses. The ability to capture detailed kinematics of LOB responses and their context in the real-world would have a profound and fundamental impact on fall prevention efforts. First, it would clarify any differences between laboratory and real-world LOBs to enhance the generalizability of lab studies to the real-world. Second, it would overcome well-known limitations of using memory recall when evaluating the frequency and characteristics of real-world falls. Third, it would greatly enhance the critical evaluation of fall prevention interventions and their mechanisms to maximize training benefits. Fourth, it would assist researchers developing algorithms to automatically detect real-world LOBs among individuals using wearable sensors.