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

可穿戴传感器的验证和应用，用于捕获现实世界中平衡受损老年人失去平衡的运动学反应

• 批准号: 10526627
• 负责人: Neil Alexander
• 金额: $ 20.36万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10526627
• 关键词: Adherence; Age; Agreement; Algorithms; Awareness; Characteristics; Communities; Data; Development; Devices; Effectiveness; Elderly; Equilibrium; Etiology; Evaluation; Event; Exercise; Exhibits; Exposure to; Fall prevention; Frequencies; Gait; Gold; Hour; Impairment; Individual; Injury; Investigation; Knowledge; Laboratories; Laboratory Study; Life; Measurable; Measurement; Measures; Memory; Methodology; Methods; Motion; Movement; Nature; Participant; Patient Self-Report; Prevention Research; Recovery; Research Personnel; Speed; System; Techniques; Time; Touchdown; Training; Validation; Video Recording; Voice; Walking; Work; age related; balance recovery; base; diaries; fall risk; falls; feasibility testing; foot; hazard; improved; injury-related death; innovation; kinematics; memory recall; novel; preventive intervention; response; sensor; wearable device; wearable sensor technology

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.

该应用的广泛目的是建立一种测量运动学的方法（即 人体运动）对日常生活中现实世界中平衡损失（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，绊倒 估计有60％的跌倒。许多实验室 研究表明，这些跌倒通常是由于年龄相关的平衡下降而引起的 恢复对这些叶的反应。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，因此允许断开连接 在实验室的旅行，滑移以及现实世界中的实际旅行和滑移之间。我们最近 开发了一种新颖的技术来捕获现实世界的运动学及其上下文 使用可穿戴传感器和语音录制器。本应用程序的目标1将调查 通过询问平衡障碍的社区居住，该技术可用于扩展使用 老年人每天在醒来的时间每天佩戴系统，持续三个星期。 AIM 2意志 通过引起AIM 1的旅行和滑倒来涉及实验室验证该技术的验证1 参与者同时测量LOB响应运动器与可穿戴传感器 系统和金标准光电运动捕获系统。 AIM 3将使用可穿戴 AIM 1和2的传感器数据开始探索现实世界和之间的差异 实验室LOB响应。捕获LOB响应的详细运动学的能力和 他们在现实世界中的背景将对秋季产生深远而根本的影响 预防努力。首先，它将阐明实验室和现实世界之间的任何差异 可以增强实验室研究对现实世界的普遍性的群。其次，会 在评估频率和 现实世界瀑布的特征。第三，它将大大增强对秋季的关键评估 预防干预措施及其最大化培训益处的机制。第四，会 协助开发算法的研究人员自动检测现实世界中的群 使用可穿戴传感器的个人。