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: 10693380
• 负责人: Neil Alexander
• 金额: $ 22.83万
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693380
• 关键词: Adherence; Age; Agreement; Algorithms; Awareness; Body measure procedure; Characteristics; Classification; Communities; Data; Development; Devices; Effectiveness; Elderly; Equilibrium; Etiology; Evaluation; Event; Exercise; Exhibits; Exposure to; Fall prevention; Frequencies; Gait; 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; diaries; fall risk; falls; feasibility testing; foot; hazard; improved; injury-related death; innovation; kinematics; memory recall; novel; preventive intervention; response; sensor; 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.

本申请的广泛目标是建立一种测量运动学（即， 身体运动）对日常生活中现实世界中的平衡丧失（LOB）的反应。 福尔斯是老年人受伤和与受伤有关的死亡的主要原因， 据估计，60%的福尔斯是由滑倒造成的。众多实验室 研究表明，这些福尔斯下降通常是由于年龄相关的平衡下降 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， 在实验室外评估LOB响应，因此允许断开连接持续存在 在实验室研究的绊倒和滑倒和现实世界中的实际绊倒和滑倒之间。我们最近 开发了一种新颖的技术来捕获真实世界LOB的运动学及其上下文 使用可穿戴传感器和录音器。本申请的目标1将研究 通过询问平衡受损的社区住宅， 老年人每天在清醒的时候佩戴该系统，持续三周。目标2将 通过在目标1中诱导绊倒和滑倒，对该技术进行实验室验证 参与者同时使用可穿戴传感器测量LOB响应运动学 系统和黄金标准的光电运动捕捉系统。Aim 3将使用可穿戴设备 从目标1和目标2到目标开始， 实验室LOB响应。能够捕获LOB响应的详细运动学， 他们在现实世界中的背景将对秋天产生深远而根本的影响 预防工作。首先，它将澄清实验室和现实世界之间的任何差异 LOB，以增强实验室研究对现实世界的普遍性。第二， 克服了在评估频率时使用记忆回忆的众所周知的局限性， 真实世界福尔斯的特征。第三，它将大大提高对跌倒的批判性评价， 预防干预措施及其机制，以最大限度地提高培训效益。第四， 帮助研究人员开发算法，以自动检测 使用可穿戴传感器的个人。