Gießler Maximilian, Werth Julian, Waltersberger Bernd, Karamanidis Kiros
Department of Mechanical and Process Engineering, Offenburg University of Applied Sciences, Offenburg, Germany.
Sport and Exercise Science Research Centre, School of Applied Sciences, London South Bank University, London, UK.
Commun Eng. 2024 Dec 16;3(1):181. doi: 10.1038/s44172-024-00325-x.
Accurate and automatic assessments of body segment kinematics via wearable sensors are essential to provide new insights into the complex interactions between active lifestyle and fall risk in various populations. To remotely assess near-falls due to balance disturbances in daily life, current approaches primarily rely on biased questionnaires, while contemporary data-driven research focuses on preliminary fall-related scenarios. Here, we worked on an automated framework based on accurate trunk kinematics, enabling the detection of near-fall scenarios during locomotion. Using a wearable inertial measurement cluster in conjunction with evaluation algorithms focusing on trunk angular acceleration, the proposed sensor-framework approach revealed accurate distinguishment of balance disturbances related to trips and slips, thereby minimising false detections during activities of daily living. An important factor contributing to the framework's high sensitivity and specificity for automatic detection of near-falls was the consideration of the individual's gait characteristics. Therefore, the sensor-framework presents an opportunity to substantially impact remote fall risk assessment in healthy and pathological conditions outside the laboratory.
通过可穿戴传感器对身体节段运动学进行准确且自动的评估，对于深入了解不同人群积极生活方式与跌倒风险之间的复杂相互作用至关重要。为了远程评估日常生活中因平衡干扰导致的险些跌倒情况，当前方法主要依赖有偏差的问卷调查，而当代数据驱动的研究则侧重于与跌倒相关的初步场景。在此，我们致力于构建一个基于准确躯干运动学的自动化框架，以实现对运动过程中险些跌倒场景的检测。通过将可穿戴惯性测量集群与专注于躯干角加速度的评估算法相结合，所提出的传感器框架方法能够准确区分与绊倒和滑倒相关的平衡干扰，从而在日常生活活动中最大限度地减少误报。该框架对险些跌倒自动检测具有高灵敏度和特异性的一个重要因素是考虑了个体的步态特征。因此，该传感器框架为在实验室之外的健康和病理状况下大幅影响远程跌倒风险评估提供了契机。
