Ibrahim Alwathiqbellah, Younis Mohammad I
Department of Mechanical Engineering, State University of New York at Binghamton, Binghamton, NY 13902, USA.
Sensors (Basel). 2014 Jul 8;14(7):12149-73. doi: 10.3390/s140712149.
This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each acceleration component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the acceleration components on various locations on the human body during various kinds of falls and ADLs. A two-degree-of-freedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumped-parameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept.
本文基于人体跌倒和日常生活活动（ADL）的详细实验数据，提出了一种全新且简单的跌倒检测概念。建立与MEMS传感器兼容的合适跌倒算法，需要有关跌倒和ADL的详细数据，这些数据要能清晰表明人体上可能的传感器节点位置（如髋部、头部和胸部）处运动学的变化。目前，缺乏与这些节点位置相关的每个加速度分量的确切方向和大小的数据。这对于MEMS结构至关重要，因为其惯性元件非常靠近衬底且由电容偏置，因此，无论运动方向是朝向还是远离衬底，对运动方向都非常敏感。这项工作展示了在各种跌倒和ADL过程中人体不同位置的加速度分量的详细数据。使用二自由度模型来辅助解释实验数据。然后建立了基于MEMS开关的跌倒检测算法。提出了一种基于该算法的新传感概念。该概念基于采用多个惯性传感器作为串联连接的电气开关，在接收到真正的跌倒信号时同时触发。在日常生活活动中，一些或没有开关会被触发，从而导致开路配置，进而防止误报。给出了该装置的集总参数模型，并展示了说明新装置概念的初步模拟结果。
