Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat^®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person.

中文翻译：

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用于人类步态和跌倒诊断的可耳式足部压力传感器

人类跌倒对人的健康构成严重威胁，特别是对老年人和受疾病影响的人。早期发现非自愿的人类步态变化可以预示即将到来的跌倒。因此，人体跌倒警告可以帮助避免跌倒及其对骨骼和关节造成的伤害。基于步态分析的简单易用的跌倒检测系统非常有用，特别是如果该系统的传感器安装在鞋内，不会给用户带来明显的不适感。我们使用三个专门设计的 Velostat ^®创建了一种跌倒预测方法-基于可穿戴脚部传感器安装在鞋衬中。测量的足部压力分布允许通过评估主要参数来分析步态：步进节奏、步长、足跟和足部之间的重量分布以及步态阶段的时间。通过记录受试者的正常步态和模拟异常步态来评估所提出的方法。得到的结果表明了所提出方法的有效性：异常步态检测的准确率达到了94%。通过这种方式，可以在早期预测跌倒或避免步态不协调并警告对象或帮助同伴。