Pre-Impact Fall Detection: Optimal Sensor Positioning Based on a Machine Learning Paradigm

The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify walking from unexpected perturbations. The Mean Detection Time (MDT) was 351±123 ms with an Accuracy of 95.4%. The procedure was repeated with data related to different subsets of all body segments whose variability appeared strongly influenced by the perturbation-induced dynamic modifications. Accordingly, feet and hands accounted for most data information and the performance of the algorithm were slightly reduced using their combination. Results support the hypothesis that, in the framework of the proposed approach, the information conveyed by all the body segments is redundant to achieve effective fall detection, and suitable performance can be obtained by simply observing the kinematics of upper and lower distal extremities. Future studies are required to assess the extent to which such results can be reproduced in older adults and in different experimental conditions.

本研究旨在筛选最优身体节段子集，以实现稳定行走向滑倒事件过渡的快速且可靠检测。15名健康青年受试者在行走过程中遭遇意外扰动。研究记录了全身三维运动学(3D kinematics)数据，并开发了机器学习算法以检测扰动事件。具体而言，通过独立成分分析(Independent Component Analysis)对所有身体节段的线加速度进行解析，并采用神经网络(Neural Network)对正常行走与意外扰动进行分类。平均检测时间(Mean Detection Time, MDT)为351±123毫秒，分类准确率达95.4%。研究针对不同身体节段子集的相关数据重复了上述实验流程，结果显示这些子集的变异性显著受扰动引发的动态变化影响。据此发现，足部与手部承载了绝大多数数据信息，而同时使用二者时算法性能略有下降。研究结果验证了如下假设：在本研究提出的方法框架下，全身体节段所携带的信息对于实现高效跌倒检测存在冗余；仅需观测上下肢远端的运动学数据，即可获得符合要求的检测性能。未来仍需开展相关研究，以评估该结果在老年人群体与不同实验条件下的可复现程度。