Walking Aid Identification Using Wearables

The distribution of wrist-worn wearable devices grows rapidly, also among aging people. Within such wearables, inertial sensors are incorporated and may be used, next to their intended purpose, for identifying the currently used walking aid of the senior. After detecting whether the user is moving or not moving, a machine learning approach can be used to identify the currently used walking aid using acceleration and angular rate features. To overcome the wearable attitude uncertainty, the computed features are based on the normalized measurements of the three sensor axes, and they overlap at approximately 0.25 s. The defined walking aids for this approach include standing, walking normal, use of a walking cane and the use of a walker or a wheelchair. A ten-fold cross validation with the labelled training data delivers recall values of 98 % for a window size of 2.56 s. When predicting the currently used walking aid in real time, blunders may occur in the classification. Such blunders can additionally be overcome by the modelling of the probability of the transition between the use of one walking aid to the use of another. The determination of the used walking aid in real time delivers 97 % correctly identified walking aids within defined test scenarios. The identification of the currently used walking aid is mainly used as input parameter for positioning or routing applications, e.g., planning a path which is walkable with the currently used walking aid.