Figure 3.

The CNN architecture of the proposed fall-risk classification model. The 3-channel acceleration or angular velocity 3 s segments are fed into the convolutional building blocks, and the high-level kinematics feature map is extracted. The features are flattened and classified as faller/non-faller by a fully connected neural network.