Fig. 3
From: Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor
The Proposed DNN for shoulder rehabilitation exercise classification. The model consists of five hidden layers, with each layer having a varying number of nodes: the first with 8, the second with 16, the third with 32, the fourth with 64, and the last with 128 nodes. The Rectified Linear Unit (ReLU) function was used as the activation function for these hidden layers. For the output layer, a SoftMax activation function was implemented, allowing for effective multi-class classification