Wearable application profile

In a typical biosignal monitoring application pipeline, the biosignal is captured by sensors on the patient’s body and forwarded as input to the application processing. Typically, the processing step consists of signal preprocessing (i.e., filtering), feature extraction(i.e., time or frequency characteristics), and inference(i.e., ML model) based on these features. However, applications can exhibit a wide range of workloads and computational requirements. For example, feature extraction can be implemented explicitly (that is, manually engineered features) or implicitly (e.g., convolutional neural network (CNN)). Similarly, the inference step can use a lightweight machine learning method, such as a random forest or a computationally intensive deep neural network (DNN).