Transforming Deep Learning Models for Resource-Efficient Activity Recognition on Mobile Devices

Training machine learning algorithms on resource-constrained mobile and wearable devices, particularly DL algorithms, is challenging and sometimes even impossible due to the limited computation power, storage, and, most importantly, the battery. TensorflowLite is a popular platform for optimizing deep learning architectures to be deployed on mobile devices. In this project, the focus is on human activity recognition with the motion sensors embedded in smartphones. You are expected to deploy an optimized model on an Android platform.  The aim is to compare the performance of the original models in terms of model accuracy, model size, and resource usages, such as CPU, memory, and energy usage, with their optimized versions.