LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing

Abstract

The use of fitness apps to track physical exercise has been proven to promote weight loss and increase physical activity. The most popular forms of exercise are cardiovascular training and resistance training. The overwhelming majority of cardio tracking apps automatically track and analyse outdoor activity with relative ease. In contrast, nearly all commercially available resistance tracking apps only record trivial data, such as the exercise weight and repetition number via manual user input, a level of functionality not far from that of a pen and paper. This paper presents LEAN, a resistance training app and exercise analysis (EA) system for both the iPhone and Apple Watch. The app provides form analysis using machine learning, automatic repetition counting in real time, and other important but seldom studied exercise metrics, such as range of motion on a per-repetition level and average repetition time. All features are implemented using lightweight inference methods that enable real-time feedback on resource-constrained devices. The performance evaluation includes a user survey and benchmarking of all data science features using both ground-truth data from complementary modalities and comparisons with commercial apps.

Fast-MFQE: A Fast Approach for Multi-Frame Quality Enhancement on Compressed Video

“…Nevertheless, it is challenging to apply these methods to other compression tasks due to the limited generalization ability. With the advance of the deep learning method, an expanding range of methods have embraced convolutional neural network (CNN) approaches [ 12 , 13 , 14 , 15 , 16 ] to improve the compressed image quality. In [ 12 ], a four-layer AR-CNN was first introduced to deal with various artifacts in JPEG images.…”

“…The term “lightweight model” refers to compressing the model size to maximize computational speed while preserving the accuracy. Researchers have been paying increasing attention to developing lightweight models in the field of image classification to enable deployment on mobile devices [ 14 , 15 , 16 , 27 , 28 ]. Among the pioneer endeavors in developing lightweight models, SqueezeNet [ 29 ] emerged, replacing 3 × 3 convolutions with 1 × 1 convolutions, resulting in a parameter reduction of approximately one-fiftieth compared to AlexNet [ 30 ].…”