On unifying deep learning and edge computing for human motion analysis in exergames development

This work describes a novel methodology for creating exergames on an edge-native platform with the integration of multiple deep neural networks. A prototype of the platform, which includes capabilities for innovative gameplay and advanced user interactivity, has been implemented and deployed in a real-world scenario. At core of the proposed methodology is the ad hoc training of classifiers for posture classification which can be dynamically adapted to the specific requirements of the usage scenario, operational and environmental conditions allowing for real-time identification of events and advanced game control. The proposed solution is ideal for individual consumers in a home environment since is supports by-design edge platforms minimizing the cost of the system and enabling in parallel the communication with state-of-the-art hardware (i.e., GPUs, TPUs, computer boards) for real-time operation. The proposed system allows the collection and analysis of game data, which can be exploited by specialized personnel in rehabilitation centers or for other purposes in the areas of healthcare and assisted living.

Automated summary

This work presents a novel methodology for creating exergames on an edge-native platform by integrating multiple deep neural networks, with a focus on training posture classifiers dynamically adapted to specific requirements for real-time event identification and game control. Ideal for individual consumers in a home environment, the system also allows communication with state-of-the-art hardware and enables the collection and analysis of game data for specialized use in rehabilitation centers and healthcare.