Weight-Mate: Adaptive Training Support for Weight Lifting - Paay, J.; Kjeldskov, J.; Sorensen, F.; Jensen, T.; Tirosh, O. (Proceedings of the 31st Australian Conference on Human-Computer-Interaction, 2019)
Weightlifting is easy to learn, but difficult to master. People who do weightlifting do it to improve their health, strengthen their muscles and build their physique. However, due to the complex and precise body positioning required, even experienced weightlifters require assistance in perfecting their technique. At the same time, the training requirements of the individual change over time, as they perfect and hone their craft. To help weightlifters achieve optimum personal performance, we designed Weight-Mate, a prototype wearable system for giving weightlifters of different skill levels personalized, precise and non-distracting immediate feedback on how to correct their current body positioning during deadlift training. By iterating Weight-Mate using cooperative usability testing (CUT) with weightlifters of different competencies with their coaches we designed a system that could adapt to individual physiology and training needs. The Weight-Mate sensor suit maps the lifter's body configuration against the ideal deadlift position throughout all stages of the life, as defined by their coach, and provides non-intrusive feedback to the lifter to correct their body position. Our formative evaluation with ten weightlifters shows that an adaptive approach to digital weight training offers great promise in assisting weight lifters of all levels to improve their technique, and hence improve the safety of the sport.