Technologies targeting a correct execution of physical training exercises typically use pre-determined models for what they consider correct, automatizing instruction and feedback. This falls short on catering to diverse trainees and exercises. We explore an alternative design approach, in which technology provides open-ended feedback for trainers and trainees to use during training. With a personal trainer we designed the augmentation of 18 strength training exercises with BodyLights: 3D printed wearable projecting lights that augment body movement and orientation. To study them, 15 trainees at different skill levels trained three times with our personal trainer and BodyLights. Our findings show that BodyLights catered to a wide range of trainees and exercises, and supported understanding, executing and correcting diverse technique parameters. We discuss design features and methodological aspects that allowed this; and what open-ended feedback offered in comparison to current technology approaches to support training towards a correct exercise execution.
https://doi.org/10.1145/3313831.3376268
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)