Anjana Wijekoon · EANN 2020
Transferability of Personalised Exercise Recognition Models
Exercise Recognition is relevant in many high impact domains, from healthcare to recreational activities to sports sciences, which faces many challenges when deployed in the real world. For instance, typical lab performances of Machine Learning~(ML) models, are hard to replicate, due to differences in personal nuances, traits and ambulatory rhythms. Thus effective transferability of a trained model depends on its ability to adapt and personalise to a new user or a user group. We look at person-agnostic and person-aware methods of evaluation to identify transferability of generic deep learning models and introduce a personalised algorithm to improve performance in different user groups. Our findings show that Exercise Recognition, when compared to results with other HAR tasks, to be a far more challenging personalisation problem.