The use of smartphones for activity recognition is becoming common practice. Most approaches use a single pretrained classifier to recognize activities for all users. Research studies, however, have highlighted how a personalized trained classifier could provide better accuracy. Data labeling for ground truth generation, however, is a time-consuming process. The challenge is further exacerbated when opting for a personalized approach that requires user specific datasets to be labeled, making conventional supervised approaches unfeasible. In this work, we present early results on the investigation into a weakly supervised approach for online personalized activity recognition. This paper describes: (i) a heuristic to generate weak labels used for personalized training, (ii) a comparison of accuracy obtained using a weakly supervised classifier against a conventional ground truth trained classifier. Preliminary results show an overall accuracy of 87% of a fully supervised approach against a 74% with the proposed weakly supervised approach.
|Title of host publication||2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)|
|Publication status||Published - 8 Oct 2018|
|Event||2018 IEEE International Conference on Pervasive Computing and Communications (PerCom Workshops) - Athens, Greece|
Duration: 19 Mar 2018 → 23 Mar 2018
|Conference||2018 IEEE International Conference on Pervasive Computing and Communications (PerCom Workshops)|
|Period||19/03/18 → 23/03/18|
Cruciani, F., Cleland, I., Nugent, CD., McCullagh, P., Kare, S., & Josef, H. (2018). Personalized Online Training for Physical Activity monitoring using weak labels. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 567-572). IEEE. https://doi.org/10.1109/PERCOMW.2018.8480292