A Public Domain Dataset for Human Activity Recognition in Free Living Conditions
In Human Activity Recognition (HAR) supervised Machine Learning methods are predominantly used, making availability of datasets a major issue for research in the field. In particular, the majority of available datasets are collected under controlled conditions. Consequently, models trained under similar circumstances, generally exhibit a significant decrease in recognition accuracy when they are moved to final deployment in the wild, within unconstrained settings. This paper presents a new dataset for HAR, collected in free living and unconstrained conditions. 10 subjects were recruited for the purpose of data collection. Data was recorded over a 6 week period using a smartphone app, and a wristband activity monitor. During the first and last week of observation, participants also wore an ActivPAL" activity logger. The data collected have been partially self labeled by participants, by means of the mobile app provided for data collection. The dataset collected can be used to evaluate HAR algorithm and models in real world unconstrained settings. Together with the description of the dataset, this work presents some preliminary results, obtained cross validating a model trained on the publicly available Extrasensory dataset, and testing its performance on our newly collected dataset. Results obtained highlighted high cross subject variability when testing on new subjects, with a balanced accuracy varying between 53.33% and 90.01% with an average balanced accuracy of 71.73%
2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)