Context-Aware Hard and Slow Fall Detection

Fall is one of the main causes of injuries for the elderly, and fall detection (FD) for senior monitoring has received considerable attention from both the academic community and healthcare industries. In recent years, there has been an increasing interest in using wearable sensors, such as accelerometers to monitor the subject’s body movement and apply Machine Learning (ML) methods to detect and prevent falls. Since it is extremely difficult to collect accelerometer data of real falls during activities of daily living (ADL), researchers tended to rely on simulating falls in well-protected environments. They collected ADLs separately, applied ML algorithms to classify falls and ADLs, and reported very high FD accuracy rates. However, these studies cannot be applied in a real fall context. In this paper, instead of classifying ADL and fall separately, we propose to incorporate fall data within ADL data to obtain more realistic datasets and apply ML to detect falls. Several ML algorithms including CatBoost (CB), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) were applied to the datasets. Experimental results show a fall detection accuracy of $88.70 \%$. We also extend our work to cover slow fall which, to the best of our knowledge, was not extensively addressed in previous works.