Human Fall Detection- Multimodality Approach
This work addresses fall detection for senior citizens, but it is incremental as it builds on existing datasets and methods.
The paper tackled human fall detection using a multimodal approach with wrist sensor and camera data, finding that using only wrist data did not impact prediction performance compared to multi-sensor methods for binary classification.
Falls have become more frequent in recent years, which has been harmful for senior citizens.Therefore detecting falls have become important and several data sets and machine learning model have been introduced related to fall detection. In this project report, a human fall detection method is proposed using a multi modality approach. We used the UP-FALL detection data set which is collected by dozens of volunteers using different sensors and two cameras. We use wrist sensor with acclerometer data keeping labels to binary classification, namely fall and no fall from the data set.We used fusion of camera and sensor data to increase performance. The experimental results shows that using only wrist data as compared to multi sensor for binary classification did not impact the model prediction performance for fall detection.