A ubiquitous architecture for wheelchair fall anomaly detection using low-cost embedded sensors and isolation forest algorithm

Abstract

Falls represent one of the major health risk issues world-wide. In this paper, a wheelchair fall anomaly detection framework based on the hybrid Isolation Forest (IF) and threshold based method (TBM) is proposed. The sensor data was obtained from tri-axial orthogonal accelerometer and gyroscope MPU-6050 sensor. In order to handle the uncertainties due to the sensor noise factor, a method based on Zero Angular Rate Update (ZART) and Complementary Filter (CF) was utilized. The concept of sensor fusion was applied to create multi-dimensional training data from the best features selected using the ReliefF algorithm. The IF detector was trained on the best eight-dimensional features selected from 48 features for the detection of the fall events in contrast to 38 features defined in our previous publication in Sheikh and Jilani. Further, the design of a ubiquitous architecture incorporating the proposed fall detection scheme was proposed. The g-mean score accuracy was achieved up-to ∼97.1% with the proposed IF +threshold hybrid strategy. While using only the IF method, an F1-score of 96.2%, an AUC-ROC score of 95.3% and a g-mean score of 92.9 % was obtained.