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Elderly gait is a source of rich information about their physical and mentalhealth condition. As an alternative to the multiple sensors on the lower bodyparts, a single sensor on the pelvis has a positional advantage and anabundance of information acquirable. This study aimed to explore a way ofimproving the accuracy of gait event detection in the elderly using a singlesensor on the waist and deep learning models. Data was gathered from elderlysubjects equipped with three IMU sensors while they walked. The input was takenonly from the waist sensor was used to train 16 deep-learning models includingCNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attentionmechanism. The groundtruth was extracted from foot IMU sensors. Fairly highaccuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at thetolerance window of $\pm$6TS ($\pm$6ms) and $\pm$1TS ($\pm$1ms) respectively.Advancing from the previous studies exploring gait event detection, the modelshowed a great improvement in terms of its prediction error having an MAE of6.239ms and 5.24ms for HS and TO events respectively at the tolerance window of$\pm$1TS. The results showed that the use of CNN-RNN hybrid models withAttention and Bidirectional mechanisms is promising for accurate gait eventdetection using a single waist sensor. The study can contribute to reducing theburden of gait detection and increase its applicability in future wearabledevices that can be used for remote health monitoring (RHM) or diagnosis basedthereon.

Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor