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Design of a CNN based autonomous in-seat passenger anomaly detection system

Physical attacks, abuse, and harassment’s on the commuters are on rise across the world and detecting distress among passengers is still a techno-social challenge. An autonomous solution to detect passenger anomaly inside the moving vehicle without compromising on the privacy of the commuters is an ideal solution to this problem. Detection of passenger’s state in the form of temporal posture of the passenger on a moving vehicle is an apt solution. The passenger state identification followed by a standard evidence capture and assistance system will fulfill all the demands of a safe mobility ecosystem. A proof-of-concept detection system is designed, developed and tested in a real-world scenario. An array of 15 pressure sensitive conductive (PSC) sensors were optimally placed inside a pair of cushion and rested individually on the backrest and the seat of a hired cab. The embedded sensors were interfaced to an edge device which is equipped to run CNN-LSTM hybrid model to identify passenger anomaly state. A dataset consisting of sensory signals for two different states namely - anomaly, and relaxed, on different road and vehicle conditions, using multiple trials from different subjects, was built to develop a robust model. The CNN-LSTM hybrid model running on edge device reported very high accuracy compared to other models such as SVM, and individual CNN and LSTM models. The CNN-LSTM hybrid model extracts spatio-temporal features to accurately predict the passenger state, which is otherwise a challenging task. A high classification accuracy was reported for the model when applied on unseen dataset which was different from the one used for training the model. A robust CNN-LSTM hybrid model along with a low cost inseat sensory design which is feasible, and easily scalable, offers a reliable solution, without compromising on the privacy of the commuters. The CNN-LSTM model deployed on an edge device was characterized for a latency of less than 2 seconds for the entire system to detect passenger anomaly state.