An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers

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The two-wheeler accidents in most populated and developing countries have become vulnerable and six accidents happen every hour on average. This paper proposes an efficient automatic accident detection system that attempts to detect the occurrences of the accidents in powered two-wheelers (PTW) automatically using vehicle-dependent parameters and the physiological parameters of the rider in real-time. The proposed system builds an accident detection system in PTW using three steps namely, critical event detection system, accident detection system, and severity assessment system. The critical event detection system reads the accelerometer sensor values from the On-Board Diagnostic (OBD) unit mounted on the PTW and classifies the state of the vehicle as normal, fall-like, and fall through the enhanced decision tree algorithm. The enhanced decision tree algorithm uses a tanh function to calculate entropy values. The rules are extracted to fix the threshold by pruning the decision tree to identify the fall of the vehicle and the rider. Due to the unstable nature of PTW and the rider, a novel Adaptive Sequence Window algorithm (ASW) is proposed to substantiate and validate the occurrence of accidents based on the sequence of states identified. Once the accident is detected, the Decision Support System (DSS) running on the OBD mounted on the PTW decides the severity of the accident by combining the three parameters namely fall of the vehicle, fall of the rider, and pulse rate of the rider using the first-order predicate logic rules. The enhanced decision tree algorithm outperforms the other classifiers such as naïve Bayes, artificial neural network, and recurrent neural network with an accuracy of 99.8%. The OBD unit mounted on the PTW and the rider’s helmet is used to detect the occurrence of accidents automatically along with its severity with less time. The ASW algorithm enables the system to detect the fall of the vehicle and rider within five minutes and prevents false positives. Further, the information can be communicated based on the severity of the accident to the emergency medical service for quick response.