A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags

Abstract

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

Adaptive Impact Mitigation Based on Predictive Control with Equivalent Mass Identification

“…In turn, within rescue applications, the effective protection of people falling from heights is provided by the use of impact detection systems [17] and safety airbags [18]. Moreover, impact mitigation devices are important for everyday applications, including the design of helmets [19], bicycle dampers and handlebars [20], and wearable airbags [21].…”