A Robust LSTM-based Step Detection Algorithm based on Low-cost Wearable Sensors

With the continuous development of smart wearable devices and with the emergence of the Internet of things era, the need for both indoor and outdoor positioning technology has become increasingly imperative. Wearable watches can enable the positioning of their movement tracks and recognize the user's movement patterns and provide services such as safety monitoring in specific scenarios such as construction sites and tunnels. However, current smart watches have some problems, such as inaccurate sensor data collection, unstable positioning effect, low accuracy of human posture recognition, especially, the performance of step detection based on wearable sensors is affected by the complex human motion. This paper proposes a low-cost and high-precision gait detection model according to Long Short-Term Memory (LSTM) neural network, the motion features provided by pre-processed acceleration vector are extracted for model training and prediction, which realizes the step detection rate of more than 98.5% and outperforms the existing step recognition algorithms and achieves a relatively accurate step notation through a large amount of real-world experiment on smart watch, laying the foundation for subsequent indoor positioning.