Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

Real-time walking behavior monitoring is essential in ensuring safety and improving people’s physical conditions with mobility difficulties. In this paper, a real-time walking motion detection system based on the intelligent walking stick, mobile phone and multi-label imbalance classification method combining focal loss and LightGBM (MFGBoost) is proposed. The Internet of Things (IoT) technology is utilized for communicating between the walking stick and mobile phone. The new MFGBoost is embedded into the Raspberry Pi to classify human motions. MFGBoost is scalable, and other boosting models, such as XGBoost, could also be used as its base classifier. An improved derivation method of the multi-classification focal loss function is proposed in this paper, which is the key to the combination of multi-classification focal loss and Boosting algorithms. We propose a novel denoise method based on window matrix and COPOD algorithm (W-OD). The window matrix is designed to extract data features and smooth noise, and COPOD could output the noise level of the model. A weighted loss function is designed to adjust the model’s attention to different samples based on the W-OD algorithm. We evaluate the latest classification model from multiple perspectives on multiple benchmark datasets and demonstrate that MFGBoost and W-OD-MFGBoost could improve classification performance and decision-making efficiency. Experiments conducted on human motion datasets show that W-OD-MFGBoost could achieve more than 97 percent classification accuracy.