Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning

Human activity recognition (HAR) plays a crucial role in human-computer interaction, smart home, health monitoring and elderly care. However, existing methods typically utilize camera, radio frequency (RF) signals or wearable devices for activity recognition. Each single-sensor modality has its inherent limitations, like camera-based methods having blind spots, Wi-Fi-based methods depending on the environment and the inconvenience of wearing Inertial Measurement Unit (IMU) devices. In this paper, we propose a HAR system that leverages three types of sensor combinations: Wi-Fi, IMU and a hybrid of Wi-Fi+IMU. We utilize the Channel State Information (CSI) provided by Wi-Fi and the accelerometer and gyroscope data from IMU devices to capture activity characteristics. Then, we employ six machine learning algorithms to recognize eight types of daily activities. These algorithms include Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Decision Tree, Random Forest, Logistic Regression and k-Nearest Neighbors (kNN). Additionally, we investigate the accuracy of hand gesture recognition using different sensor combinations and analyze the calculation speed of each combination. We conduct a survey to collect user feedback on the performance of various sensor combinations in our HAR system. The results show that the combination of CSI+IMU yields the best HAR recognition accuracy, with a accuracy of 89.38%. The SVM algorithm consistently performs well across all systems, especially excelling in the CSI+IMU system supported by energy and average Fast Fourier Transform (FFT) features. However, we also find that the success of sensor fusion depends on specific algorithms and features. Fusion of CSI and IMU does not universally enhance recognition accuracy for all features and algorithms and can, in some cases, actually reduce accuracy.