A Time Window Based Lightweight Real-Time Activity Recognition Method

Lack of exercise or excessive exercise would damage our body, so real-time human activity recognition using smart-phones or wearable devices can keep people aware of their physical status and contribute to proper exercise. However, most of existing high-precision activity recognition algorithms cannot directly apply to smart-phones or wearable devices due to their high computational cost and large storage request. Meanwhile, these algorithms cannot update recognition model according to user behavior. We propose a time window based lightweight real-time activity recognition method (EasiSports) which uses SVM to recognize five kinds of human activity status including sitting, walking, running, walking upstairs and downstairs with one accelerate sensor and fair low computational cost. Our method can set up personalized activity recognition model in many cases using k-means clustering and preset data to further improve the accuracy and reduce computational cost. Experiments show that our method can achieve an accuracy of 87.45% in recognizing five kinds of human activity status above mentioned. The performance of our algorithm can improve more than 30% compared with other algorithms.