Smartphone Sensor Based Physical Activity Identification by Using Hardware-Efficient Support Vector Machines for Multiclass Classification

Smartphone sensor-based activity identification has been recently received significant attention in versatile applications such as elderly people physical condition monitoring, general health monitoring, disease likelihood and other vital contexts to make human life more productive, secure and sound. Due to sensor derived data popularity, along with smartphone researchers are using other ad-hoc wearable devices like smartwatch, fitness tracker, fitbit for activity data collection. This paper emphasizes on heterogeneous optimal feature selection process based on Sequential Floating Forward Search (SFFS) approach. At the first stage, prominent discriminant features are elected from both time and frequency domain signal in order to create a robust model with better accuracy and generalization capability. Then the prime features are trained by Multiclass Support Vector Machines (SVMs) to identify twelve human activities by analyzing accelerometer-gyroscope sensory data taken from a published dataset. In this paper, SVM is applied to create nonlinear classifiers by adopting the kernel trick. Lastly, we have validated our model with online based benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification with trivial time duration as opposed to neural network.