Static Postural Transition-based Technique and Efficient Feature Extraction for Sensor-based Activity Recognition

Smartphone sensor-based activity recognition seeks broad, high-level knowledge about human behaviors from multitudes of low-level sensor readings, and makes considerable headway in healthcare domain. Our primary contribution is to study the effective pre-processing technique and the extraction of robust features for the classification of sensor data for human activity recognition (HAR). In the pre-processing stages, we investigated multiple filtering parameters for reducing waveform delay, smartphone orientation constraint by introducing magnitude and jerk-based features, and optimum window length for analyzing the trade-off between model performance and latency. Besides, we proposed a feature named “Average Height” that summarizes the average peak to trough distance of the activity and encodes any change of motion for classification. We also proposed two feature selection techniques for offline and real-time faster activity recognition, and analyzed the impact of different feature sets on classifying different activities. Moreover, after performing the classification with optimized hyperparameters, we proposed a Static Postural Transition-based Post-Processing (SPTPP) technique. This post-processing approach analyzes the existence of postural transition from previous window activity to current window activity, and helps to improve the model output by analyzing the posture change. The impact of our proposed techniques are demonstrated on three benchmark datasets named HASC, HAR, and HAPT, where we obtained the state-of-the-art results. We used HASC dataset for optimizing model parameters in different stages, and explored HAR and HAPT datasets as test-beds to verify our optimizations and postprocessing technique.