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2017 IEEE SENSORS
The main aim of this work is to compare the performance of different algorithms for human activity recognition by extracting various statistical time domain and frequency domain features from the inertial sensor data. Our results show that Support Vector Machines with quadratic kernel classifier (accuracy: 93.5%) and Ensemble classifier with bagging and boosting (accuracy: 94.6%) outperforms other known activity classification algorithms. A parallel coordinate plot based on visualization ofdoi:10.1109/icsens.2017.8234222 fatcat:ec3fdyhi2zdzfi5lllc2u4y4s4