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New method for human activity recognition based on IMU sensors and digital speech processing theory
aUniversity of Belgrade, Faculty of Electrical Engineering + University of Belgrade, Electrical Engineering Institute 'Nikola Tesla'
bUniversity of Belgrade, Electrical Engineering Institute 'Nikola Tesla'

emailnikolacakic@ieent.org
Keywords: human activity recognition; short-time log energy; cumulative sum; activity state detection; IMU sensors; physical exercises
Abstract
This paper presents a new method for human activity recognition (HAR). Nowadays the biggest parts of HAR systems are relying on wearable IMU (inertial measurement unit) sensors. The common IMU sensors are accelerometers and gyroscopes. These sensors are widespread in mobile devices such as smartphones and smart watches. Authors usually use real time signal features as inputs for classifiers that are calculated using sliding windows only. This paper proposes a new method based on speech-silence discrimination technique for detecting the beginning and the end of an activity. The presented method relies on short-time log energy (STLE) and cumulative sum of angle of STLE values. The method was tested on two similar physical activities: squat and knee raise. This algorithm provides a 41.2% pre-classification accuracy, by precise detection of the length of individual exercise states (start, intermediate, and finish position) only. The proposed method reduces complexity, classifying only activities when they are detected (not classifying pause between activities).
References
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Moreno, W., Yurur, O., Liu, C.-H. (2013) Unsupervised posture detection by smartphone accelerometer. Electronics Letters, 49(8): 562-564
Taniguchi, R., El-Shazly, E.H., Shimada, A., Abdelwahab, M. M. (2016) Early gesture recognition with adaptive window selection employing canonical correlation analysis for gaming. Electronics Letters, 52(16): 1379-1381
Tao, D., Jin, L., Yuan, Y., Xue, Y. (2016) Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition. IEEE Transactions on Neural Networks and Learning Systems, 27(6): 1392-1404
Toth-Laufer, E., Varkonyi-Koczy, A.R. (2014) A Soft Computing-Based Hierarchical Sport Activity Risk Level Calculation Model for Supporting Home Exercises. IEEE Transactions on Instrumentation and Measurement, 63(6): 1400-1411
Yurur, O., Liu, C.H., Sheng, Z., Leung, V.C. M., Moreno, W., Leung, K.K. (2016) Context-Awareness for Mobile Sensing: A Survey and Future Directions. IEEE Communications Surveys & Tutorials, 18(1): 68-93
 

About

article language: Serbian
document type: Professional Paper
DOI: 10.5937/zeint28-19587
published in SCIndeks: 28/12/2018
peer review method: single-blind
Creative Commons License 4.0