We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the probability distribution of output classes occurrences. Also, we propose a memory optimization technique based on SVM parameters, which results in storing fewer support vectors and as a result requiring less memory. To demonstrate the efficiency of our proposed techniques, we performed an activity recognition experiment and were able to save up to 35% and 56% in memory storage when classifying 14 and 6 different activities, respectively. In addition, we demonstrated that there is a trade-off between accuracy of classification and memory savings, which can be controlled based on application requirements.
@article{arxiv.1907.03247,
title = {Resource-Efficient Computing in Wearable Systems},
author = {Mahdi Pedram and Mahsan Rofouei and Francesco Fraternali and Zhila Esna Ashari and Hassan Ghasemzadeh},
journal= {arXiv preprint arXiv:1907.03247},
year = {2019}
}