Resource-Efficient Computing in Wearable Systems

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 computati...

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Bibliographic Details
Published inarXiv.org
Main Authors Pedram, Mahdi, Rofouei, Mahsan, Fraternali, Francesco, Zhila Esna Ashari, Ghasemzadeh, Hassan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.07.2019
Subjects
Activity recognition
Classification
Computer memory
Optimization
Optimization techniques
Storage
Structural hierarchy
Support vector machines
Wearable technology
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Summary: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.
ISSN:2331-8422