Resource-Efficient Computing in Wearable Systems
This work addresses resource constraints in wearable systems for real-time classification, but it is incremental as it builds on existing SVM methods.
The paper tackled the problem of resource efficiency in wearable systems by proposing optimization techniques for memory usage and computation in real-time classification, achieving up to 56% memory savings in activity recognition experiments.
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.