# Resource-Efficient Computing in Wearable Systems

## TL;DR

This paper introduces optimization techniques for wearable systems that significantly reduce memory and computation for real-time activity classification, balancing accuracy and resource usage.

## Contribution

It presents novel hierarchical SVM structures and memory optimization methods tailored for resource-constrained wearable devices.

## Key findings

- Saved up to 56% memory in activity recognition tasks
- Reduced computation by hierarchical SVM design
- Demonstrated trade-off between accuracy and memory savings

## Abstract

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.