Frugal Machine Learning

11/05/2021
by   Mikhail Evchenko, et al.
10

Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things. In most machine learning applications, the main focus is on the quality of the results achieved (e.g., prediction accuracy), and hence vast amounts of data are being collected, requiring significant computational resources to build models. In many scenarios, however, it is infeasible or impractical to set up large centralized data repositories. In personal health, for instance, privacy issues may inhibit the sharing of detailed personal data. In such cases, machine learning should ideally be performed on wearable devices themselves, which raises major computational limitations such as the battery capacity of smartwatches. This paper thus investigates frugal learning, aimed to build the most accurate possible models using the least amount of resources. A wide range of learning algorithms is examined through a frugal lens, analyzing their accuracy/runtime performance on a wide range of data sets. The most promising algorithms are thereafter assessed in a real-world scenario by implementing them in a smartwatch and letting them learn activity recognition models on the watch itself.

READ FULL TEXT

page 21

page 22

research
07/13/2022

Collaborative Machine Learning-Driven Internet of Medical Things – A Systematic Literature Review

The growing adoption of IoT devices for healthcare has enabled researche...
research
05/09/2019

Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

Recent years have witnessed the rapid development of human activity reco...
research
09/28/2017

Inference of Personal Attributes from Tweets Using Machine Learning

Using machine learning algorithms, including deep learning, we studied t...
research
07/07/2019

Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification

Advances in embedded systems have enabled integration of many lightweigh...
research
08/01/2022

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing

Health monitoring applications increasingly rely on machine learning tec...
research
01/16/2022

Enhancement of Healthcare Data Performance Metrics using Neural Network Machine Learning Algorithms

Patients are often encouraged to make use of wearable devices for remote...
research
11/30/2020

Robust error bounds for quantised and pruned neural networks

With the rise of smartphones and the internet-of-things, data is increas...

Please sign up or login with your details

Forgot password? Click here to reset