Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Study on Temperature Variance for SimCLR based Activity Recognition

Pranjal Kumar * ORCID logo
Version 1 : Received: 6 July 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (11:38:18 CEST)
Version 2 : Received: 9 July 2021 / Approved: 9 July 2021 / Online: 9 July 2021 (15:46:05 CEST)

How to cite: Kumar, P. Study on Temperature Variance for SimCLR based Activity Recognition. Preprints 2021, 2021070138 (doi: 10.20944/preprints202107.0138.v1). Kumar, P. Study on Temperature Variance for SimCLR based Activity Recognition. Preprints 2021, 2021070138 (doi: 10.20944/preprints202107.0138.v1).

Abstract

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. In this paper, we propose a robust SimCLR model for human activity recognition with a temperature variance study. In this work, SimCLR, a contrasting learning technique is optimized via regulating the temperature for visual representations, is incorporated for improving the HAR performance in healthcare.

Keywords

Contrastive learning; activity recognition

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.