Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Study on Temperature (τ) Variation 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 (τ) Variation for SimCLR based Activity Recognition. Preprints 2021, 2021070138 (doi: 10.20944/preprints202107.0138.v2). Kumar, P. Study on Temperature (τ) Variation for SimCLR based Activity Recognition. Preprints 2021, 2021070138 (doi: 10.20944/preprints202107.0138.v2).

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. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

Keywords

Contrastive learning ; activity recognition ; healthcare

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (1)

Comment 1
Received: 9 July 2021
Commenter: Pranjal Kumar
Commenter's Conflict of Interests: Author
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