Study on Temperature Variance for SimCLR based Activity Recognition

Pranjal Kumar

doi:10.20944/preprints202107.0138.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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)

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.

Contrastive learning; activity recognition

Computer Science and Mathematics, Algebra and Number Theory

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Study on Temperature Variance for SimCLR based Activity Recognition

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