Abstract
In this paper, we propose SmartMonitor, a system that utilizes an edge hardware device to record, analyze and log user activity while not interfering with said activity. The analysis of the activity can help users identify the time they spend on different tasks and provide real-time feedback from a Large Language Model (LLM) to give better awareness of user activity. The SmartMonitor passes through the HDMI signal from the video card and analyzes the user’s activity on edge using two artificial intelligence models, logs user activity and sends the log for analysis to a Large Language Model for feedback. SmartMonitor enables non-intrusive self-quantifying technology that both records and analyzes user activity while protecting privacy and reducing the processing burden on the user’s device, which can serve as an excellent research framework for behavioral analysis in the workplace and a way to enhance user's work activity.
Cite this work
Wangfan Li, Ravindu Tharanga Perera, Claire Gendron, Cecilia Delgado Solorzano, and Carlos Toxtli-Hernández. 2024. SmartMonitor: Edge-Based Activity Monitoring from Visual Input. CHIWORK '24: 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work, Demo Track. https://doi.org/10.13140/RG.2.2.20165.46561
@misc{Li2024SmartMonitor,
doi = {10.13140/RG.2.2.20165.46561},
url = {https://www.researchgate.net/doi/10.13140/RG.2.2.20165.46561},
author = {{Wangfan Li} and {Ravindu Perara} and Gendron, Claire and Delgado-Solórzano, Cecilia and Toxtli, Carlos},
language = {en},
title = {SmartMonitor: Edge-Based Activity Monitoring from Visual Input},
publisher = {Unpublished},
year = {2024},
howpublished = {Preprint, ResearchGate},
note = {Preprint}
}Related
