Year: 2024
Type: article
Abstract: Self-quantifying technology enables users to evaluate their performance and define strategies for improvement. Workplace technology assists users in identifying activities and patterns that facilitate task completion. Software that measures workplace signals requires access to information from the user interface and peripherals. Current software solutions that track computer activity lack goal orientation and do not share raw data that could aid users and researchers in analyzing behavioral patterns at work. This paper presents Wellbot, an intelligent AI assistant capable of tracking workers' activities and providing personalized insights. The solution employs machine learning models to detect goal-oriented and recreational time. Based on users' goals and recent activities, Wellbot generates recommendations aided by Large Language Models. This work aims to enable tools that assist workers through an improved understanding of their context and goals.
Authors: Ravindu Perera, Claire Gendron, Cecilia Delgado, Alberto Campos Hernández, Victor Rios Muñoz, Mathew Rogers, Carlos Toxtli
Institution: Clemson University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406809318