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MindArc: On-Device AI for Digital Wellbeing and Habit Formation
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Mobile Computing
DOI: https://doi.org/10.64388/IREV9I10-1716250
Abstract
Excessive smartphone usage degrades productivity and mental wellbeing, while existing digital wellbeing solutions provide limited enforcement and inadequate privacy safeguards. MindArc is an on-device digital wellbeing framework integrating real-time app restriction, usage analytics, activity-based unlocking, and gamified feedback. The system’s three-layer architecture leverages Android AccessibilityService for reliable foreground app interception, ML Kit Pose Detection for real-time exercise quantification, and Room-backed persistence for offline-first operation. A four-phase finite state machine with exponential moving average smoothing drives pushup and squat repetition counting. Reward mechanisms link verified physical and cognitive effort directly to screen-time grants, promoting sustained behavioral change. Experimental results validate reliable enforcement, accurate tracking, low latency, and minimal battery overhead, confirming effective digital self-regulation.
Keywords
Digital Wellbeing, Screen Time Management, Accessibility Service, Pose Detection, Gamification, Android, ML Kit, Behavior Change, Habit Formation
How to cite this paper
@article{1716250,
author = {T R Chandrasagar, Richie Antony, Kiran K Kannan, Stewart Lalu, Asst. Prof. Adeena K D},
title = {MindArc: On-Device AI for Digital Wellbeing and Habit Formation},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1063-1072},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716250.pdf},
abstract = {Excessive smartphone usage degrades productivity and mental wellbeing, while existing digital wellbeing solutions provide limited enforcement and inadequate privacy safeguards. MindArc is an on-device digital wellbeing framework integrating real-time app restriction, usage analytics, activity-based unlocking, and gamified feedback. The system’s three-layer architecture leverages Android AccessibilityService for reliable foreground app interception, ML Kit Pose Detection for real-time exercise quantification, and Room-backed persistence for offline-first operation. A four-phase finite state machine with exponential moving average smoothing drives pushup and squat repetition counting. Reward mechanisms link verified physical and cognitive effort directly to screen-time grants, promoting sustained behavioral change. Experimental results validate reliable enforcement, accurate tracking, low latency, and minimal battery overhead, confirming effective digital self-regulation.},
keywords = {Digital Wellbeing, Screen Time Management, Accessibility Service, Pose Detection, Gamification, Android, ML Kit, Behavior Change, Habit Formation},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716250}
}