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
Citations
IRE Journals:
T R Chandrasagar, Richie Antony, Kiran K Kannan, Stewart Lalu, Asst. Prof. Adeena K D "MindArc: On-Device AI for Digital Wellbeing and Habit Formation" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1063-1072 https://doi.org/10.64388/IREV9I10-1716250
IEEE:
T R Chandrasagar, Richie Antony, Kiran K Kannan, Stewart Lalu, Asst. Prof. Adeena K D
"MindArc: On-Device AI for Digital Wellbeing and Habit Formation" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716250
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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