MindArc: On-Device AI for Digital Wellbeing and Habit Formation

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.

Authors

Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-04-11
DOI
https://doi.org/10.64388/irev9i10-1716250
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
0.00
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article

MindArc: On-Device AI for Digital Wellbeing and Habit Formation

K D Adeena, Kiran K Kannan, Richie Antony, Stewart Lalu et al.
Iconic Research and Engineering Journals
Digital Mental Health Interventions
article

MindArc: On-Device AI for Digital Wellbeing and Habit Formation

K D Adeena, Kiran K Kannan, Richie Antony, Stewart Lalu, T R Chandrasagar
article en

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.

Iconic Research and Engineering JournalsVol. 9(10)
Decent work and economic growth
Openalex Percentile: Top 7%
Digital Mental Health Interventions
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