SoberVerse: A Personalized Addiction Recovery System for Relapse Prediction
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Zenodo
2026
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| _version_ | 1866901197238042624 |
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| author | Mohammed Obaidullah S Harshitha Kanthamani Dokka Sowmya Voore Nithya |
| author_facet | Mohammed Obaidullah S Harshitha Kanthamani Dokka Sowmya Voore Nithya |
| contents | SoberVerse is a behaviour-aware addiction and recovery tracking system designed to provide data-driven insights into user habits by integrating emotional states, trigger factors and usage patterns. Existing solutions primarily focus on usage tracking and fail to capture contextual behavioural factors that influence relapse. To address this limitation, the system introduces a quantitative behavioural risk model that evaluates relapse probability using parameters such as mood, craving intensity and trigger frequency. The system is implemented using a reactive architecture with offline-first data management to ensure privacy and low-latency performance. User data is processed locally to generate real-time analytical insights and personalised interventions. Experimental evaluation demonstrates a reduction in high-risk usage patterns and improved behavioural awareness, with average craving frequency decreasing from 5.2 to 3.1 instances per day. The proposed approach provides a unified framework combining behavioural analytics, risk modelling and privacy-preserving system design for personalised recovery tracking. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19878464 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SoberVerse: A Personalized Addiction Recovery System for Relapse Prediction Mohammed Obaidullah S Harshitha Kanthamani Dokka Sowmya Voore Nithya Addiction Recovery Behavioural Analytics Relapse Prediction Risk Modelling Habit Tracking Digital Health Systems Context-Aware Computing Time-Series Analysis Privacy-Preserving Systems SoberVerse is a behaviour-aware addiction and recovery tracking system designed to provide data-driven insights into user habits by integrating emotional states, trigger factors and usage patterns. Existing solutions primarily focus on usage tracking and fail to capture contextual behavioural factors that influence relapse. To address this limitation, the system introduces a quantitative behavioural risk model that evaluates relapse probability using parameters such as mood, craving intensity and trigger frequency. The system is implemented using a reactive architecture with offline-first data management to ensure privacy and low-latency performance. User data is processed locally to generate real-time analytical insights and personalised interventions. Experimental evaluation demonstrates a reduction in high-risk usage patterns and improved behavioural awareness, with average craving frequency decreasing from 5.2 to 3.1 instances per day. The proposed approach provides a unified framework combining behavioural analytics, risk modelling and privacy-preserving system design for personalised recovery tracking. |
| title | SoberVerse: A Personalized Addiction Recovery System for Relapse Prediction |
| topic | Addiction Recovery Behavioural Analytics Relapse Prediction Risk Modelling Habit Tracking Digital Health Systems Context-Aware Computing Time-Series Analysis Privacy-Preserving Systems |
| url | https://doi.org/10.5281/zenodo.19878464 |