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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61969
Volume:
10 |
4 |
April 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Engineering
Mahatma Gandhi Mission's College of Engineering and Technology
Department of Computer Engineering
Mahatma Gandhi Mission's College of Engineering and Technology
Department of Computer Engineering
Mahatma Gandhi Mission's College of Engineering and Technology
Department of Computer Engineering
Mahatma Gandhi Mission's College of Engineering and Technology
Department of Computer Engineering
Mahatma Gandhi Mission's College of Engineering and Technology
With the rapid growth of artificial intelligence and digital technologies, monitoring daily productivity and wellness has become increasingly important. This research paper presents Dayscore, an AI-based system designed to analyze daily behavioral data such as activity levels, sleep patterns, mood, and productivity habits. The system processes data collected from wearable devices, mobile applications, and manual user inputs to generate a meaningful daily score representing an individual's overall performance and wellness. By applying machine learning techniques and data analytics, the system identifies behavioral patterns and provides personalized insights and recommendations to improve lifestyle and productivity. The proposed solution aims to enhance self-awareness, support preventive healthcare, and assist individuals in making better decisions regarding their daily routines.
KEYWORDS
The Dayscore system provides an effective solution for integrating productivity and wellness tracking into a single platform. By leveraging artificial intelligence and data analytics, it offers meaningful insights and personalized recommendations to users. The system helps individuals improve their daily habits, enhance productivity, and maintain a balanced lifestyle. Future enhancements may include advanced AI models, deeper integrations with wearable devices, and real-time behavioral predictions for more accurate analysis.
1. K. Kakhi, S. K. Jagatheesaperumal, A. Khosravi, R. Alizadehsani, and U. R. Acharya, “Fatigue Monitoring Using Wearables and AI: Trends, Challenges, and Future Opportunities,” Computers in Biology and Medicine, 2025. 2. A. Pinge et al “Detection and Monitoring of Stress Using Wearables: A Systematic Review,” Frontiers in Computer Science, 2024. 3. S. Abbaspourazad, O. Elachqar, A. C. Miller, et al., \“Large-scale Training of Foundation Models for wearable biosignals”. arXiv preprint arXiv:2312.05409, 2023. 4. P. Sharma, J. C. Justus, M. Thapa, and G. R. Poudel, “Sensors and Systems for Monitoring Mental Fatigue: A Systematic Review,” arXiv preprint arXiv:2307.01666, 2023. 5. A. Bampakis, S. Yfantidou, and A. Vakali, “UBIWEAR: An End-to-End, Data-Driven Framework for Intelligent Physical Activity Prediction to Empower mHealth Interventions,” arXiv preprint arXiv:2212.14731, 2022 (widely cited in recent 2023–24 work).
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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