A SYSTEMATIC LITERATURE REVIEW OF LIGHTWEIGHT IOT AND ML-BASED APPROACHES FOR MONITORING DIETARY COMPLIANCE AND USER ENGAGEMENT - Olugbemi, Oluwaseun; Afolayan, Obiniyi A .; Opeyemi, Abisoye A.; Taiwo, Abiodun; Adebayo, Phillip; Lawrence, Osanuwa; David, Idris (FUDMA JOURNAL OF SCIENCES, 2026)
In mitigating diseases related to Nutrition, dietary compliance remains vital, however monitoring remains difficult in low-resource settings. Potential solutions that have surfaced include Internet of Things (IoT) and lightweight Machine learning (ML) technologies, but evidence remains fragmented. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and studies from online repositories such as Google Scholar, IEEE, PubMed, Xplore, and Scopus were used in conducting this systematic literature review (SLR), and thereafter supplemented with snowballing. Research papers published between the year 2015 to 2025 were the focus of this review which examined lightweight Machine learning models such as MobileNet and TinyML, IoT-based dietary monitoring solutions, and techniques for user engagement. Among the identified records numbering 358, twenty five (25) studies were included for qualitative synthesis. It was discovered that there was strong accuracy in food recognition by the lightweight ML models while still achieving computational efficiency. Furthermore, even though their use in African contexts were limited, positive indices for efficient diet monitoring were observed in some IoT-based systems using mobile and wearable devices. End-user compliance was enhanced via some techniques of engagement such as personalized feedback, cultural adaptation, and gamification. This review highlights opportunities for affordable, localized, and culturally relevant IoT–ML solutions in regions such as North-Central Nigeria.