Hansa Rajput
The convergence of Internet of Things (IoT) cameras and Edge artificial intelligence (Edge AI) is redefining nutritional monitoring by enabling real-time, automated, and context-aware dietary assessment. Conventional methods such as self-reporting, manual data entry, or infrequent clinical evaluations often produce inconsistent and incomplete information. To address these shortcomings, this chapter introduces an Edge AI-based architecture capable of analyzing food images directly on energy-efficient, low-latency edge devices linked to IoT cameras. By processing visual data locally, the system reduces dependence on cloud infrastructure, strengthens data privacy, and supports deployment in remote or resource-limited environments. Leveraging deep learning models, particularly convolutional neural networks (CNNs), the proposed system detects food items, estimates portion sizes, and computes nutritional values. It can further integrate with mobile health applications and personalized nutrition platforms to deliver real-time feedback, individualized dietary suggestions, and behavioral prompts that promote preventive care for conditions such as obesity, diabetes, and cardiovascular disorders. Despite these advantages, several challenges remain, such as inconsistent lighting conditions, complex backgrounds, and the variability of food appearances, all of which affect model accuracy. To mitigate these issues, the chapter explores the use of federated learning for privacy-preserving distributed training and optimization techniques like model pruning and quantization to improve computational efficiency. Additionally, it discusses the role of multimodal data fusion, where visual data are combined with information from wearable sensors to enhance the precision of nutritional assessment. Ethical and practical dimensions, including informed consent, data ownership, transparency, and fairness, are also examined to promote responsible implementation. Overall, the proposed Edge AI–IoT framework envisions a shift toward dynamic, personalized, and proactive dietary monitoring, offering particular promise for clinical nutrition, vulnerable populations, and elderly care.