In the evolving landscape of Machine Learning (ML) and Deep Learning (DL), there is an increasing emphasis on developing models that efficiently balance performance and computational constraints, rather than focusing exclusively on accuracy. This work investigates efficient ML and DL models for detecting the presence of hands and other objects using low-resolution infrared (IR) sensors, which represent a cost-effective, low-power, and privacy preserving alternative to higher resolution cameras. To support this study, a specialized dataset was collected, containing images of hands, objects, hands with objects, and empty backgrounds. The dataset includes a variety of objects with different sizes and temperatures, providing a comprehensive foundation for robust model training and evaluation across diverse detection scenarios.