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Hand signals are the most widely used, feasible, and device-free communication method in manufacturing plants, airport ramps, and other noisy or voice-prohibiting environments. Enabling IoT agents, such as robots, to recognize and communicate by hand signals will facilitate human-machine collaboration for the emerging “Industry 5.0.” While many prior works succeed in hand signal recognition, few can rigorously guarantee the accuracy of their predictions. This project proposes a method that builds on the theory of conformal prediction (CP) to provide statistical guarantees on hand signal recognition accuracy and, based on it, measure the uncertainty in this communication process. It utilizes a calibration set with a few representative samples to ensure that trained models provide a conformal prediction set that reaches or exceeds the truth worth and trustworthiness at a user-specified level. Subsequently, the uncertainty in the recognition process can be detected by measuring the length of the conformal prediction set. Furthermore, the proposed CP-based method can be used with IoT models without fine-tuning as an out-of-the-box and promising lightweight approach to modeling uncertainty. Our experiments show that the proposed conformal recognition method can achieve accurate hand signal prediction in novel scenarios. When selecting an error level α = 0.10, it provided 100% accuracy for out-of-distribution test sets.