Abstract
The Internet of Things (IoT) permeates all aspects of human existence shortly. As a result of the IoT, it can now construct a smart world. For this to happen, however, extracting meaningful information from raw sensory input functioning in loud and complicated settings must be addressed to achieve it. For example, bandwidth, processing power, and power consumption must be addressed while building a possible IoT system. Due to the current epidemic, the need for contactless solutions has risen. Possible solutions include a gesture-based control system that protects user privacy and can operate several different appliances simultaneously. When implementing such gesture-based control systems, opaque box artificial intelligence (AI) models are used. This opaque box AI model has shown good performance metrics on in-distribution data when tested in a lab. However, their complexity and opaqueness make them prone to failure when exposed to real-world out-of-distribution input. In contrast to opaque box models, explainable AI models based on fuzzy logic (EAI-FL) demonstrate comparable performance on lab data distributions. The type-2 fuzzy models, on the other hand, are readily calibrated and modified to offer equivalent performance to those attained on the lab in-distribution data in the real world.