<![CDATA[Deaf people rely on hand gestures as their primary means of communication; however, communication barriers often arise when surrounding individuals do not understand sign language. This study presents the design and evaluation of an Internet of Things (IoT)-based smart glove to improve communication accessibility for deaf individuals. The proposed system utilizes multiple MPU6050 motion sensors integrated with an Arduino Nano to detect finger and hand movements. Gesture recognition is implemented using a rule-based approach with predefined threshold values, enabling real-time detection without the need for training data. System performance was evaluated through response time and recognition accuracy measurements, as well as qualitative observations related to system stability and usability. Experimental results show response times ranging from 146–147 ms, indicating a fast and stable system. Recognition accuracy varies between 70% and 85%, depending on gesture complexity and finger movement patterns. Although the accuracy is moderate compared to machine learning-based approaches, the proposed system offers advantages in computational efficiency, simplicity, and ease of implementation. These findings demonstrate the potential of the smart glove as a practical assistive communication device, while also highlighting opportunities for further development through improved gesture modeling and user-centered evaluation.]]>
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