Wireless Body Sensor Networks for Sign Language Recognition with Real-time Data Analysis
Aymen Shaafi, Osman Salem, Mostafa Gheryani, Ahmed Mehaoua · 2022
To improve the communications between the deaf and the hearers using hand-held devices, we propose a lightweight approach to quickly identify the word in American Sign Language (ASL). We acquire inertial data and muscular activity during hands movements. Then we aggregate the received data to reduce the required processing complexity and memory usage in a portable device. Afterward, we feed extracted features from aggregated data into the Support Vector Machine (SVM) to identify the associated word. Our experimental results showed that our data aggregation approach was able to enhance the recognition accuracy of the associated word when comparing the performance of SVM and Decision Tree (DT) classifiers with and without data aggregation. We conduct a performance analysis and we showed that our proposed approach is faster and able to achieve better recognition accuracy (92%) when compared with existing work.