Improving the Recognition of Sign Language from Acquired Data by Wireless Body Area Network

Accurate and fast recognition of sign language would greatly improve communications between the deaf and the hearers using hand-held devices. We used Myo armband as our wireless data measurement device, which is wearable technology equipped with on-board 3D Accelerometer, 3D Gyroscope and 8 channel Electromyogram acquisition system. The main objective of this paper is to provide a lightweight approach for American Sign Language recognition by reducing the dimensionality of inputs using a novel method. Data from each sensors are aggregated into one dimension to reduce the required time for data processing, as well as the amount of required memory for storage. Afterward, we extract features from aggregated data and we proceed to classification using Support Vector Machine (SVM). We compare the performance of SVM with and without our aggregation approach. Our experimental results prove that our proposed approach improves the speed of model derivation (four times faster than existing methods) and reduces the size of input data with the same accuracy.