Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1D CNN - Al Mudawi, Naif; Ansar, Hira; Alazeb, Abdulwahab; Aljuaid, Hanan; AlQahtani, Yahay; Algarni, Asaad; Jalal, Ahmad; Liu, Hui (Frontiers in Bioengineering and Biotechnology, 2024)
Introduction: Hand gestures are an effective communication tool that may convey a wealth of information in a variety of sectors, including medical and education. E-learning has grown significantly in the last several years and is now an essential resource for many businesses. Still, there hasn't been much research done on the use of hand gestures in e-learning. Similar to this, gestures are frequently used by medical professionals to help with diagnosis and treatment. Method: Our goal in this work is to improve the way instructors, students, and medical professionals receive information by introducing a dynamic method to hand gesture monitoring and recognition. Six modules make up our approach: video-to-frame conversion, pre-processing for quality enhancement, hand skeleton mapping with Single-Shot Multibox Detector (SSMD) tracking, hand detection using background modeling and convolutional neural network (CNN) bounding box technique, feature extraction using point-based and full-hand coverage techniques, and optimization using a populationbased incremental learning algorithm. Next, a 1D CNN classifier is used to identify hand motions. Results: After a lot of trial and error, we were able to obtain hand tracking accuracy of 83.71% and 85.71% over the ISL and WLASL datasets, respectively. Our findings show how well our method works to recognize hand motions. Discussion: Teachers, students, and medical professionals can all efficiently transmit and comprehend information by utilizing our suggested system. The obtained accuracy rates highlight how our method might improve communication and make information exchange easier in various domains.