k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1096
• Main Authors: Barioul, Rim, Kanoun, Olfa
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.01.2023; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bias; Classification; Electromyography; Embedded systems; extreme learning machine; Feature selection; force myography; Gesture; Gestures; grasshopper optimization algorithm; Hand; Humans; Identification and classification; k-tournament selection; Linear algebra; Machine learning; Machine vision; Mechanical Phenomena; Myography; Network management systems; Neural networks; Neural Networks, Computer; Optimization; Physiology; Sensors; Sign language; Germany; grasshopper optimization algorithm; k-tournament selection; extreme learning machine; force myography
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23031096

The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.