Achenbach, P.; Laux, S.; Purdack, D.; Müller, P.N.; Göbel, S. Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition. Sensors2023, 23, 9847.
Achenbach, P.; Laux, S.; Purdack, D.; Müller, P.N.; Göbel, S. Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition. Sensors 2023, 23, 9847.
Achenbach, P.; Laux, S.; Purdack, D.; Müller, P.N.; Göbel, S. Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition. Sensors2023, 23, 9847.
Achenbach, P.; Laux, S.; Purdack, D.; Müller, P.N.; Göbel, S. Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition. Sensors 2023, 23, 9847.
Abstract
Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.
Keywords
machine learning: classification; support vector machines; random forest classifier; outlier detection; feature selection; data augmentation; hand shape recognition; sign language; virtual reality
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
