Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition

Author:

Achenbach Philipp1ORCID,Laux Sebastian1ORCID,Purdack Dennis1ORCID,Müller Philipp Niklas1ORCID,Göbel Stefan1ORCID

Affiliation:

1. Serious Games Group, Technical University of Darmstadt, 64289 Darmstadt, Germany

Abstract

Human-to-human communication via the computer is mainly carried out 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 hearing loss). Data gloves help to increase immersion within VR, as they correspond to our natural interaction. At the same time, they offer the possibility of accurately capturing 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 created new artificial data from the recorded and filtered data to augment the training data set. 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. The voting meta-classifier (VL2) proved 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 was proven to be an effective approach, especially in improving the classification time. Overall, we have shown that our hand-shape recognition system using data gloves is suitable for communication within VR.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference35 articles.

1. World Health Organization (2021). Deafness and Hearing Loss, World Health Organization. Technical Report.

2. Stokoe, W.C., Casterline, D.C., and Croneberg, C.G. (1976). A Dictionary of American Sign Language on Linguistic Principles, Linstok Press.

3. Stokoe, W. (1960). Sign Language Structure. 1978, Linstok Press.

4. Achenbach, P., Göksu, Y., Kullmann, T., Tregel, T., and Göbel, S. (2021, January 1–2). Towards handshape identification for automatic gesture recognition using sign notation systems. Proceedings of the 8th European Conference on Social Media (ECSM ’21), Online.

5. The ASL-LEX 2.0 Project: A Database of Lexical and Phonological Properties for 2,723 Signs in American Sign Language;Sehyr;J. Deaf Stud. Deaf Educ.,2021

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