Recognition and Classification of Dynamic Hand Gestures by a Wearable Data-Glove

Pezzuoli, Francesco; Corona, Dario; Corradini, Maria Letizia

doi:10.1007/s42979-020-00396-5

Cited by 17 publications

(10 citation statements)

References 37 publications

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“…Gestures that remain the same for a given time frame are called static gestures while dynamic gestures keep changing in a given time frame, such as, waving. Some common approaches for hand gesture recognition include the use of electromyography (EMG) [13], cameras [14], and wearable gloves [15]. Asif et al [16] used a convolutional neural network (CNN) to detect and recognize hand gestures.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Sign Language Recognition System for E-Learning Context

Hussain¹,

Shaoor²,

Alsuhibany³

et al. 2022

Computers, Materials &Amp; Continua

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In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASLalphabet (American Sign Language) and ISL-HS (Irish Sign Language) sign language datasets. Different machine learning classifiers including random forest, decision tree, and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been compared. Since the random forest classifier performed better, it has been selected as the base classifier for the proposed system. It showed 96.7% accuracy with ISL-HS and 93.7% accuracy with ASL-alphabet dataset using the extracted features.

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Section: Related Workmentioning

confidence: 99%

Intelligent Sign Language Recognition System for E-Learning Context

Hussain¹,

Shaoor²,

Alsuhibany³

et al. 2022

Computers, Materials &Amp; Continua

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“…The more features are present, the more information is available for the classifier to successfully recognize the hand gesture. Therefore, many of these papers ([ 10 , 11 , 12 ]) not only use hand-shape information for classification, but also add hand orientation as an additional feature.…”

Section: Introductionmentioning

confidence: 99%

“…A distinction is also made between static and dynamic gestures: Static gestures possess spatial information, like the already-mentioned hand shape, the orientation of the hand, or the location of the hand where the gesture is performed. Dynamic gestures additionally possess temporal information, such as the movement of the hand [ 12 ], the rotation of the ulnar, or a change in finger pose (e.g., closed fingers that are spread) [ 5 ]. Therefore, some of the papers use dynamic gestures instead of static ones [ 10 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

Achenbach,

Laux,

Purdack

et al. 2023

Sensors

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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.

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“…To demonstrate the capabilities of the glove, we perform rolling time-series classification of 24 letters and words from American Sign Language (ASL) [8]. This vocabulary has been explored for wearable systems by many past research endeavors, such as [9]- [11]. It offers impactful applications such as improving communication between people or inspiring alternative human-computer interfaces, especially involving deaf participants or challenging auditory environments.…”

Section: Introductionmentioning

confidence: 99%

A Wearable Smart Glove and Its Application of Pose and Gesture Detection to Sign Language Classification

DelPreto

Hughes

D'Aria

et al. 2022

IEEE Robot. Autom. Lett.

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Advances in soft sensors coupled with machine learning are enabling increasingly capable wearable systems. Since hand motion in particular can convey useful information for developing intuitive interfaces, glove-based systems can have a significant impact on many application areas. A key remaining challenge for wearables is to capture, process, and analyze data from the high-degree-of-freedom hand in real time.We propose using a commercially available conductive knit to create an unobtrusive network of resistive sensors that spans all hand joints, coupling this with an accelerometer, and deploying machine learning on a low-profile microcontroller to process and classify data. This yields a self-contained wearable device with rich sensing capabilities for hand pose and orientation, low fabrication time, and embedded activity prediction.To demonstrate its capabilities, we use it to detect static poses and dynamic gestures from American Sign Language (ASL). By pre-training a long short-term memory (LSTM) neural network and using tools to deploy it in an embedded context, the glove and an ST microcontroller can classify 12 ASL letters and 12 ASL words in real time. Using a leave-one-experiment-out cross validation methodology, networks successfully classify 96.3% of segmented examples and generate correct rolling predictions during 92.8% of real-time streaming trials.

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Recognition and Classification of Dynamic Hand Gestures by a Wearable Data-Glove

Cited by 17 publications

References 37 publications

Intelligent Sign Language Recognition System for E-Learning Context

Intelligent Sign Language Recognition System for E-Learning Context

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

A Wearable Smart Glove and Its Application of Pose and Gesture Detection to Sign Language Classification

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