State-of-the-Art in Data Gloves: A Review of Hardware, Algorithms, and Applications

Pan, Mingzhang; Tang, Yingzhe; Li, Hongqi

doi:10.1109/tim.2023.3243614

Cited by 18 publications

(13 citation statements)

References 123 publications

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“…Looking at our data, we also noticed that the value of these two joints is often exactly the same, explaining why one of these two joints for each finger was discarded by our feature selection so often. Anatomically, it is not possible to move the upper phalanx (= DIP) independently of the middle phalanx (= PIP) without external influence [ 9 ]. This dependence explains why so many values are filtered out here.…”

Section: Resultsmentioning

confidence: 99%

“…A 9-degrees of freedom (DoF) inertial measurement unit (IMU) and a 2D flex sensor are attached to each finger to obtain reliable values about the flexion/stretch of the fingers and the spread between each finger. The latter information is not available in some data gloves, but is indispensable for distinguishing individual hand shapes such as R , U , and V (see Figure 3 ) [ 9 ]. A 6-DoF IMU is attached to the back of the hand to obtain its orientation.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Some features may be more important than others. For example, the distal interphalangeal (DIP) and proximal interphalangeal (PIP) joints, shown in Figure 6 , are interdependent in most of the gestures that were investigated here [ 9 ]. Consequently, only one of these features holds significant information about the performed gesture.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Between 2015 and 2022, more than 100 papers were published in English on this topic in reputable sources, like the Institute of Electrical and Electronics Engineers (IEEE) or the Association for Computing Machinery (ACM), according to the Web of Science (WoS). More than 70% of these examine static gestures [ 9 ].…”

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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Section: Resultsmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

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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“…These frameworks are highly accurate but represent several drawbacks. Wearable sensors such as gloves require carrying a load of cables that connect the device to a computer while per-forming the gesture [4]. This makes the system impractical and can cause discomfort for users.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Korti

Slimane

Lakhdari

2023

Int. j. electr. comput. eng. syst. (Online)

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Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting.

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