Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

https://www.mdpi.com/2313-433X/6/8/73/pdf

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Computer vision for assistive technologies

Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures.

/pdf/real-time-hand-gesture-recognition-using-finger-segmentation-2pucfo1z8m.pdf

Real-time hand gesture recognition using finger segmentation.

http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7857/2/Sergio%20Fernandovitch%20Chevtchenko.pdf

A convolutional neural network with feature fusion for real-time hand posture recognition

We improved the performance of a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface based on relatively short task duration and multiclass classification. A custom-built eight-channel fNIRS system was used over the motor cortex areas in both hemispheres to measure the hemodynamic responses evoked by four different motor tasks (overt execution of arm lifting and knee extension for both sides) instead of finger tapping. The hemodynamic responses were classified using the naive Bayes classifier. Among the mean, max, slope, variance, and median of the signal amplitude and the time lag of the signal, several signal features are chosen to obtain highest classification accuracy. Ten runs of threefold cross-validation were conducted, which yielded classification accuracies of 87.1%±2.4% to 95.5%±2.4%, 77.5%±1.9% to 92.4%±3.2%, and 73.8%±3.5% to 91.5%±1.4% for the binary, ternary, and quaternary classifications, respectively. Eight seconds of task duration for obtaining sufficient quaternary classification accuracy was suggested. The bit transfer rate per minute (BPM) based on the quaternary classification accuracy was investigated. A BPM can be achieved from 2.81 to 5.40 bits/min.

https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-19/issue-06/067009/Multiclass-classification-of-hemodynamic-responses-for-performance-improvement-of-functional/10.1117/1.JBO.19.6.067009.pdf

Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain-computer interface.

This paper presents a novel hand gesture system for intelligent human-computer interaction (HCI) and its applications in medical assistance, e.g. intelligent wheelchair control. The hand gesture vocabulary in the system consists of five key hand postures and three compound states, and its design strategy covers the minimal hand motions, distraction detection and user-friendly design. The experiment results show that the designed lexicon is intuitive, ergonomic, and easy to be remembered and performed. The system is tested in both of the indoor and outdoor environments and shows the robustness to lighting change and users' errors. The proposed intelligent HCI system can run in real-time and offers a natural and efficient interface for people with disability in their limbs to communicate with robots.

A Natural Hand Gesture System for Intelligent Human-Computer Interaction and Medical Assistance

This work was supported by the Grants from the National Natural Science Foundation of China (60970062 and 61173116) and the Doctoral Fund of Ministry of Education of China (20110072110014).

https://bura.brunel.ac.uk/bitstream/2438/11641/1/Fulltext.pdf

Automatic Correction of Hand Pointing in Stereoscopic Depth

The paper focuses on a new noninvasive electroencephalographic analysis technique for limb movement analysis and its medical applications such as motor disability assistance, motor rehabilitation, etc. The method combines goal selection with process control strategies in the limb movement decoding and control. The goal selection that provides fine accuracy but limited decodable information due to its susceptibility to noise is widely implemented in the current brain-computer interface (BCI) systems. The process control is mainly employed in the invasive electroencephalographic analysis methods, which offers continuous and diverse motor control information. The proposed method based on noninvasive electroencephalographic decoding provides an efficient way for limb movement analysis, which has a great potential value in neuromotor prosthesis control, motor rehabilitation, and others.

Motor Disability Assistance Based on Novel Noninvasive Electroencephalographic Decoding

This work was supported by the Grants from the National Natural Science Foundation of China (60970062 and 61173116), the Ministry of Finance of China (GY2014G-2), and the Doctoral Fund of Ministry of Education of China (2011- 0072110014).

Jinhua Zeng

Papers

A Natural Hand Gesture System for Intelligent Human-Computer Interaction and Medical Assistance

Automatic Correction of Hand Pointing in Stereoscopic Depth

Motor Disability Assistance Based on Novel Noninvasive Electroencephalographic Decoding

A Natural Hand Gesture System for People with Brachial Plexus Injuries