Gesture recognition using machine-learning methods is valuable in the development of advanced cybernetics, robotics and healthcare systems, and typically relies on images or videos. To improve recognition accuracy, such visual data can be combined with data from other sensors, but this approach, which is termed data fusion, is limited by the quality of the sensor data and the incompatibility of the datasets. Here, we report a bioinspired data fusion architecture that can perform human gesture recognition by integrating visual data with somatosensory data from skin-like stretchable strain sensors made from single-walled carbon nanotubes. The learning architecture uses a convolutional neural network for visual processing and then implements a sparse neural network for sensor data fusion and recognition at the feature level. Our approach can achieve a recognition accuracy of 100% and maintain recognition accuracy in non-ideal conditions where images are noisy and under- or over-exposed. We also show that our architecture can be used for robot navigation via hand gestures, with an error of 1.7% under normal illumination and 3.3% in the dark. A bioinspired machine-learning architecture can combine visual data with data from stretchable strain sensors to achieve human gesture recognition with high accuracy in complex environments.

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Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors

Recent methods and databases in vision-based hand gesture recognition

Space-time representation of people based on 3D skeletal data

Hand gesture is one of the most prominent ways of communication since the beginning of the human era. Hand gesture recognition extends human-computer interaction (HCI) more convenient and flexible. Therefore, it is important to identify each character correctly for calm and error-free HCI. Literature survey reveals that most of the existing hand gesture recognition (HGR) systems have considered only a few simple discriminating gestures for recognition performance. This paper applies deep learning-based convolutional neural networks (CNNs) for robust modeling of static signs in the context of sign language recognition. In this work, CNN is employed for HGR where both alphabets and numerals of ASL are considered simultaneously. The pros and cons of CNNs used for HGR are also highlighted. The CNN architecture is based on modified AlexNet and modified VGG16 models for classification. Modified pre-trained AlexNet and modified pre-trained VGG16 based architectures are used for feature extraction followed by a multiclass support vector machine (SVM) classifier. The results are evaluated based on different layer features for best recognition performance. To examine the accuracy of the HGR schemes, both the leave-one-subject-out and a random 70–30 form of cross-validation approach were adopted. This work also highlights the recognition accuracy of each character, and their similarities with identical gestures. The experiments are performed in a simple CPU system instead of high-end GPU systems to demonstrate the cost-effectiveness of this work. The proposed system has achieved a recognition accuracy of 99.82%, which is better than some of the state-of-art methods.

CNN based feature extraction and classification for sign language

Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.

Hand posture and gesture recognition techniques for virtual reality applications: a survey

Video‐based hand gesture recognition plays an important role in human‐computer interaction (HCI). Recent advanced methods usually add 3D convolutional neural networks to capture the information from both spatial and temporal dimensions. However, these methods suffer the issue of requiring large‐scale training data and high computational complexity. To address this issue, we proposed an attentive feature fusion framework for efficient hand‐gesture recognition. In our proposed model, we utilize a shallow two‐stream CNNs to capture the low‐level features from the original video frame and its corresponding optical flow. Following, we designed an attentive feature fusion module to selectively combine useful information from the previous two streams based on the attention mechanism. Finally, we obtain a compact embedding of a video by concatenating features from several short segments. To evaluate the effectiveness of our proposed framework, we train and test our method on a large‐scale video‐based hand gesture recognition dataset, Jester. Experimental results demonstrate that our approach obtains very competitive performance on the Jester dataset with a classification accuracy of 95.77%.

Hand gesture recognition based on attentive feature fusion

Feature learning based on SAE–PCA network for human gesture recognition in RGBD images

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Hand gesture recognition based on attentive feature fusion

Feature learning based on SAE–PCA network for human gesture recognition in RGBD images