Highly Robust and Multimodal PVA/Aramid Nanofiber/MXene Organogel Sensors for Advanced Human-Machine Interfaces.

To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.

The authors developed a human-interface system to specify the moving direction by recognition of beckon motion. In this system, the recognition of gesture is performed by using a colour image processing system. The communication between persons are performed by using not only voice but also the gesture. In noisy environment, if machines can recognize the human gesture as a command, one operates the machine easily like making command to another person by his gesture. Then, the authors propose a communication method from human to machines by recognition of human gesture with an image processing system. This system makes communication with machines similar to communication with another person. This system needs no special environment, for example a data glove, data suit or a black background for monochrome image processing. It is necessary only that the operator wear a glove of a colour not found in the background. >

Human gesture recognition and motion representation has become a vital base of current intelligent human-machine interfaces because of ubiquitous and more comfortable interaction. Human-Gesture recognition chiefly deals with recognizing meaningful, expressive body movements involving physical motions of face, head, arms, fingers, hands or body. This review article presents a concise overview of optimal human-gesture and motion representation of medical images. This paper surveys various works undertaken on human gesture design and discusses various design methodologies used for image segmentation and gesture recognition. It further provides a general idea of modeling techniques for analysing hand gesture images and even discusses the diverse techniques involved in motion recognition. This survey provides an insight into various efforts and developments made in the gesture/motion recognition domain through analyzing and reviewing the procedures, datasets, recognition rates and approaches employed for identifying diverse human motions and gestures for supporting better and devising improved applications in near future.

Flexible staircase triboelectric nanogenerator for motion monitoring and gesture recognition

ABSTRACTIn this paper, we propose a method for generating visual interactive art with 3D geometric features using Microsoft Kinect® sensor. Natural human movement and gesture recognition are used to create and interact with various objects in 3D space for art design. The Kinect output coordinates are recorded and transformed into a data structure that accurately represents the captured movement. This method of representing human gestures in a data structure provides an iterative design process that enables and preserves the sequence of human gestures either for future works or for applying transformations on existing structures. The process consists of time-dependent depth data acquisition and joint identification, followed by weighted undirected graph generation by means of a graph scanning algorithm with visual conversion and post processing. The obtained results show that various art forms can be created, ranging from 3D static designs to dynamic installations.

Human motion and gesture recognition receive much concern in sports field, such as physical education and fitness for all. Although plenty of mature applications appear in sports training using photography, video camera, or professional sensing devices, they are either expensive or inconvenient to carry. MEMS devices would be a wise choice for students and ordinary body builders as they are portable and have many built-in sensors. In fact, recognition of hand gestures is discussed in many studies using inertial sensors based on similarity matching. However, this kind of solution is not accurate enough for human movement recognition and cost much time. In this paper, we discuss motion recognition in sports training using features extracted from distance estimation of different kinds of sensors. To deal with the multivariate motion sequence, we propose a solution that applies Max-Correlation and Min-Redundancy strategy to select features extracted with interclass distance similarity estimation. With this method, we are able to screen out proper features that can distinguish motions in different classes effectively. According to the results of experiment in real world application in dance practice, our solution is quite effective with fair accuracy and low time cost.

Gesture recognition for human–machine interaction in table tennis video based on deep semantic understanding

Aramid nanofiber (ANF) is regarded as a fascinating reinforcing filler because of a high miscibility with polymers and stiff nature of p-aramid backbone. In this study, we studied the rheological properties of poly(vinyl alcohol) (PVA)/ANF nanocomposites solutions in dimethyl sulfoxide (DMSO) in terms of the ANF content. Incorporation of ANF significantly resulted in the deterioration of the solution homogeneity of PVA. In the ANF content of 0.5-10.0 wt%, the sudden increase of the viscosity and storage modulus was observed in the vicinity of 3.5 wt%, indicating the rheological percolation of ANF.

We present a new human motion recognition technique for a hands-free user interface. Although many motion recognition technologies for video sequences have been reported, no man-machine interface that recognizes enough variety of motions has been developed. The difficulty was the lack of spatial information that could be acquired from video sequences captured by a normal camera. The proposed system uses a depth image in addition to a normal grayscale image from a time-of-flight camera that measures the depth to objects, so various motions are accurately recognized. The main functions of this system are gesture recognition and posture measurement. The former is performed using the bag-of-words approach. The trajectories of tracked key points around the human body are used as features in this approach. The main technical contribution of the proposed method is the use of 3.5D spatiotemporal trajectory features, which contain horizontal, vertical, time, and depth information. The latter is obtained through face detection and object tracking technology. The proposed user interface is useful and natural because it does not require any contact-type devices, such as a motion sensor controller. The effectiveness of the proposed 3.5D spatiotemporal features was confirmed through a comparative experiment with conventional 3.0D spatiotemporal features. The generality of the system was proven by an experiment with multiple people. The usefulness of the system as a pointing device was also proven by a practical simulation.

The evolution in people and technology over a period of time has resulted in the current day situation where based on the posture of the human being, the probable diseases to which he is prone can be predicted. In present day scenario where applications involving healthcare sectors, military sectors to entertainment sectors, the detection and recognition of human movements and gestures has become more prevalent than before due to its widespread applications. The applicability of posture detection system is realized using the Human Motion Capture (HMC) system. To better understand the features of HMC, In in this paper a general working of a HMC system is given, consecutively, a brief review of literature along with the research methodologies and techniques are provided for each stage constituting the HMC system. The research gap and the future scope of the HMC system is also mentioned with corresponding research challenges.

Research on human motion gesture recognition has been widely used for several technological devices to support monitoring of human-computer interaction, elderly people and so forth. This research area can be observed by conducting experiments for several body movements, such as hand movements, or body movements as a whole. Many methods have been used for human motion gesture recognition in previous studies. This paper attempted to collect data of performance evaluation of support vector machine algorithms for human motion recognition. We developed research methodology that is adapted PRISMA. This methodology is consisted of four main steps for reviewing scientific articles, including identification, screening, eligibility and inclusion criteria. After we obtained result of systematic literature review. We also conducted pilot study of SVM implementation for human gesture recognition. Based on the previous study result, the accuracy performance of vector machine algorithms for body gesture dataset is between 82.88% - 99.92% and hand gesture dataset 88.24% - 95.42%. Based on our pilot experiment, recognition accuracy with the SVM algorithm for human gesture recognition achieved 94,50% (average) accuracy.

Multifunctional poly(vinyl alcohol)/Ag nanofibers-based triboelectric nanogenerator for self-powered MXene/tungsten oxide nanohybrid NO2 gas sensor

A scalable hydrogel processing route to high-strength, foldable clay-based artificial nacre

Aramid nanofibers and poly (vinyl alcohol) nanocomposites for ideal combination of strength and toughness via hydrogen bonding interactions