Real-time Hand Research Articles

The Effect of Automated Monitoring and Real-Time Prompting on Nurses’ Hand Hygiene Performance

Adequate hand hygiene compliance by healthcare staff is considered an effective method to reduce hospital-acquired infections. The electronic system developed at Toronto Rehabilitation Institute automatically detects hand hygiene opportunities and records hand hygiene actions. It includes an optional visual hand hygiene status indication, generates real-time hand hygiene prompting signals, and enables automated monitoring of individual and aggregated hand hygiene performance. The system was installed on a complex continuous care unit at the entrance to 17 patient rooms and a utility room. A total of 93 alcohol gel and soap dispensers were instrumented and 14 nurses were provided with the personal wearable electronic monitors. The study included three phases with the system operating in three different modes: (1) an inactive mode during the first phase when hand hygiene opportunities and hand hygiene actions were recorded but prompting and visual indication functions were disabled, (2) only hand hygiene status indicators were enabled during the second phase, and (3) both hand hygiene status and real-time hand hygiene prompting signals were enabled during the third phase. Data collection was performed automatically during all of the three phases. The system indicated significantly higher hand hygiene activity rates and compliance during the third phase, with both hand hygiene indication and real-time prompting functions enabled. To increase the efficacy of the technology, its use was supplemented with individual performance reviews of the automatically collected data.

Real-time hand gesture recognition

Bài báo trình bày một số kết quả nhận dạng cử chỉ của bàn tay người theo thời gian thực sử dụng thông tin thu được từ cảm biến Kinect của hãng Microsoft. Một số kết quả chính của hướng nghiên cứu được trình bày như: kỹ thuật tách vùng bàn tay, nhận dạng tư thế của bàn tay, đề xuất thuật toán hiệu chỉnh kết quả nhận dạng từ chuỗi các tư thế. Kết quả nhận dạng cho độ chính xác khả quan (trên 93%) tạo tiền đề cho các ứng dụng tương tác người máy theo thời gian thực.

Real-Time 3D Hand Tracking from Depth Images

In this paper, we propose a depth image based real-time 3D hand tracking method. Our method is based on the fact that human hand is an end point of human body. Therefore, we locate human hand by finding the end point from a predicted position of hand based on the hand position of the previous frame. We iteratively grow a region around the predicted position. The end point on the major axis of the region which stops moving with region growing is selected as the final position of human hand. Experiments on Microsoft Kinect for Xbox captured sequences show the effectiveness and efficiency of our proposed method.

A Research on Real-Time Hand-Detection Technology Oriented to the Somatic Games

Hand-detection is a key technology to the somatic games. In this paper, we present a real-time hand-detection method based on Adaboost and skin-color characteristic. By processing the video frames with Adaboost classifier, we abstract the target regions which may contain the hand gestures. Then a filter based on skin color is proposed to select the correct regions. The best detection rate reaches above 89% with an acceptable failure rate and misjudgment rate. Experimental results show that this method is a lightweight and rapid approach to implement real-time hand detection in somatic games.

Real-Time Pressure-Measuring System for Evaluating the Depth of Pulse

In order to standardize the pressure/depth against radial artery, the externally-applied-pressure measuring system was fabricated and evaluated. Based on the resistance-variable characteristic of the very thin(10 ㎛) film conductive tape along the pressure of a tip of a examiner"s hand, this system was designed. The change of the pressure was processed through voltage regulator and Matlab S/W, then showed on computer monitor. The signal output through voltage regulator, and Matlab S/W was evaluated on various conditions. The evaluation was executed on these cases; an examiner slowly increases and decreases the pressure, rapidly increases and decreases the pressure, sequentially increases and decreases the pressure, sustains the pressure, micro-changes the pressure. As a pulse examiner varies the pressure on the radial artery of the examinee, the system"s real-time output consistently varies according to the pressure. From the results, it is concluded that this system consistently shows the pressure of the tip of a examiner"s hand in real time without interrupting the evaluation of the radial artery pulse. Therefore this system is expected to standardize the value of the pressure/depth externally applied by an examiner.

A Real Time Hand Gesture Recognition System Based on the Prior Facial Knowledge and SVM

A Real Time Hand Gesture Recognition System Based on the Prior Facial Knowledge and SVM

An optimized real-time hands gesture recognition based interface for individuals with upper-level spinal cord injuries

This paper presents a hand gesture-based interface to facilitate interaction with individuals with upper-level spinal cord injuries, and offers an alternative way to perform "hands-on" laboratory tasks. The presented system consists of four modules: hand detection, tracking, trajectory recognition, and actuated device control. A 3D particle filter framework based on color and depth information is proposed to provide a more efficient solution to the independent face and hands tracking problem. More specifically, an interaction model utilizing spatial and motion information was integrated into the particle filter framework to tackle the "false merge" and "false labeling" problem through hand interaction and occlusion. To obtain an optimal parameter set for the interaction model, a neighborhood search algorithm was employed. An accuracy of 98.81 % was achieved by applying the optimal parameter set to the tracking module of the system. Once the hands were tracked successfully, the acquired gesture trajectories were compared with motion models. The dynamic time warping method was used for signals' time alignment, and they were classified by a CONDENSATION algorithm with a recognition accuracy of 97.5 %. In a validation experiment, the decoded gestures were passed as commands to a mobile service robot and a robotic arm to perform simulated laboratory tasks. Control policies using the gestural control were studied and optimal policies were selected to achieve optimal performance. The computational cost of each system module demonstrated a real-time performance.

Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Natural Interface with Computer using the intelligent approaches is the need of Human Computer Interaction (HCI)applications. In this paper, three techniques were proposed and experimented for interaction with Desktop/Laptop with static hand gesture. All these techniques were using real time approach with different feature descriptors such as Fourier Descriptor(FD),7 Hu moments, Convex Hull and Finger Detection. Real time Recognition efficiency was calculated with respect to recognition time for FD and 7 Hu moments. The 300 samples were trained and stored into database for recognition. For unknown user average recognition time was required 1.7 sec using FD as a feature and 4.6 sec using 7 Hu moments and recognition efficiency was achieved 96% and 98% using 7 Hu and FD respectively. In the second technique New Finger detection algorithm was developed and experimented with Hand tracking system(HTS). In this approach, system was working in dynamic background but gives better result in static background. In the third approach, 3-D Kinect camera was used where hand segmentation was achieved using depth image and finger detection were calculated using Convex Hull. In this approach hand segmentation became easier than first two techniques. With all these approaches feature extraction using 7 Hu and FD can be extend for any other HCI application including sign language recognition. Finger counting algorithm can be combined with other descriptor in complex hand sign as a feature. Currently system is working on static hand gestures, further it will be extended to dynamic hand gesture recognition for Indian sign language interpretation.

Instant 3D design concept generation and visualization by real-time hand gesture recognition

3D conceptual design is a creative process. While the designers are in the inspirational mode, they want to quickly express the design ideas in 3D and visualize it in real time without obstacle. In an exploration of a new, natural and more user friendly interface to assist rapid 3D conceptual generation and visualization, a prototype system of hand gesture and motion capture based user interface has been proposed and implemented. In this paper, we present the framework and components of this real time 3D conceptual generation and visualization system. Hand gestures were designed with the consideration of ease to use and the suitability for real time continuous recognition. Rule-based intelligent menu/icons were designed which changes the prompts according to the design process and expected operations. Real time hand gesture recognition was realized by skeleton model based template matching, and the use of hidden Markov models (HMMs). The recognized hand gestures become command script in OpenSCAD environment, and the 3D design concept was instantly generated and displayed on the screen. A case study was conducted for the initial evaluation of the system.

시간 간격 특징 벡터를 이용한 AdaBoost 기반 제스처 인식

본 논문에서는 키넥트 센서를 이용한 AdaBoost 기반 제스처 인식에 관한 알고리즘을 제안한다. 최근 스마트 TV에 대한 보급으로 관련 산업이 주목받고 있다. 기존 리모컨을 이용하여 TV를 컨트롤 하던 시대에서 벗어나 제스처를 이용하여 TV를 컨트롤 할 수 있는 새로운 접근을 제안한다. AdaBoost 학습 모델에 신체 정규화 된 시간 간격 특징 벡터의 집합을 특징 패턴으로 하여, 속도가 다른 동작들을 인식할 수 있도록 하였다. 또한 속도가 다른 다양한 제스처를 인식하기 위해 다중 AdaBoost 알고리즘을 적용하였다. 제안된 알고리즘을 실제 동영상 플레이어와 연결하여 적용하였고, 실험 후 좌표 변화를 이용한 알고리즘에 비해 정확도가 향상되었음을 확인하였다. The task of 3D gesture recognition for controlling equipments is highly challenging due to the propagation of 3D smart TV recently. In this paper, the AdaBoost algorithm is applied to 3D gesture recognition by using Kinect sensor. By tracking time interval trajectory of hand, wrist and arm by Kinect, AdaBoost algorithm is used to train and classify 3D gesture. Experimental results demonstrate that the proposed method can successfully extract trained gestures from continuous hand, wrist and arm motion in real time.

Virtual pottery: a virtual 3D audiovisual interface using natural hand motions

In this paper, we present our approach towards designing and implementing a virtual 3D sound sculpting interface that creates audiovisual results using hand motions in real time. In the interface "Virtual Pottery," we use the metaphor of pottery creation in order to adopt the natural hand motions to 3D spatial sculpting. Users can create their own pottery pieces by changing the position of their hands in real time, and also generate 3D sound sculptures based on pre-existing rules of music composition. The interface of Virtual Pottery can be categorized by shape design and camera sensing type. This paper describes how we developed the two versions of Virtual Pottery and implemented the technical aspects of the interfaces. Additionally, we investigate the ways of translating hand motions into musical sound. The accuracy of the detection of hand motions is crucial for translating natural hand motions into virtual reality. According to the results of preliminary evaluations, the accuracy of both motion-capture tracking system and portable depth sensing camera is as high as the actual data. We carried out user studies, which took into account information about the two exhibitions along with the various ages of users. Overall, Virtual Pottery serves as a bridge between the virtual environment and traditional art practices, with the consequence that it can lead to the cultivation of the deep potential of virtual musical instruments and future art education programs.

Design and development of data glove based on printed polymeric sensors and Zigbee networks for Human–Computer Interface

Current trends in Human–Computer Interface (HCI) have brought on a wave of new consumer devices that can track the motion of our hands. These devices have enabled more natural interfaces with computer applications. Data gloves are commonly used as input devices, equipped with sensors that detect the movements of hands and communication unit that interfaces those movements with a computer. Unfortunately, the high cost of sensor technology inevitably puts some burden to most general users. In this research, we have proposed a low-cost data glove concept based on printed polymeric sensor to make pressure and bending sensors fabricated by a consumer ink-jet printer. These sensors were realized using a conductive polymer (poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) [PEDOT:PSS]) thin film printed on glossy photo paper. Performance of these sensors can be enhanced by addition of dimethyl sulfoxide (DMSO) into the aqueous dispersion of PEDOT:PSS. The concept of surface resistance was successfully adopted for the design and fabrication of sensors. To demonstrate the printed sensors, we constructed a data glove using such sensors and developed software for real time hand tracking. Wireless networks based on low-cost Zigbee technology were used to transfer data from the glove to a computer. To our knowledge, this is the first report on low cost data glove based on paper pressure sensors. This low cost implementation of both sensors and communication network as proposed in this paper should pave the way toward a widespread implementation of data glove for real-time hand tracking applications.Implications for RehabilitationData gloves can interface the movements of hands to a computer, translating human needs to assistive functions such as controlling of home appliances.We have proposed a low-cost data glove concept based on printed polymeric bending and pressure sensor using consumer ink-jet printer.This low cost implementation of the printed sensors and Zigbee communication network as proposed in this paper should pave the way toward a widespread applications of data glove for real-time hand tracking in healthcare.

A Real Time Hand Gesture Recognition System Based on DFT and SVM

Vision based band gesture recognition provides a more nature and powerful means for human-computer interaction. A fast detection process of hand gesture and an effective feature extraction process are presented. The proposed a hand gesture recognition algorithm comprises four main steps. First use Cam-shift algorithm to track skin color after closing process. Second, in order to extract feature, we use BEA to extract the boundary of the hand. Third, the benefits of Fourier descriptor are invariance to the starting point of the boundary, deformation, and rotation, and therefore transform the starting point of the boundary by Fourier transformation. Finally, outline feature for the nonlinear non-separable type of data was classified by using SVM. Experimental results showed the accuracy is 93.4% in average and demonstrated the feasibility of proposed system.

E_GRASP: Robotic Hand Modeling and Simulation Environment

Modeling and simulation of robotics hands are significant topics that have been looked into by many robotics Specialists and programming experts. This is due to a demand to build a friendly platform for analyzing proposed hand design and movements, earlier to hand physical construction. For meeting such demands, a dexterous robotic hand software simulator was synthesized. The developed code is dexterity characterized robotic hand modeling and simulation software environment. The simulator was developed for robotics hands research purposes. This manuscript is presenting a brief documentation of such a modeling and simulating environment for simulating dynamic movements of multi-finger robotics hand. The environment is named as the e_GRASP. To make use of other supporting environments, e_GRASP is a Mat lab based simulator, with a quite large number of linked functionalities and routines that helps in simulating hand movements in a defined 3D space. e_GRASP was built after a number of years of experience while dealing with robot hands, hence it is a comprehensive Mat lab based Toolbox that makes use of other Mat lab defined Toolboxes. e_GRASP can also be interfaced to real-time hand control, with an ability to be linked with even higher levels of hierarchy. This includes Mat lab AI Tools, optimization, as considered useful toolboxes for dexterous hands for grasping and manipulation.

A Real-Time Hand Pose Recognition Method with Hidden Finger Prediction

ABSTRACT In this paper, we present a real-time hand pose recognition method to provide an intuitive user interface through hand poses or movements without a keyboard and a mouse. For this, the areas of right and left hands are segmented from the depth camera image, and noise removal is performed. Then, the rotation angle and the centroid point of each hand area are calculated. Subsequently, a circle is expanded at regular intervals from a centroid point of the hand to detect joint points and end points of the finger by obtaining the midway points of the hand boundary crossing. Lastly, the matching between the hand information calculated previously and the hand model of previous frame is performed, and the hand model is recognized to update the hand model for the next frame. This method enables users to predict the hidden fingers through the hand model information of the previous frame using temporal coherence in consecutive frames. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 95% and the performance indicated over 32 fps. The proposed method can be used as a contactless input interface in presentation, advertisement, education, and game applications. Keywords : Hand Pose Recognition, Hand-based User Interface, Hand Model

Give spontaneity and self-discovery a chance in ASD: spontaneous peripheral limb variability as a proxy to evoke centrally driven intentional acts

Autism can be conceived as an adaptive biological response to an early unexpected developmental change. Under such conceptualization one could think of emerging biological compensatory mechanisms with unique manifestations in each individual. Within a large group of affected people this would result in a highly heterogeneous spectral disorder where it would be difficult to tap into the hidden potentials of any given individual. A pressing question is how to treat the disorder while harnessing the capabilities and predispositions that the individual has already developed. It would indeed be ideal to use such strengths to accelerate the learning of self-sufficiency and independence, important as the person transitions into adulthood. In this report, we introduce a new concept for therapeutic interventions and basic research in autism. We use visuo-spatial and auditory stimuli to help augment the physical reality of the child and sensory-substitute corrupted kinesthetic information quantified in his/her movement patterns to help the person develop volitional control over the hand motions. We develop a co-adaptive child-computer interface that closes the sensory-motor feedback loops by alerting the child of a cause-effect relationship between the statistics of his/her real-time hand movement patterns and those of external media states. By co-adapting the statistics of the media states and those of the child's real-time hand movements, we found that without any food/token reward the children naturally remained engaged in the task. Even in the absence of practice, the learning gains were retained, transferred and improved 2–4 weeks later. This new concept demonstrates that individuals with autism do have spontaneous sensory-motor adaptive capabilities. When led to their self-discovery, these patterns of spontaneous behavioral variability (SBV) morph into more predictive and reliable intentional actions. These can unlock and enhance exploratory behavior and autonomy in the individual with autism spectrum disorders (ASD).

Vision and Depth Information based Real-time Hand Interface Method Using Finger Joint Estimation

In this paper, we propose a vision and depth information based real-time hand gesture interface method using finger joint estimation. For this, the areas of left and right hands are segmented after mapping of the visual image and depth information image, and labeling and boundary noise removal is performed. Then, the centroid point and rotation angle of each hand area are calculated. Afterwards, a circle is expanded at following pattern from a centroid point of the hand to detect joint points and end points of the finger by obtaining the midway points of the hand boundary crossing and the hand model is recognized. Experimental results that our method enabled fingertip distinction and recognized various hand gestures fast and accurately. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 90% and the performance indicated over 25 fps. The proposed method can be used as a without contacts input interface in HCI control, education, and game applications.

A Neural Network based Real Time Hand Gesture Recognition System

Gesture is habitually used in every day life style. It is so natural way to communicate. Hand gesture recognition method is widely used in the application area of Controlling mouse and/or keyboard functionality, mechanical system, 3D World, Manipulate virtual objects, Navigate in a Virtual Environment, Human/Robot Manipulation and Instruction Communicate at a distance. This paper introduces a real time hand gesture recognition system. This system consists of three stages: image acquisition, feature extraction, and recognition. In the first stage input image of hand gestures are acquiesced by digital camera in approximate frame rate. In second stage a rotation, translation, scaling and orientation invariant feature extraction method has been introduce to extract the feature of the input image based on moment feature extraction method. Finally, a neural network is used to recognize the hand gestures. The performance of the system tested on real data. Based on the experimental results, we noted that this system shows satisfactory performance in hand gesture recognition.

Implementation of an interactive interview system using hand gesture recognition

This paper presents an effective approach to real time hand tracking in dynamic environments of the virtual interactive interview system. Considering simplicity and practical feasibility, we perform hand tracking in the 2D image plane. An interviewer's hand motion recognition algorithm based on the particle filters is applied in this system. The particle filter is well operated for human hand motion recognition more than any other recognition algorithm. Through these experiments, we show that the proposed scheme is stable and works well in virtual interview system's environments.

Novel Haar features for real-time hand gesture recognition using SVM

Due to the effect of lighting and complex background, most visual hand gesture recognition systems work only under restricted environments. Here, we propose a robust system which consists of three modules: digital zoom, adaptive skin detection, and hand gesture recognition. The first module detects user face and zooms in so that the face and upper torus take the central part of the image. The second module utilizes the detected user facial color information to detect the other skin color regions like hands. The last module is the most important part for doing both static and dynamic hand gesture recognition. The region of interest next to the detected user face is for fist/waving hand gesture recognition. To classify the dynamic hand gestures under complex background, motion history image and four groups of novel Haar-like features are investigated to classify the dynamic up, down, left, and right hand gestures. A simple efficient algorithm using Support Vector Machine is developed. These defined hand gestures are intuitive and easy for user to control most home appliances. Five users doing 50 dynamic hand gestures at near, medium, and far distances, respectively, were tested under complex environments. Experimental results showed that the accuracy was 95.37 % on average and the processing speed was 3.93 ms per frame. An application integrated with the developed hand gesture recognition was also given to demonstrate the feasibility of proposed system.