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Objectives: Although the Microsoft Kinect has compelling potential for gait analysis in medicine, data available to compare it with observational gait analysis (OGA) is scarce. This study compared the Microsoft Kinect and the OGA in assessing the gait parameters of apparently healthy adults. Methods: Ninety-seven apparently healthy young male adults participated in this comparative study. First, the participant’s age, height, weight, and body mass index were obtained. Afterward, gait parameters involving the number of steps, cadence, stride length, and step length were assessed concurrently following OGA standard procedures and the Microsoft Kinect during a 6-m walk down the hallway. The obtained data were analyzed using descriptive and inferential statistics. The significance level was set at P

Airports are a fundamental node in the aviation system. The growth of this sector undergoes the evolution of the airports procedures and infrastructures. Air traffic control is considered to be one of the most important activity performed in an airport. This often involves numerous personnel, construction and maintenance costs etc. With the ongoing digitalisation process in various fields, the concept of remote and virtual control towers (RVT) has emerged owing to the innovation in this field. Technologies such as augmented reality (AR) have successfully paved their smooth way to bring in improvements to an RVT. The current work aims to enhance and improve the controller work in an RVT exploiting the adaption of virtual reality and AR systems. In particular, this concept is under investigation within the EU funded SESAR project: RETINA (Resilient Synthetic Vision for Advanced Control Tower Air Navigation Service Provision) and DTT (Digital Technologies for Tower). AR content generation by virtue of various display technologies are studied and, Optical Spatial See-through displays (O SSTDs) have been considered for its own advantages in developing this application. One of the necessary requirements of AR in this configuration is identified as the tracking of the operator's point of view (eye-tracking), to provide virtual content consistent with its real position. Thus, this paper elaborates the design of specific eye-tracking system using Microsoft Kinect V2 for the virtual control tower application. The need to have binocular vision to use AR content is assessed and the interface has been tested with few subjects to evaluate the precision of the measurements detected through the proposed solution. [ABSTRACT FROM AUTHOR]

Objectives: Although the Microsoft Kinect has compelling potential for gait analysis in medicine, data available to compare it with observational gait analysis (OGA) is scarce. This study compared the Microsoft Kinect and the OGA in assessing the gait parameters of apparently healthy adults. Methods: Ninety-seven apparently healthy young male adults participated in this comparative study. First, the participant's age, height, weight, and body mass index were obtained. Afterward, gait parameters involving the number of steps, cadence, stride length, and step length were assessed concurrently following OGA standard procedures and the Microsoft Kinect during a 6-m walk down the hallway. The obtained data were analyzed using descriptive and inferential statistics. The significance level was set at P<0.05. Results: The Mean±SD walk time, steps, cadence, velocity, and stride length were 8.07±1.39 s, 14.0±2.96 counts, 72.9±11.9 steps/min, 0.8±0.13 m/s, and 0.77±0.13m, respectively. Step length was significantly higher (P<0.05) with Microsoft Kinect than OGA, whereas stride length and walk speed values were significantly (P<0.05) lower with Microsoft Kinect. A moderate but significant (P=0.001) positive correlation existed between Microsoft Kinect and OGA regarding walk speed. In contrast, regarding the step length, a weak but significant (P<0.05) positive correlation was found between Microsoft Kinect and OGA. Discussion: Step length values of Microsoft Kinect were significantly higher than OGA values, whereas stride length and walk speed values of Microsoft Kinect were significantly lower than OGA values. Walk speed and step length measured by Microsoft Kinect and OGA were positively correlated. [ABSTRACT FROM AUTHOR]

Many events, such as robberies, missing people, and other suspicious activities, are often captured by cameras. However, these videos are, more often than not, short and not from an optimal angle. Biometric recognition techniques such as facial or iris recognition are inadequate for situations like this. Gait recognition techniques are more suitable than other types of biometrics in such situations. In this work, we propose a new gait recognition technique using viewpoint normalization and a sliding window process. The proposed technique is designed to handle short walking videos captured from any angle. The proposed technique consists of 3 steps. First, a 2-second walk is preprocessed using the sliding window process. This step allows us to generate more gait data in a form of a set of sliding windows from only a 2-second walk. Then sliding windows are transformed into the optimal viewpoint using $\mathit {ViewNet}$ , a proposed neural network designed for finding and transforming sliding windows into the optimal viewpoint angle. Finally, local joint movement information is extracted from sliding windows and used to identify a person using $\mathit {IdenNet}$ , a proposed neural network designed for identifying a person from local joint movements. Four evaluation methods, the top $k$ accuracy test, the precision-recall curves, the cumulative matching characteristic curves, and the gallery-size test, are used to assess the proposed technique. The experimental results show that the proposed technique outperforms existing techniques on all four tests. In particular, the proposed technique can provide a small group, not more than 5, of suspects with a chance above 90% that the real person of interest is in the group. Moreover, the proposed technique still maintains high accuracy even when used with a larger pool of people.

Background: Knee kinematic parameters during a drop vertical jump (DVJ) have been demonstrated to be associated with increased risk of noncontact anterior cruciate ligament (ACL) injury. However, standard motion analysis systems are not practical for routine screening. Affordable and practical motion sensor alternatives exist but require further validation in the context of ACL injury risk assessment. Purpose/Hypothesis: To prospectively study DVJ parameters as predictors of noncontact ACL injury in collegiate athletes using an affordable motion capture system (Kinect; Microsoft). We hypothesized that athletes who sustained noncontact ACL injury would have larger initial and peak contact coronal abduction angles and smaller peak flexion angles at the knee during a DVJ. Study Design: Case-control study; Level of evidence, 3. Methods: 102 participants were prospectively recruited from a collegiate varsity sports program. A total of 101 of the 102 athletes (99%) were followed for an entire season for noncontact ACL injury. Each athlete performed 3 DVJs, and the data were recorded using the motion capture system. Initial coronal, peak coronal, and peak sagittal angles of the knee were identified by our software. Results: Five of the 101 athletes sustained a noncontact ACL injury. Peak coronal angles were significantly greater and peak sagittal flexion angles were significantly smaller in ACL-injured athletes (P =.049, P =.049, respectively). Receiver operating characteristic (ROC) analysis demonstrated an area under the curve of 0.88, 0.92, and 0.90 for initial coronal, peak coronal, and peak sagittal angle, respectively. An initial coronal angle cutoff of 2.96° demonstrated 80% sensitivity and 72% specificity, a peak coronal angle cutoff of 6.16° demonstrated 80% sensitivity and 72% specificity, and a peak sagittal flexion cutoff of 93.82° demonstrated 80% sensitivity and 74% specificity on the study cohort. Conclusion: Increased peak coronal angle and decreased peak sagittal angle during a DVJ were significantly associated with increased risk for noncontact ACL injury. Based on ROC analysis, initial coronal angle showed good prognostic ability, whereas peak coronal angle and peak sagittal flexion provided excellent prognostic ability. Affordable motion capture systems show promise as cost-effective and practical options for large-scale ACL injury risk screening. [ABSTRACT FROM AUTHOR]

Social distancing is one of the most important ways to prevent many diseases, especially the respiratory system, where the latest internationally spread is coronavirus disease, and it will not be the last. The spreading of this pandemic has become a major threat to human life, especially to the elderly and people suffering from chronic diseases. During the Corona pandemic, medical authorities were keen to control the spread through social distancing and monitoring it in markets, universities, and schools. This monitoring was mostly used to estimate the distance with the naked eye and interfere with estimating the distance on the observer only. In this study, a computer application was designed to monitor social distancing in closed areas, especially in schools and kindergartens, using a fast, effective and unobtrusive technique for children. In addition to this system, we use augmented reality to help to determine the location of violation of social distancing. This system was tested, and the results were accurate exceeding 98.5%. [ABSTRACT FROM AUTHOR]

Identification of human activity with decent precision is a challenging task in the field of computer vision, especially when applying for surveillance purpose. A rule-based classifier method is proposed in this paper, which is capable of recognizing a view-invariant multiple human activity recognition in real time. A single Kinect sensor is used for the input of RGB-D data in real time. Initially, a skeleton-tracking algorithm is applied. After tracking the skeletons, activities are recognized from each individually tracked skeleton independently. Different rules are defined to recognize discrete skeleton positions and classify a particular order of multiple postures into activities. During the experimentation, we examine about 14 activities and found that the proposed method is robust and efficient concerning multiple views, scaling and phase variation activities during different realistic acts. A self-generated dataset in the controlled environment is used for the experiment. About 2 min of data was collected. Data from two different males were collected for multiple human activities. Experimental results show that the proposed method is flexible and efficient for multiple view activities as well as scale and phase variation activities. It provides a detection accuracy of 98%. [ABSTRACT FROM AUTHOR]

Recent world events have caused a dramatic rise in the use of video conferencing solutions such as Zoom and FaceTime. Although 3D capture and display technologies are becoming common in consumer products (e.g., Apple iPhone TrueDepth sensors, Microsoft Kinect devices, and Meta Quest VR headsets), 3D telecommunication has not yet seen any appreciable adoption. Researchers have made great progress in developing advanced 3D telepresence systems, but often with burdensome hardware and network requirements. In this work, we present HoloKinect, an open-source, user-friendly, and GPU-accelerated platform for enabling live, two-way 3D video conferencing on commodity hardware and a standard broadband internet connection. A Microsoft Azure Kinect serves as the capture device and a Looking Glass Portrait multiscopically displays the final reconstructed 3D mesh for a hologram-like effect. HoloKinect packs color and depth information into a single video stream, leveraging multiwavelength depth (MWD) encoding to store depth maps in standard RGB video frames. The video stream is compressed with highly optimized and hardware-accelerated video codecs such as H.264. A search of the depth and video encoding parameter space was performed to analyze the quantitative and qualitative losses resulting from HoloKinect's lossy compression scheme. Visual results were acceptable at all tested bitrates (3–30 Mbps), while the best results were achieved with higher video bitrates and full 4:4:4 chroma sampling. RMSE values of the recovered depth measurements were low across all settings permutations. [ABSTRACT FROM AUTHOR]

The present article demonstrates the process of designing, developing, implementing, and evaluating an active video game or exergame. The main goal of our proposed exergame is to develop the simple reaction time of players. The main target group are simple users; however, it can work on tennis specialists. Herein, we used the exergame to investigate the hypothesis that players can improve their reaction time through practice. To achieve this, players' simple reaction time was measured at the start of the game. Then, the players took part in a 4-week training session. At the end of the training session, the simple reaction time of players was measured again and a questionnaire was completed. Another goal of this paper was to investigate the difference between perceived usefulness (general usability, usefulness, and user interface satisfaction) of experts and non-experts (sports science students and computer science students) and pro-gamers and casual gamers. The findings were encouraging. The majority of the players reported that the gaming experience was very satisfying and the game was easy to use and learn. Moreover, after the analysis, it was discovered that this game can significantly improve the simple reaction time of all players. This improvement was independent from the players' background. [ABSTRACT FROM AUTHOR]

This paper describes the development of a Lévy flights-based ViBe algorithm for foreground detection. It is based on a novel approach, using a particular class of the generalized random walk known as Lévy flights, to improve the spatial update mechanism. This mechanism originally used the uniform probability distribution, and it is responsible for handling the new objects that appear in the scene. The proposed approach speeds up the inclusion process of ghost regions in the background model and makes it faster than the inclusion of real static foreground objects while maintaining the classification performance. The developed detection algorithm was evaluated using inclusion speed and classification tests, the results showing the efficacy of using Lévy flights with ViBe’s updating mechanism. Experimental tests were also undertaken on the proposed algorithm to validate its ability with real images, obtained through a series of experiments performed using a multi-spectral Kinect laser imaging sensor, and also from a public dataset. The experimental results show the high adaptation capability of this algorithm against the background modification and validate its ability to deal with multi-spectral real images. The developed algorithm achieved a better performance in comparison with traditional ViBe algorithms when extracting background and detecting foreground objects. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has transformed daily life, as individuals must reduce contacts among each other to prevent the spread of the disease. Consequently, patients’ access to outpatient rehabilitation care was curtailed and their prospect for recovery has been compromised. Telerehabilitation has the potential to provide these patients with equally efficacious therapy in their homes. Using commercial gaming devices with embedded motion sensors, data on movement can be collected toward objective assessment of motor performance, followed by training and documentation of progress. Herein, we present a low-cost telerehabilitation system dedicated to bimanual exercise, wherein the healthy arm drives movements of the affected arm. In the proposed setting, a patient manipulates a dowel embedded with a sensor in front of a Microsoft Kinect sensor. In order to provide an engaging environment for the exercise, the dowel is interfaced with a personal computer, to serve as a controller. The patient’s gestures are translated into actions in a custom-made citizen-science project. Along with the system, we introduce an algorithm for classification of the bimanual movements, whose inner workings are detailed in terms of the procedures performed for dimensionality reduction, feature extraction, and movement classification. We demonstrate the feasibility of our system on eight healthy subjects, offering support to the validity of the algorithm. These preliminary findings set forth the development of precise motion analysis algorithms in affordable home-based rehabilitation. [ABSTRACT FROM AUTHOR]

Introduction: Clinicians typically observe and describe abnormal head postures (AHPs) and may also measure them. Depth cameras have been suggested as a reliable measurement device for measuring head position using face-tracking technology. This study compared a depth camera (Microsoft Kinect) to a gold standard electromagnetic tracking system (Polhemus device) to measure head position. Method: Twenty healthy volunteers (mean age 21 years) had their head position simultaneously recorded using the depth camera (Kinect) and the electromagnetic tracking system (Polhemus). Participants were asked to make 30-degree head movements into chin up, chin down, head turn and head tilt positions. The head movement made and the stability of the head at each position were recorded and analysed. Results: Compared to the electromagnetic tracking system (Polhemus), the depth camera (Kinect) always measured a smaller head movement. Measurements with the two devices were not statistically significantly different for turn right (P = 0.3955, p > 0.05), turn left (P = 0.4749, p > 0.05), tilt right (P = 0.7086, p > 0.05) and tilt left (P = 0.4091, p > 0.05) head movements. However, the smaller depth camera measurement of chin up and chin down head movements were statistically significant, chin up (P = 0.0001, p < 0.01) and chin down (P = 0.0005, p < 0.001). At each eccentric position, the depth camera (Kinect) recordings were more variable than the electromagnetic tracking system (Polhemus). Conclusions: Compared to the electromagnetic tracking system (Polhemus), the depth camera (Kinect) was comparable for measuring head turns and tilts but was less accurate at measuring chin up and chin down head positions. Further research is needed before the depth cameras are considered for clinical recordings of head position.

The output of Microsoft Kinect is a multimodal signal, which provides RGB videos, depth sequences and skeleton information at the same time, opening up a new opportunity for the research of human action recognition. However, for different single modalities of the signals, how to exploit and fuse useful features of these various sources remains a very challenging problem. Most of the methods based on RGB-D action recognition simply fuse the multimodal features, ignoring the potential semantic relationship between different models. In this paper, we propose a multi-modal action recognition model based on Bilinear Pooling and Attention Network (BPAN), which could effectively fuse multi-modal for RGB-D action recognition. Firstly, we adopt the efficient data preprocessing methods for RGB and skeleton data. Then, we propose a multimodal fusion network combining RGB video and skeleton sequences. The proposed BPAN module could effectively compress the features of RGB and skeleton, and project them into latent subspace to get the fusion features. In the end, a fully connected three-layer perceptron is adopted to obtain the final classification decision. Experimental results on three public datasets demonstrate that our proposed method leads to a more favorable performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recent world events have caused a dramatic rise in the use of video conferencing solutions such as Zoom and FaceTime. Although 3D capture and display technologies are becoming common in consumer products (e.g., Apple iPhone TrueDepth sensors, Microsoft Kinect devices, and Meta Quest VR headsets), 3D telecommunication has not yet seen any appreciable adoption. Researchers have made great progress in developing advanced 3D telepresence systems, but often with burdensome hardware and network requirements. In this work, we present HoloKinect, an open-source, user-friendly, and GPU-accelerated platform for enabling live, two-way 3D video conferencing on commodity hardware and a standard broadband internet connection. A Microsoft Azure Kinect serves as the capture device and a Looking Glass Portrait multiscopically displays the final reconstructed 3D mesh for a hologram-like effect. HoloKinect packs color and depth information into a single video stream, leveraging multiwavelength depth (MWD) encoding to store depth maps in standard RGB video frames. The video stream is compressed with highly optimized and hardware-accelerated video codecs such as H.264. A search of the depth and video encoding parameter space was performed to analyze the quantitative and qualitative losses resulting from HoloKinect’s lossy compression scheme. Visual results were acceptable at all tested bitrates (3–30 Mbps), while the best results were achieved with higher video bitrates and full 4:4:4 chroma sampling. RMSE values of the recovered depth measurements were low across all settings permutations.

The present article demonstrates the process of designing, developing, implementing, and evaluating an active video game or exergame. The main goal of our proposed exergame is to develop the simple reaction time of players. The main target group are simple users; however, it can work on tennis specialists. Herein, we used the exergame to investigate the hypothesis that players can improve their reaction time through practice. To achieve this, players’ simple reaction time was measured at the start of the game. Then, the players took part in a 4-week training session. At the end of the training session, the simple reaction time of players was measured again and a questionnaire was completed. Another goal of this paper was to investigate the difference between perceived usefulness (general usability, usefulness, and user interface satisfaction) of experts and non-experts (sports science students and computer science students) and pro-gamers and casual gamers. The findings were encouraging. The majority of the players reported that the gaming experience was very satisfying and the game was easy to use and learn. Moreover, after the analysis, it was discovered that this game can significantly improve the simple reaction time of all players. This improvement was independent from the players’ background.

• A lightweight Open Pose model was developed for community settings' applications. • The proposed binocular vision system showed good feasibility in static experiments. • The proposed system proved good effective compared to the NOKOV system. • The proposed system exhibited better stability than the Kinect series devices. This study aimed to achieve precise 3D human motion data acquisition without the need for markers. Specifically, the proposed system utilized two affordable cameras to capture human motion from diverse angles. It developed a lightweight Open Pose algorithm to extract 2D human body joints from recorded videos. Subsequently, a stereo-matching method was applied to derive accurate 3D human body joint data. Comprehensive experiments with 10 healthy participants showcased the system's exceptional performance, demonstrating superior measurement accuracy compared to established standards like the NOKOV system (RMSE values consistently below 9°) and commonly used Microsoft Kinect devices (V2 and Azure). This innovative binocular vision system presents a new approach to motion capture, potentially offering a cost-effective and precise tool for practical applications across various scenarios (e.g., motor functional assessment, virtual avatar streaming), contributing significantly to both scientific and academic communities. [ABSTRACT FROM AUTHOR]

Accurate gait recognition is of high significance for numerous industrial and consumer applications, including video surveillance, virtual reality, on-line games, medical rehabilitation, collaborative space exploration, and others. This paper proposes a new architecture designed using deep learning neural network for a highly accurate and robust Kinect-based gait recognition. Two new geometric features: joint relative cosine dissimilarity and joint relative triangle area are introduced. Both of the proposed features are view and pose invariant, thus enhancing recognition performance. The proposed neural network model is trained using the feature vector of dynamic joint relative cosine dissimilarity and joint relative triangle area. Subsequent application of Adam optimization method minimizes the loss of the objective function iteratively. The performance of the proposed deep learning neural network architecture is evaluated on two publicly available 3D skeleton-based gait datasets recorded with the Microsoft Kinect sensor. It is experimentally proven that the accuracy, precision, recall, and F-score of the proposed neural network architecture, trained using introduced dynamic geometric features, is superior to other state-of-the-art methods for Kinect skeleton-based gait recognition.