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Human activity recognition is known as the backbone of the development of interactive systems, such as computer games. This process is usually performed by either vision-based or depth sensors. So far, various solutions have been developed for this purpose; however, all the challenges of this process have not been completely resolved. In this paper, a solution based on pattern recognition has been developed for labeling and scoring physical exercises performed in front of the Kinect sensor. Extracting the features from human skeletal joints and then generating relative descriptors among them is the first step of our method. This has led to quantification of the meaningful relationships between different parts of the skeletal joints during exercise performance. In this method, the discriminating descriptors of each exercise motion are used to identify the adaptive kernels of the Constrained Energy Minimization method as a target detector operator. The results indicated an accuracy of 95.9% in the labeling process of physical exercise motions. Scoring the exercise motions was the second step after the labeling process, in which a geometric method was used to interpolate numerical quantities extracted from descriptor vectors to transform into semantic scores. The results demonstrated the scoring process coincided with the scores derived by the sports coach by a 99.5 grade in the R2 index. [ABSTRACT FROM AUTHOR]

Everyone aspires to live a healthy life, but many will inevitably experience some form of disease, illness, or accident that results in disability at some point. Rehabilitation plays a crucial role in helping individuals recover from these disabilities and return to their daily activities. Traditional rehabilitation methods are often expensive, are inefficient, and lead to slow progress for patients. However, in this era of technology, various sensor-based automatic rehabilitation is also possible. A Kinect sensor is a skeletal tracking device that captures human motions and gestures. It can provide feedback to the users, allowing them to better understand their progress and adjust their movements accordingly. In this study, stroke-based rehabilitation is presented along with the Toronto Rehab Stroke Pose Dataset (TRSP). Pre-processing of the raw dataset was performed using various features, and several state-of-the-art classifiers were applied to evaluate the data provided by the Kinect sensor. Among the various classifiers, eXtreme Gradient Boosing (XGB) attained the maximum accuracy of 92% for the TRSP dataset. Furthermore, hyperparameters of the XGB have been optimized using a metaheuristic gray wolf optimizer for better performance. [ABSTRACT FROM AUTHOR]

We investigated children's positive emotions as an indicator of their underlying prosocial motivation. In Study 1, 2-, and 5-year-old children (N = 64) could either help an individual or watch as another person provided help. Following the helping event and using depth sensor imaging, we measured children's positive emotions through changes in postural elevation. For 2-year-olds, helping the individual and watching another person help was equally rewarding; 5-year-olds showed greater postural elevation after actively helping. In Study 2, 5-year-olds' (N = 59) positive emotions following helping were greater when an audience was watching. Together, these results suggest that 2-year-old children have an intrinsic concern that individuals be helped whereas 5-year-old children have an additional, strategic motivation to improve their reputation by helping.

Traditional robotic arms rely on complex programming and predefined trajectories to operate, which limits their applicability. To improve the flexibility and adaptability of the robot arm, the research focuses on improving the grasping performance of the robot arm based on vision technology. Kinect technology is used to capture human arm movements, and Kalman filter is introduced to smooth image data, so as to optimize the motion recognition process. In this study, the residual network model is further improved, and ELU activation function and pre-activation mechanism are introduced to enhance the classification accuracy of gesture images. The results showed that the improved ResNet50 model achieves 95% recognition accuracy after 25 iterations of training, while the original model is 80%. The application of Kalman filter makes the motion tracking curve smoother and shows the correction effect of this method. In simulation tests, the robotic arm is able to identify different elbow bending angles with 90-96 percent accuracy, while mimicking five specific hand gestures with 96-98 percent accuracy. These data support the practicability and effectiveness of the application of vision capture technology and deep learning model in the field of intelligent control of robotic arms. [ABSTRACT FROM AUTHOR]

As manufacturing processes of complex products become automated, manual operations still occupy a considerable portion of industrial maintenance and assembly (IMA), especially in the machinery and aerospace fields. Workers often exhibit awkward posture in IMA activities. In these scenarios, posture assessment is critical for improving the well-being of workers because awkward postures can lead to work-related musculoskeletal disorder (WMSD). Although there are several categorized WMSD risk assessment methods, limited evidence suggests that these methods are compliant for modern complex IMA scenarios. In this paper, a posture analysis system for manual operation (PASMO) is presented to monitor working postures and evaluate WMSD risks in IMA processes. The noninvasive depth sensor Kinect v2 and the rapid upper limb analysis (RULA) method are integrated to achieve this purpose. In the PASMO, the RULA is optimized and driven by motion capture (MoCap) data to make evaluating the WMSD risk of working postures more effective and accurate. Industrial and laboratory experiments are designed to verify the effectiveness and system performance of the PASMO. The results show that for the overall body and most joints, the scores obtained by the PASMO substantially agree with those obtained by the ground truth data (p < 0.01, κ = 0.65) under the real industrial environment. Because the experiments are conducted in real IMA scenarios with body occlusion, the results preliminarily prove the effectiveness of the PASMO for WMSD risk assessment in continuous IMA tasks. [ABSTRACT FROM AUTHOR]

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

This study aims to develop an efficient 24-type Tai Chi motion recognition system by using Kinect and mobile edge computing (MEC) technology, to improve the teaching and training effect of Tai Chi. First, by recruiting Tai Chi experts and beginners to conduct experiments, a large number of 24-type Tai Chi movement data are collected to ensure the representativeness and generalization ability of the dataset. Second, in an experimental environment equipped with Kinect devices and MEC nodes, a Tai Chi action recognition model by utilizing Graph Convolutional Network with attention mechanism is constructed. The experimental results reveal that the proposed algorithm achieves 95.17% accuracy in action recognition, markedly higher than the prediction accuracy of the Convolutional Neural Network algorithm. In terms of data transmission, the proposed algorithm’s average delay is stable at 0.53 seconds when the data volume is 6Mb, and the packet loss rate is maintained below 7.7%. The conclusion shows that the proposed algorithm not only has excellent performance in recognition accuracy and real-time performance but also has high stability and reliability in network data transmission performance, which provides effective technical support for Tai Chi’s digital teaching and training.

Objective: Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults. Methods: Twenty-three typically developing children and healthy young adults (5-29 years, 110–189 cm) performed five movement tasks while being recorded simultaneously with a marker-based Vicon system and an Azure Kinect RGB-D camera. Motion parameters were computed from the extracted kinematics of both methods: time series measurements, i.e., measurements over time, peak measurements, i.e., measurements at a single time instant, and movement smoothness. The agreement of these parameter values was evaluated using Pearson’s correlation coefficients r for time series data, and mean absolute error (MAE) and Bland-Altman plots with limits of agreement for peak measurements and smoothness. Results: Time series measurements showed strong to excellent correlations (r-values between 0.8 and 1.0), MAE for angles ranged from 1.5 to 5 degrees and for smoothness parameters (SPARC) from 0.02-0.09, while MAE for distance-related parameters ranged from 9 to 15 mm. Conclusion: Extracted motion parameters are valid and accurate for various movement tasks in children and young adults, demonstrating the suitability of our tracking method for clinical motion analysis. Clinical Impact: The low-cost portable hardware in combination with our tracking method enables motion analysis outside of specialized facilities while providing measurements that are close to those of the clinical gold-standard.

Human activity recognition is known as the backbone of the development of interactive systems, such as computer games. This process is usually performed by either vision-based or depth sensors. So far, various solutions have been developed for this purpose; however, all the challenges of this process have not been completely resolved. In this paper, a solution based on pattern recognition has been developed for labeling and scoring physical exercises performed in front of the Kinect sensor. Extracting the features from human skeletal joints and then generating relative descriptors among them is the first step of our method. This has led to quantification of the meaningful relationships between different parts of the skeletal joints during exercise performance. In this method, the discriminating descriptors of each exercise motion are used to identify the adaptive kernels of the Constrained Energy Minimization method as a target detector operator. The results indicated an accuracy of 95.9% in the labeling process of physical exercise motions. Scoring the exercise motions was the second step after the labeling process, in which a geometric method was used to interpolate numerical quantities extracted from descriptor vectors to transform into semantic scores. The results demonstrated the scoring process coincided with the scores derived by the sports coach by a 99.5 grade in the R2 index.

Effects of bed material and downstream flow depth on bed evolution in a right-angled channel confluence were studied through laboratory experiments. Velocity field, water surface elevation, and bed profile were measured intermittently until the flow attained equilibrium. Sediment discharges at various locations of the confluence were estimated from bed levels. Six prominent bedforms were identified, and their characteristics were quantified. The sediment discharges from different channels were initially high but decreased to approximately zero as the flow attained equilibrium. Downstream flow depth influenced overall sediment transport in the system, including the main features of bed morphology. Overall erosion in the confluence reduced as the particle size of the bed material increased. In addition, the maximum scour depth occurred at the confluence edge, and, due to the sharp corner of the confluence, its location did not change with time. Results from the present experimental study can help validate numerical models and assist in the design of a right-angled confluence. This study is related to the hydraulics of man-made confluences with mobile beds and can be useful in the following practical applications. More accurate numerical models to predict maximum scour, bed morphodynamics, and sediment transport in man-made confluences can be developed using the detailed bed profile measurements at different time instants presented in this study. Note that available numerical models use sediment discharge formulas (e.g., Meyer-Peter-Muller, Van Rijn), which are for sediment flows in straight channels. The interplay between the three-dimensional flow field, developed bed shear stress, and sediment discharge can be explored. In addition, A man-made confluence with the mobile bed can be designed with findings from the present study. The bedforms corresponding to equilibrium flow depend on discharge ratio, confluence angle, flow conditions at the exit channel, and bed material characteristics. The major bedforms can be incorporated into the confluence geometry. [ABSTRACT FROM AUTHOR]

Body composition assessment using instruments such as dual X-ray densitometry (DXA) can be complex and their use is often limited to research. This cross-sectional study aimed to develop and validate a densitometric method for fat mass (FM) estimation using 3D cameras. Using two such cameras, stereographic images, and a mesh reconstruction algorithm, 3D models were obtained. The FM estimations were compared using DXA as a reference. In total, 28 adults, with a mean BMI of 24.5 (±3.7) kg/m2 and mean FM (by DXA) of 19.6 (±5.8) kg, were enrolled. The intraclass correlation coefficient (ICC) for body volume (BV) was 0.98–0.99 (95% CI, 0.97–0.99) for intra-observer and 0.98 (95% CI, 0.96–0.99) for inter-observer reliability. The coefficient of variation for kinetic BV was 0.20 and the mean difference (bias) for BV (liter) between Bod Pod and Kinect was 0.16 (95% CI, −1.2 to 1.6), while the limits of agreement (LoA) were 7.1 to −7.5 L. The mean bias for FM (kg) between DXA and Kinect was −0.29 (95% CI, −2.7 to 2.1), and the LoA was 12.1 to −12.7 kg. The adjusted R2 obtained using an FM regression model was 0.86. The measurements of this 3D camera-based system aligned with the reference measurements, showing the system's feasibility as a simpler, more economical screening tool than current systems. [ABSTRACT FROM AUTHOR]

Falls are a leading cause of unintentional injury and death and pose a significant risk to individuals living alone, particularly older adults. The World Health Organization reports that approximately 684000 fatal falls occur annually, and that timely fall detection is essential to enable prompt treatment and mitigate harm. To address this challenge, we propose a novel inhome fall detection system that uses the Kinect V2 sensor instead of a wearable device. The system can promptly alert a person's caregivers or family members when it detects a fall, enabling timely assistance. The Kinect V2 sensor captures depth frames and performs skeleton tracking, and body parts in single-depth image pixels are identified using a random forest classifier. v-disparity images, the Hough transform, and the random sample consensus algorithm are used to identify the floor in depth images, and falls are predicted by analyzing the vertical accelerations of 10 joints in the human skeleton. The system can issue an alarm up to 0.5 s before a fall, enabling preventive measures to be taken. In experiments, the proposed system was effective in accurately predicting falls and hence, has potential to improve fall risk management in caregiving environments. [ABSTRACT FROM AUTHOR]

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder known for its significant heterogeneity and varied symptom presentation. Describing the different subtypes as predominantly inattentive (ADHD–I), combined (ADHD–C), and hyperactive–impulsive (ADHD–H) relies primarily on clinical observations, which can be subjective. To address the need for more objective diagnostic methods, this pilot study implemented a Microsoft Kinect-based Stroop Color–Word Test (KSWCT) with the objective of investigating the potential differences in executive function and motor control between different subtypes in a group of children and adolescents with ADHD. A series of linear mixture modeling were used to encompass the performance accuracy, reaction times, and extraneous movements during the tests. Our findings suggested that age plays a critical role, and older subjects showed improvements in KSWCT performance; however, no significant divergence in activity level between the subtypes (ADHD–I and ADHD–H/C) was established. Patients with ADHD–H/C showed tendencies toward deficits in motor planning and executive control, exhibited by shorter reaction times for incorrect responses and more difficulty suppressing erroneous responses. This study provides preliminary evidence of unique executive characteristics among ADHD subtypes, advances our understanding of the heterogeneity of the disorder, and lays the foundation for the development of refined and objective diagnostic tools for ADHD. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of Kinect-based mixed reality (KMR) exercise and unsupervised individual exercise on health-related fitness. A total of 27 participants underwent cardiorespiratory fitness tests for the inclusion criteria and were randomly assigned to three groups: a KMR group (KMRG), an unsupervised individual group (UIG), or a control group (CG). Pre and post-tests were conducted to measure Maximum oxygen uptake (VO₂max), body composition, upper and lower-body (LB) muscle strength, and endurance. KMRG and UIG attended exercise sessions 3 days per week for 8 weeks. KMRG used the KMR device and UIG used an instructive banner for exercise. All groups maintained their daily routines and submitted diet records every 4 weeks. Results showed that VO₂max, upper-body muscle endurance, and LB muscle endurance of knee extension was increased in KMRG and UIG. LB muscle strength in knee flexion was increased in UIG and LB muscle endurance in knee flexion was increased in KMRG. VO₂max, LB muscle strength, and LB muscle endurance were greater in KMRG than in CG. LB muscle strength in knee flexion was greater in KMRG than in UIG. Body fat was increased and skeletal muscle mass was decreased in CG. KMR exercise showed better performance than unsupervised individual (UI) exercise, and the exercise program was effective in both KMR and UI environments. These findings contribute to the growing evidence supporting the use of technology-based exercise interventions as a potential strategy to enhance health-related fitness.

BackgroundOutcome measures for non-ambulant Duchenne muscular dystrophy (DMD) patients are limited, with only the Performance of the Upper Limb (PUL) approved as endpoint for clinical trials.ObjectiveWe assessed four outcome measures based on devices developed for the gaming industry, aiming to overcome disadvantages of observer-dependency and motivation.MethodsTwenty-two non-ambulant DMD patients (range 8.6-24.1 years) and 14 healthy controls (HC; range 9.5-25.4 years) were studied at baseline and 16 patients at 12 months using Leap Motion to quantify wrist/hand active range of motion (aROM) and a Kinect sensor for reached volume with Ability Captured Through Interactive Video Evaluation (ACTIVE), Functional Workspace (FWS) summed distance to seven upper extremity body points, and trunk compensation (KinectTC). PUL 2.0 was performed in patients only. A stepwise approach assessed quality control, construct validity, reliability, concurrent validity, longitudinal change and patient perception.ResultsLeap Motion aROM distinguished patients and HCs for supination, radial deviation and wrist flexion (range p = 0.006 to

The elderly population experiences a decline in upper extremity range of motion (ROM), impairing activities of daily living. The primary mode of quantification is by goniometer measurement. In this cross-sectional observation study, we investigate a sensor-acquired reachable workspace for assessing shoulder ROM decline in an elderly population in comparison to traditional measurements. Sixty-one healthy subjects aged ≥ 65 years were included and compared to a cohort of 39 younger subjects, aged 20 to 64. A sensor acquired reachable workspace using a Kinect motion capture camera measured the maximum reaching ability of both arms while in a seated position, measured in m2 and normalized to arm length to calculate a novel score defined as a relative surface area. This score approximates range of motion in the upper extremity. This measurement was compared to goniometer measurements, including active ROM in shoulder flexion and abduction. Total RSA shows moderate to strong correlation between goniometer in flexion and abduction in the dominant arm (R = 0.790 and R = 0.650, P < .001, respectively) and moderate correlations for the nondominant arm (R = 0.622 and R = 0.615, P < .001). Compared to the younger cohort, the elderly population demonstrated significantly reduced total RSA in the dominant arm (meanelderly = 0.774, SD = 0.09; meanyounger = 0.830, SD = 0.07, P < .001), with significant reductions in the upper lateral quadrant in both arms (dominant: meanelderly = 0.225, SD = 0.04; meanyounger = 0.241, SD = 0.01; P < .001; nondominant: meanelderly = 0.213, SD = 0.03; meanyounger = 0.228, SD = 0.01; P = .004). The test-retest reliability was strong for both dominant and nondominant total RSA (ICC > 0.762). The reachable workspace demonstrates promise as a simple and quick tool for clinicians to assess detailed and quantitative active shoulder ROM decline in the elderly population.

Everyone aspires to live a healthy life, but many will inevitably experience some form of disease, illness, or accident that results in disability at some point. Rehabilitation plays a crucial role in helping individuals recover from these disabilities and return to their daily activities. Traditional rehabilitation methods are often expensive, are inefficient, and lead to slow progress for patients. However, in this era of technology, various sensor-based automatic rehabilitation is also possible. A Kinect sensor is a skeletal tracking device that captures human motions and gestures. It can provide feedback to the users, allowing them to better understand their progress and adjust their movements accordingly. In this study, stroke-based rehabilitation is presented along with the Toronto Rehab Stroke Pose Dataset (TRSP). Pre-processing of the raw dataset was performed using various features, and several state-of-the-art classifiers were applied to evaluate the data provided by the Kinect sensor. Among the various classifiers, eXtreme Gradient Boosing (XGB) attained the maximum accuracy of 92% for the TRSP dataset. Furthermore, hyperparameters of the XGB have been optimized using a metaheuristic gray wolf optimizer for better performance.

Abstract Background With concerns about accurate diagnosis through telehealth, the Kinect sensor offers a reliable solution for movement analysis. However, there is a lack of practical research investigating the suitability of a Kinect-based system as a functional fitness assessment tool in homecare settings. Hence, the objective of this study was to evaluate the feasibility of using a Kinect-based system to assess physical function changes in the elderly. Methods The study consisted of two phases. Phase one involved 35 young healthy adults, evaluating the reliability and validity of a Kinect-based fitness evaluation compared to traditional physical examination using the intraclass correlation coefficient (ICC). Phase two involved 665 elderly subjects, examining the correlation between the Kinect-based fitness evaluation and physical examination through Pearson’s correlation coefficients. A Kinect sensor (Microsoft Xbox One Kinect V2) with customized software was employed to capture and compute the movement of joint centers. Both groups performed seven functional assessments simultaneously monitored by a physical therapist and the Kinect system. System usability and user satisfaction were assessed using the System Usability Scale (SUS) and Questionnaire for User Interface Satisfaction (QUIS), respectively. Results Kinect-based system showed overall moderate to excellent within-day reliability (ICC = 0.633-1.0) and between-day reliability (ICC = 0.686-1.0). The overall agreement between the two devices was highly correlated (r ≧ 0.7) for all functional assessment tests in young healthy adults. The Kinect-based system also showed a high correlation with physical examination for the functional assessments (r = 0.858–0.988) except functional reach (r = 0.484) and walking speed(r = 0.493). The users’ satisfaction with the system was excellent (SUS score = 84.4 ± 18.5; QUIS score = 6.5–6.7). Conclusions The reliability and validity of Kinect for assessing functional performance are generally favorable. Nonetheless, caution is advised when employing Kinect for tasks involving depth changes, such as functional reach and walking speed tests for their moderate validity. However, Kinect’s fundamental motion detection capabilities demonstrate its potential for future applications in telerehabilitation in different healthcare settings.

Aiming at the current lack of innovation in Chinese ceramic culture, this paper proposes an inheritance and innovation path combining ceramic digitization technology and ceramic virtual display technology. Based on the three-dimensional morphology reconstruction technology method, using a three-dimensional scanner in different groups of lamps under the scanning pottery plastic to obtain point cloud data, using an iterative proximity point algorithm to achieve point cloud data alignment, and obtaining a complete three-dimensional model of pottery plastic. Then, we propose the ceramic virtual display technology based on Kinect, using Kinect to capture human motion data, designing somatosensory interaction subjects, expanding Kinect human-computer interaction actions, and providing an interactive and experiential ceramic culture display. Using ceramic digital technology to build three-dimensional digital modeling of the five famous kilns in China, the simulation effect evaluation score is greater than 80 points, of which the Gao kiln has the highest average score of 90 points. The use of ceramic virtual display technology to construct a virtual ceramic museum of the Maritime Silk Road demonstrates good system performance. In each satisfaction evaluation factor, the online visitors exhibit a general, satisfied, or very satisfied attitude, with a proportion exceeding 60%.

Augmented reality (AR) technology has rapidly advanced across various domains, propelled by its robust interactive immersion and the seamless integration of real and virtual environments. However, its exploration and deployment in theatrical contexts remain limited. This study leverages the Kinect system to capture images during costume performances, employing algorithms for dynamic frame difference merging and human-computer interaction to detect performers’ body movements. Building on this, the study constructs a visually innovative stage for costume performances that enhances the interactive experience for the audience. Additionally, a multimodal emotion analysis model is utilized to assess audience emotions, demonstrating significantly higher accuracy and F1 scores compared to other emotion analysis models. This model effectively integrates speech, expression, and action, surpassing the performance of unimodal analyses in emotion recognition. Furthermore, the audience's experiential perception of stage lighting effects notably exceeds expectations (P=0.013 < 0.05), underscoring an enhanced interaction experience. This research substantiates the transformative potential of AR technology in stage design, offering audiences a more innovative visual and interactive experience, and serves as a valuable reference for future applications in this field.

The integration of digital media art into the public art design of the smart city can enhance people’s participation and sense of identity in the art design of public space and maximize the attractiveness and cultural connotation of the city itself. Based on this, this paper builds a fusion technology framework mainly based on digital projection technology and digital interaction technology, proposes an interactive projection technology integrating digital projection and Kinect, and combines the dynamic scene image splicing and alignment fusion technology to realize the interaction between the user and the digital scene. In the case analysis of Qianjiang New City, the number of visitors reached 31.55 million in 2022. The evaluation score of landscape facilities in the public evaluation of architectural landscape is under 3.5. The overall evaluation grade is only “general,” and the evaluation mean value of the degree of intelligence of landscape facilities is only 3.089. The comprehensive evaluation satisfaction of the digital landscape includes multimedia landscape, architectural landscape, and landscape facilities, and the average value of the evaluation of the degree of intelligence of the landscape facilities is only 3.089. The satisfaction value of the multimedia landscape, landscape of buildings and structures, and water landscape in the evaluation of satisfaction is 4.36, 4.159, and 4.027, respectively, which reaches the level of “good” satisfaction, while the satisfaction value of landscape facilities is 3.479, which is less than 3.5. The satisfaction level only reaches the level of “general.” The satisfaction value of landscape facilities is 3.479, which is less than 3.5, and the satisfaction level is only “fair.”

Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.

This paper combines Kinect and convolutional neural networks to construct a dance movement recognition technology based on 3D CNNs. Applying dance movement recognition technology to dance teaching builds a new mode of training dance talents in colleges and universities. The role of Kinect in recognizing dance movements is explored from three aspects: real-time motion capture, human skeleton tracking, and information input. The dance movement image is computerized by calculating the depth of the points using stereo analysis. To analyze dance movements and classify them by features, a convolutional neural network is combined. Based on two-dimensional convolutional neural networks, three-dimensional convolutional neural networks have been constructed, which improve the comprehensiveness of dance movement information. By combining dance movement recognition technology with dance talent cultivation, we analyze the students’ professional dance ability and the teaching effect under the new talent cultivation mode. The results show that the teaching effect of the dance talent cultivation mode combined with the movement recognition technology is better, and the percentage of students dance movements reaching the standard in a movement completion is 0.95. The professional ability of dance talent is 0.8 percent.

With the increasing prosperity of the cultural market, the dance culture industry is also developing rapidly. In the face of opportunities and challenges brought by dance industrialization, dance professionals trained by the traditional and single-skill training system of art colleges and universities expose the contradiction between supply and demand. In this paper, the SAA-training model is taken as the calculation tool, and the OBE and Kinect motion capture technology are combined to explore the construction and practice of the training system for dance professionals. The research shows that the training system of dance professionals can finally obtain three types of dance professionals. The first type is compound dance talents, accounting for about 33%. The second category consists of dancers with rich market experience, accounting for up to 46 percent. The third category is dance talent with innovative consciousness, limited by the existing training mode, its proportion is only 21 percent.

In this paper, a static gesture classification model based on a convolutional neural network is proposed to achieve the goal of efficient interaction control and fast response time for gesture recognition, and hand feature extraction is achieved by using alternating convolution and pooling. In the convolutional neural network, image data is obtained by preprocessing operations such as adaptive thresholding and functions such as Gaussian filtering to provide data for the convolutional neural network structure to build the recognition model, which is used to quantify the edge characteristics of the image and construct the image edges with gradient amplitude to build the static gesture recognition algorithm for Kinect. In the accuracy rate for gesture interaction, the average recognition rate for slow movement is 96.5%, while when the interactive gesture is recognized at an accelerated speed, its recognition rate slips to 93.1%, and the total average recognition rate reaches 95.2%.

Falling down is a common symptom of geriatric syndromes, and fractures and intracranial hemorrhages triggered by falling down lead to serious problems and impair life functioning. Moreover, it sometimes leads to a higher risk of death. In Japan in recent years, the number of fatalities from traffic accidents has been declining, whereas the number of fatalities from falls has been leveling off. In 2020, 8851 people died from falls, whereas the number of fatalities from traffic accidents was 2199. The number of fatalities among the elderly due to falls is approximately four times the number of fatalities from traffic accidents. Therefore, in this paper, we propose a system that analyzes the body by using Kinect, an infrared depth sensor for tracking a skeletal model of a user. In this study, the goal is for the predicted fall values from Kinect-measured data and the predicted fall values from motion-capture-measured data to be close to the predicted values, so that this technology can eventually be used in clinical practice. On the basis of information from the skeletal model, the system analyzes element indices such as the center of gravity and body tilt of people in need of nursing care when falling down. Then, it predicts the risk factor for falling down. This information is used for detecting warning signs for falling down in the early stages. Finally, this study will contribute to decreasing number of falls from the bed. [ABSTRACT FROM AUTHOR]

Innovative technologies can improve user usability and satisfaction in computer-based activities in the work environment, including surgeons working in the operating room (OR). A scrubbed surgeon must ask unscrubbed staff to retrieve medical images from a surgical PACS system on a monitor in a hybrid operating room. The study aimed to check users' usability and satisfaction with the designed and developed Ortho_Kinect_OR application, which enables contactless control of access to medical images during surgery. The application also facilitates access to telemedicine applications such as intraoperative telementoring during orthopedic procedures or tele-education. The application's usability was tested by assigning standardized tasks to surgeons for PACS and teleconferencing in the operating room. Hand gestures were recognized and translated to function like mouse buttons. Field tests conducted among orthopedic surgeons showed high usability and user satisfaction. PACS access in the operating room did not distract the orthopedic surgeon during orthopedic surgery procedures. All surgeons completed the tests and tasks without any problems. OR field test results showed high agreement among users and were very satisfactory. Junior surgeons and residents pointed out that people with average computer knowledge could use the application. It has been shown that the contactless system designed and built based on the Kinect sensor available on the shelves meets the requirements of the operating room environment and is easy to use. Touchless controller technology provides the opportunity to increase the use of intraoperative imaging previews and improve the safety of surgical patients by improving sterility and reducing unnecessary staff in the operating room. Using the Ortho_Kinect_OR application and the Kinect sensor, it is possible to provide contactless access to videoconference telementoring and PACS in the operating room without an orthopedic surgeon's unnecessary distraction in the operating room environment. [ABSTRACT FROM AUTHOR]

Background: This study relates to emerging concepts of appropriate trial designs to evaluate effects of intervention on the accumulation of irreversible disability in multiple sclerosis (MS). Major starting points of our study are the known limitations of current definitions of disability progression by rater-based clinical assessment and the high relevance of gait and balance dysfunctions in MS. The study aims to explore a novel definition of disease progression using repeated instrumental assessment of relevant motor functions performed by patients in their home setting. Methods: The study is a prospective single-center observational cohort study with the primary outcome acquired by participants themselves, a home-based assessment of motor functions based on an RGB-Depth (RGB-D) camera, a camera that provides both depth (D) and color (RGB) data. Participants are instructed to perform and record a set of simple motor tasks twice a day over a one-week period every 6 months. Assessments are complemented by a set of questionnaires. Annual research grade assessments are acquired at dedicated study visits and include clinical ratings as well as structural imaging (MRI and optical coherence tomography). In addition, clinical data from routine visits is provided semiannually by treating neurologists. The observation period is 24 months for the primary endpoint with an additional clinical assessment at 27 month to confirm progression defined by the Expanded Disability Status Scale (EDSS). Secondary analyses aim to explore the time course of changes in motor parameters and performance of the novel definition against different alternative definitions of progression in MS. The study was registered at Deutsches Register für Klinische Studien (DRKS00027042). Discussion: The study design presented here investigates disease progression defined by marker-less home-based assessment of motor functions against 3-month confirmed disease progression (3 m-CDP) defined by the EDSS. The technical approach was chosen due to previous experience in lab-based settings. The observation time per participant of 24, respectively, 27 months is commonly conceived as the lower limit needed to study disability progression. Defining a valid digital motor outcome for disease progression in MS may help to reduce observation times in clinical trials and add confidence to the detection of progression events in MS. [ABSTRACT FROM AUTHOR]

The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial burdens. A growing elderly population leads to a direct increase in health service costs, and indirectly to a deterioration of social life in the long term. Unsurprisingly, socioeconomic costs have triggered new scientific health research to detect falls in older people. One of the most appropriate solutions for monitoring the elderly and automatically detecting falls is computer vision. The Kinect camera plays a vital role in recognizing and detecting activities while ensuring seniors' comfort, safety, and privacy preferences in the fall detection system. This research surveys several Kinect-based works in the literature that cover the approaches used in fall detection. In addition, we discuss the public fall benchmark based on Kinect technology. In general, the main objective of this survey is to provide a complete description of the modules making up the fall detectors and thereby guide researchers in developing fall approaches based on Kinect. [ABSTRACT FROM AUTHOR]

Introduction: Individuals with Parkinsonian disorders often face limited access to specialized physiotherapy and movement training due to staff shortages and increasing disease incidence, resulting in a rapid decline in mobility and feelings of despair. Addressing these challenges requires allocating adequate resources and implementing specialized training programs to ensure comprehensive care and support. Regarding these problems, a computer software was invented that might serve as an additional home-based extension to conventional physiotherapy. Methods: The trial took place in a rehabilitation center where every patient received equivalent treatment apart from the training program that was set up to be investigated over 3 weeks. Seventy four Patients were included and randomized between two intervention and one control group. Intervention group 1 (IG1) trained with the computer-based system two times a week while Intervention group 2 (IG2) received five training sessions a week. Using the markerless Microsoft Kinect® camera, participants controlled a digital avatar with their own body movements. UPDRS-III and Clinical measurements were performed before and after the three-week period. Results: Patients in all groups improved in UPDRS-III pre and post intervention whereas reduction rates were higher for IG1 (-10.89%) and IG2 (-14.04%) than for CG (-7.74%). Differences between the groups were not significant (value of ps CG/IG1 0.225, CG/IG2 0.347). Growth rates for the arm abduction angle were significantly higher in IG1 (11.6%) and IG2 (9.97%) than in CG (1.87%) (value of ps CG/IG1 0.006 and CG/IG2 0.018), as was the 5-steps-distance (CG 10.86% vs. IG1 24.5% vs. UG2 26.22%, value of ps CG/IG1 0.011 and CG/IG2 0.031). Discussion: The study shows the beneficial effects of computer-based training and substantiates the assumption of a similar impact in a home-based setting. The utilized software is feasible for such interventions and meets with the patient's approval. Group dynamics seem to have an additional supporting effect for the aspired objective of improving mobility and should be seen as an essential aspect of video games in therapy. [ABSTRACT FROM AUTHOR]

Background: With concerns about accurate diagnosis through telehealth, the Kinect sensor offers a reliable solution for movement analysis. However, there is a lack of practical research investigating the suitability of a Kinect-based system as a functional fitness assessment tool in homecare settings. Hence, the objective of this study was to evaluate the feasibility of using a Kinect-based system to assess physical function changes in the elderly. Methods: The study consisted of two phases. Phase one involved 35 young healthy adults, evaluating the reliability and validity of a Kinect-based fitness evaluation compared to traditional physical examination using the intraclass correlation coefficient (ICC). Phase two involved 665 elderly subjects, examining the correlation between the Kinect-based fitness evaluation and physical examination through Pearson's correlation coefficients. A Kinect sensor (Microsoft Xbox One Kinect V2) with customized software was employed to capture and compute the movement of joint centers. Both groups performed seven functional assessments simultaneously monitored by a physical therapist and the Kinect system. System usability and user satisfaction were assessed using the System Usability Scale (SUS) and Questionnaire for User Interface Satisfaction (QUIS), respectively. Results: Kinect-based system showed overall moderate to excellent within-day reliability (ICC = 0.633-1.0) and between-day reliability (ICC = 0.686-1.0). The overall agreement between the two devices was highly correlated (r ≧ 0.7) for all functional assessment tests in young healthy adults. The Kinect-based system also showed a high correlation with physical examination for the functional assessments (r = 0.858–0.988) except functional reach (r = 0.484) and walking speed(r = 0.493). The users' satisfaction with the system was excellent (SUS score = 84.4 ± 18.5; QUIS score = 6.5–6.7). Conclusions: The reliability and validity of Kinect for assessing functional performance are generally favorable. Nonetheless, caution is advised when employing Kinect for tasks involving depth changes, such as functional reach and walking speed tests for their moderate validity. However, Kinect's fundamental motion detection capabilities demonstrate its potential for future applications in telerehabilitation in different healthcare settings. [ABSTRACT FROM AUTHOR]

Augmented Reality has become a bridge from the virtual to the real world. This technology has been an implementable choice for fashion companies to provide more information and customization for their user's shopping experience. From the COVID-19 pandemic alongside technological globalization, fashion industries have to find a new way for customers to purchase products. All things considered, AR helped fashion industries keep customers attracted and satisfied. This paper is conducted in a Systematic Literature Review manner to review and compare the AR technologies used by fashion industries. Furthermore, the result would be a proposition of which Augmented Reality technology or platform option is feasible and implementable for the fashion industry. After comparing the technologies used in past papers and their technical and economic feasibility, we propose a 3-term plan. The short-term plan with zero cost and mobile platform-focused application, the medium-term plan with an upgraded version of the garment object from 2D images to 3D objects, and the long-term plan changing the platform used from mobile to a new device. We also conclude that artificial intelligence can also support and improve the augmented reality experience for customers in the fashion retail industry by implementing an AI-powered virtual assistant. [ABSTRACT FROM AUTHOR]

Aiming at the problem of feature point tracking loss caused by large head rotation and facial occlusion in doctors, this paper designs a head-position-tracking system based on geometric triangles and unscented Kalman filtering. By interconnecting the three feature points of the left and right pupil centers and the tip of the nose, they form a coplanar triangle. When the posture of the doctor's head changes due to rotation, the shape of the corresponding geometric triangle will also deform. Using the inherent laws therein, the head posture can be estimated based on changes in the geometric model. Due to the inaccurate positioning of feature points caused by the deflection of the human head wearing a mask, traditional linear Kalman filtering algorithms are difficult to accurately track feature points. This paper combines geometric triangles with an unscented Kalman Filter (UKF) to obtain head posture, which has been fully tested in different environments, such as different faces, wearing/not wearing masks, and dark/bright light via public and measured datasets. The final experimental results show that compared to the linear Kalman filtering algorithm with a single feature point, the traceless Kalman filtering algorithm combined with geometric triangles in this paper not only improves the robustness of nonlinear angle of view tracking but also can provide more accurate estimates than traditional Kalman filters. [ABSTRACT FROM AUTHOR]

Low-cost, portable RGB-D cameras with integrated body tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications. In this study, we investigated the concurrent validity of our custom tracking method based on RGB-D images with respect to a gold-standard marker-based system. Additionally, we analyzed the validity of the publicly available Microsoft Azure Kinect Body Tracking (K4ABT). We recorded 23 typically developing children and healthy young adults (aged 5 to 29 years) performing five different movement tasks using a Microsoft Azure Kinect RGB-D camera and a marker-based multi-camera Vicon system simultaneously. Our method achieved a mean per joint position error over all joints of 11.7 mm compared to the Vicon system, and 98.4% of the estimated joint positions had an error of less than 50 mm. Pearson’s correlation coefficients ${r}$ ranged from strong ( ${r}$ =0.64) to almost perfect ( ${r}>$ 0.99). K4ABT demonstrated satisfactory accuracy most of the time but showed short periods of tracking failures in nearly two-thirds of all sequences limiting its use for clinical motion analysis. In conclusion, our tracking method highly agrees with the gold standard system. It paves the way towards a low-cost, easy-to-use, portable 3D motion analysis system for children and young adults.

Mana Yamamoto,1 Teiji Yagasaki,1,2 Sho Ichikawa,1 Takuto Nakamura,3,4 Yuumi Konishi,5 Yoshimi Yokoyama1 1Department of Ophthalmology, Japan Community Healthcare Organization Chukyo Hospital, Nagoya, Aichi, Japan; 2Yagasaki Eye Clinic, Ichinomiya, Aichi, Japan; 3Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; 4Japan Society for the Promotion of Science, Tokyo, Japan; 5Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo, JapanCorrespondence: Mana Yamamoto, Department of Ophthalmology, Japan Community Healthcare Organization Chukyo Hospital, 1-1-10 Sanjo Minami-ku, Nagoya, Aichi, 457-8510, Japan, Tel +81-52-691-7151, Fax +81-52-692-5220, Email yama357moto@gmail.comObjective: To determine the reliability of the Kinect-based semi-automatic scoring method (KSSM) using Kinect for Windows v2 for head posture compared to the cervical range-of-motion (CROM) device.Methods and Analysis: Head positions between − 40° and +40° of chin up/down (X), head turn (Y), and lateral tilt (Z) were measured in 10° increments in healthy volunteers. Their head positions were simultaneously measured using the KSSM and CROM. The following four points were analyzed: the success rate of the KSSM, the correlation between the two methods, the comparison of results by 95% limits of agreement (LA), and proportional error at 95% LA.Results: The measurability of the KSSM for all positions within ± 30° of the X, Y, and Z axes was 100%. The correlations for both methods were 0.979 (95% CI: 0.967– 0.987), 0.985 (0.976– 0.991), and 0.988 (0.981– 0.993) for the X-, Y-, and Z-axes, respectively. The simple linear regression analysis equations for 95% LA were Y=− 0.024X-0.452 for X axes, Y=0.024X-0.363 for Y axes, and Y=− 0.045X+0.217 for Z axes (95% confidence interval for each axis: − 0.055– 0.007, − 0.006– 0.050, and − 0.071– 0.018). However, the proportional biases were small because the predictive values of the differences in head positions from − 40° to 40° determined by the equations were within ± 5° for chin up/down and within ± 3° for head tilt.Conclusion: Head posture measurements using the KSSM and CROM were found to be similar when used in clinical settings.Keywords: anomalous head posture, Kinect, CROM, spine, reliability

Plant height can be used for assessing plant vigor and predicting biomass and yield. Manual measurement of plant height is time-consuming and labor-intensive. We describe a method for measuring maize plant height using an RGB-D camera that captures a color image and depth information of plants under field conditions. The color image was first processed to locate its central area using the S component in HSV color space and the Density-Based Spatial Clustering of Applications with Noise algorithm. Testing showed that the central areas of plants could be accurately located. The point cloud data were then clustered and the plant was extracted based on the located central area. The point cloud data were further processed to generate skeletons, whose end points were detected and used to extract the highest points of the central leaves. Finally, the height differences between the ground and the highest points of the central leaves were calculated to determine plant heights. The coefficients of determination for plant heights manually measured and estimated by the proposed approach were all greater than 0.95. The method can effectively extract the plant from overlapping leaves and estimate its plant height. The proposed method may facilitate maize height measurement and monitoring under field conditions.

Abstract Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was developed rather than one weak classifier. The ensemble approach based on SVMs was proposed for the high-dimensional data to gather the advantages of stacking and bagging. To address the problem of an imbalanced dataset, a hybrid strategy combining three separate techniques was used. Model robustness and stability was evaluated using fivefold cross-validation. The results showed that the classifier is robust, stable and performs well for different train and test samples. The study demonstrates that FAUs acquired by the Kinect sensor can be used in classifying FP. The developed FP assessment and classification system provides a detailed quantitative report and has significant advantages over existing grading scales.

This paper proposes the author's design of a mobile robot intended for creating a two-dimensional map of the environment. The first part of the paper describes the scope of automated systems called robots. On this basis, the design assumptions of the author's robot were defined, the components used in the construction are shown, the operation of the Robot Operating System (ROS) and the packages used in this robot control system are presented. The main idea, which was to govern the project was its low manufacturing cost. Thus, the use of the optical system of the Kinect device. As a result. the operation of the algorithm controlling the robot via the ATmega328 microcontroller is demonstrated. The results of the experimental verification of the robot's performance are presented in the form of two-dimensional maps of the rooms in which the experiments were carried out. [ABSTRACT FROM AUTHOR]

IntroductionIndividuals with Parkinsonian disorders often face limited access to specialized physiotherapy and movement training due to staff shortages and increasing disease incidence, resulting in a rapid decline in mobility and feelings of despair. Addressing these challenges requires allocating adequate resources and implementing specialized training programs to ensure comprehensive care and support. Regarding these problems, a computer software was invented that might serve as an additional home-based extension to conventional physiotherapy.MethodsThe trial took place in a rehabilitation center where every patient received equivalent treatment apart from the training program that was set up to be investigated over 3 weeks. Seventy four Patients were included and randomized between two intervention and one control group. Intervention group 1 (IG1) trained with the computer-based system two times a week while Intervention group 2 (IG2) received five training sessions a week. Using the markerless Microsoft Kinect® camera, participants controlled a digital avatar with their own body movements. UPDRS-III and Clinical measurements were performed before and after the three-week period.ResultsPatients in all groups improved in UPDRS-III pre and post intervention whereas reduction rates were higher for IG1 (−10.89%) and IG2 (−14.04%) than for CG (−7.74%). Differences between the groups were not significant (value of ps CG/IG1 0.225, CG/IG2 0.347). Growth rates for the arm abduction angle were significantly higher in IG1 (11.6%) and IG2 (9.97%) than in CG (1.87%) (value of ps CG/IG1 0.006 and CG/IG2 0.018), as was the 5-steps-distance (CG 10.86% vs. IG1 24.5% vs. UG2 26.22%, value of ps CG/IG1 0.011 and CG/IG2 0.031).DiscussionThe study shows the beneficial effects of computer-based training and substantiates the assumption of a similar impact in a home-based setting. The utilized software is feasible for such interventions and meets with the patient’s approval. Group dynamics seem to have an additional supporting effect for the aspired objective of improving mobility and should be seen as an essential aspect of video games in therapy.

There is a phenomenon of attaching importance to technique and neglecting emotion in the training of sports dance (SP), which leads to the lack of integration between movement and emotion and seriously affects the training effect. Therefore, this article uses the Kinect 3D sensor to collect the video information of SP performers and obtains the pose estimation of SP performers by extracting the key feature points. The Arousal-Valence (AV) emotion model, based on the Fusion Neural Network model (FUSNN), is also combined with theoretical knowledge. It replaces long short term memory (LSTM) with gate recurrent unit (GRU), adds layer-normalization and layer-dropout, and reduces stack levels, and it is used to categorize SP performers’ emotions. The experimental results show that the model proposed in this article can accurately detect the key points in the performance of SP performers’ technical movements and has a high emotional recognition accuracy in the tasks of 4 categories and eight categories, reaching 72.3% and 47.8%, respectively. This study accurately detected the key points of SP performers in the presentation of technical movements and made a major contribution to the emotional recognition and relief of this group in the training process.

Introduction: Balance impairment is an important indicator to a variety of diseases. Early detection of balance impairment enables doctors to provide timely treatments to patients, thus reduce their fall risk and prevent related disease progression. Currently, balance abilities are usually assessed by balance scales, which depend heavily on the subjective judgement of assessors.Methods: To address this issue, we specifically designed a method combining 3D skeleton data and deep convolutional neural network (DCNN) for automated balance abilities assessment during walking. A 3D skeleton dataset with three standardized balance ability levels were collected and used to establish the proposed method. To obtain better performance, different skeleton-node selections and different DCNN hyperparameters setting were compared. Leave-one-subject-out-cross-validation was used in training and validation of the networks.Results and Discussion: Results showed that the proposed deep learning method was able to achieve 93.33% accuracy, 94.44% precision and 94.46% F1 score, which outperformed four other commonly used machine learning methods and CNN-based methods. We also found that data from body trunk and lower limbs are the most important while data from upper limbs may reduce model accuracy. To further validate the performance of the proposed method, we migrated and applied a state-of-the-art posture classification method to the walking balance ability assessment task. Results showed that the proposed DCNN model improved the accuracy of walking balance ability assessment. Layer-wise Relevance Propagation (LRP) was used to interpret the output of the proposed DCNN model. Our results suggest that DCNN classifier is a fast and accurate method for balance assessment during walking.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder known for its significant heterogeneity and varied symptom presentation. Describing the different subtypes as predominantly inattentive (ADHD–I), combined (ADHD–C), and hyperactive–impulsive (ADHD–H) relies primarily on clinical observations, which can be subjective. To address the need for more objective diagnostic methods, this pilot study implemented a Microsoft Kinect-based Stroop Color–Word Test (KSWCT) with the objective of investigating the potential differences in executive function and motor control between different subtypes in a group of children and adolescents with ADHD. A series of linear mixture modeling were used to encompass the performance accuracy, reaction times, and extraneous movements during the tests. Our findings suggested that age plays a critical role, and older subjects showed improvements in KSWCT performance; however, no significant divergence in activity level between the subtypes (ADHD–I and ADHD–H/C) was established. Patients with ADHD–H/C showed tendencies toward deficits in motor planning and executive control, exhibited by shorter reaction times for incorrect responses and more difficulty suppressing erroneous responses. This study provides preliminary evidence of unique executive characteristics among ADHD subtypes, advances our understanding of the heterogeneity of the disorder, and lays the foundation for the development of refined and objective diagnostic tools for ADHD.

Facioscapulohumeral Dystrophy (FSHD) results in slowly progressive strength impairment, especially the upper extremities. Recent discoveries regarding pathophysiology have led to exciting novel therapeutic strategies. To further facilitate drug development, improved FSHD outcome measures that are functionally-relevant and sensitive to longitudinal change will be critical. Recently, a motion sensor (Kinect)-based upper extremity outcome called 'reachable workspace' that provides a quantitative reconstruction of an individual's reachability was developed. In this study, changes in reachable workspace were tracked upwards for five-years in 18 FSHD subjects. Results show -1.63 %/year decline in total reachable workspace (p = 0.144); with most notable decline in the above-the-shoulder level quadrants (upper-lateral Q3: -9.5 %/year, p

Mild Cognitive Impairment (MCI) in pensioners has become an important concern in the aging population, and there are an increasing number of products, especially virtual reality (VR)-based products, to assist in the identification, intervention, and treatment of MCI older adults. Multiple studies on the efficacy and usability of VR products are also receiving increased attention from designers. However, issues such as the efficacy testing of VR products still face serious challenges. In this study, we evaluated VR products looking at the interactive responsiveness of MCI older adults when using VR products, and analyzed the brain activation status and behavioral conditions of MCI older adults. Multisource data were generated by a functional near-infrared spectroscopy (fNIRS) device with high spatial resolution and a behavioral recording device reflecting motor abilities. Small-wave amplitudes were selected as indicators of brain activation to analyze six brain areas: LPFC, RPFC, LOL, ROL, LMC, and RMC. Eight aspects, such as overall upper-limb speed, upper-limb global acceleration, and median velocity, were selected as indicators for behavioral recording. The differences were observed by comparing the level of completion of interactive responses by MCI older adults between the two groups. The results showed that MCI older adults showed different levels of activation in brain regions when performing VR product-based tasks. The higher the level of cognition, the better the interactive response in the task and the stronger the activation of brain regions. Meanwhile, the level of interaction response had a significant correlation with the motor performance of MCI older adults, with stronger motor functions leading to a more effective interaction response to the product. This study proposes a new method to evaluate the feasibility of monitoring the interaction between the MCI elderly and VR-based products using fNIRS with Kinect, which provides a new way to evaluate the effectiveness of VR-based product-assisted treatment. [ABSTRACT FROM AUTHOR]

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence. [ABSTRACT FROM AUTHOR]

This research shows the development of a teleoperation system with an assistive robot (NAO) through a Kinect V2 sensor, a set of Meta Quest virtual reality glasses, and Nintendo Switch controllers (Joycons), with the use of the Robot Operating System (ROS) framework to implement the communication between devices. In this paper, two interchangeable operating models are proposed. An exclusive controller is used to control the robot's movement to perform assignments that require long-distance travel. Another teleoperation protocol uses the skeleton joints information readings by the Kinect sensor, the orientation of the Meta Quest, and the button press and thumbstick movements of the Joycons to control the arm joints and head of the assistive robot, and its movement in a limited area. They give image feedback to the operator in the VR glasses in a first-person perspective and retrieve the user's voice to be spoken by the assistive robot. Results are promising and can be used for educational and therapeutic purposes. [ABSTRACT FROM AUTHOR]

Background: We developed the Blexer system consisting of a database and a web interface for therapists that can host different types of adaptive and personally configurable virtual reality exergames based on Kinect (Microsoft Corp) motion capture to provide entertaining exercises for children with motor disabilities. It allows for parameter adjustment and the monitoring of results remotely, thereby providing a useful tool to complement traditional physical therapy sessions with home exercises. Objective: The aim of this study was to observe the motor benefits achieved through the use of a video exergame and the importance and implications of correctly setting the game's difficulty parameters. Methods: This was an observational case study of 6 children with different physical disabilities receiving physical therapy at school combined with the use of a fully configurable exergame under research that forms a part of the Blexer environment. The game integrates 4 repeatedly appearing upper limb exercises with individually adjustable difficulties (intermittent arm rising, arm forward and backward movement, rising and holding of one arm, and trunk control in all directions). The outcomes were 3 assessments of 2 efficacy measures: Box and Block Test and Jebsen Taylor Hand Function Test. Results: A total of 5 children with cerebral palsy (mean 8.4, SD 2.7 years; Gross Motor Function Classification II--2/5,40%; in--2/5,40%; and IV--1/5,20%) and 1 child with obstetric brachial plexus palsy (aged 8 years; Mallet Classification III) received between 8 and 11 sessions of training (10-20 minutes per session), depending on age, motivation, and fatigue. Significant associations were observed between game parameter settings and improvements in motor function, on the one hand, and between the type of improvement and disability severity, on the other: with adjusted game parameters goal and time in the range of 70% to 100%, only less affected children improved in the Box and Block Test (+11 blocks vs -1 block), and more affected children improved more in the Jebsen Taylor Hand Function Test (+90 seconds vs +27 seconds). Conclusions: When defining the difficulty parameters for an exergame, we suggest a classification in levels ranging from very easy to very hard. For practical use, we suggest setting the difficulty for the player to an easy or medium level rather than high-commitment goals, as this leads to a longer playtime with more fun and, therefore, seems to improve the results of the game and, consequently, mobility. [ABSTRACT FROM AUTHOR]

Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation. [ABSTRACT FROM AUTHOR]

Accuracy and Correlation of the Kinect-Based Semi-Automatic Scoring Method for Measuring Anomalous Head Posture as Compared to the CROM® Device For all axes, although the measurable rates in this study were 100% within 30° angles of the head position, there was a decrease in the rates for a 40° angle of the head position. Recently, the use of electronic AHP measuring devices with head-mounted motion trackers or an infrared head tracker has been reported to achieve more accurate head posture measurements.[13],[14] However, both methods require head-mounted equipment, which risks altering the head posture and restricts its use to adults and compliant children. When the head position of the CROM device indicator showed an angle of 0°, it was set as the 0° head position for the KSSM device. [Extracted from the article]