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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]

Language is a key resource for speakers and signers to index different aspects of their social identities, such as their ethnicities and sexualities. Yet, for users of sign language – who exploit movements of the hands, face, head and torso for linguistic purposes – it is often assumed that any communicative movement of the body is part of a sign language rather than a general feature of the body's potential to communicate social meaning, shared by hearing and deaf individuals. In this study, we test this claim by comparing the movement features produced by gay and straight Israeli Sign Language signers to the gestural movements produced by gay and straight Hebrew speakers. The findings reveal that deaf gay signers and hearing gay gesturers exploit similar movements of the body. By incorporating the notion of embodiment into sign language sociolinguistics, we can better conceptualize the relationship between sign language and social identities. [ABSTRACT FROM AUTHOR]

Introduction: Our recent reports have shown that movement pattern and displacement assessed with an infrared depth sensor during a 20-s stepping-in-place test (ST) correlates with measures of balance and need for assistance in older individuals. This study investigated ST test-retest reliability in community-dwelling older adults with and without supportive care. Methods: Two groups were included: those not participating (HO: n = 25, 74.7 ± 5.2 years) and those participating (DSU: n = 41, 78.8 ± 5.8 years) in regular senior day services (supportive care). HO completed three ST trials, separated by 1 week, while DSU completed two ST trials during the same half-day supportive care visit. Testing was conducted with eyes open. ST measures included head movement path distance (TMD), maximum movement displacement (MMD), knee movement path length (KMD), and stepping rate (STEP). TMD×KMD−1 ratio indicated upper-body sway relative to lower-body. Test-retest reliability (intra-class correlation coefficients, ICCs) and between-trial and between group differences (ANCOVA, adjusting for age) were assessed. Results: After adjusting for age, TMD, KMD, TMD×KMD−1 were lesser and STEP was greater in HO than DUS. HO ST variables did not differ across testing days. HO ICC (1, 3) for TMD (0.911 [95% confidence interval: 0.827–0.958]), MMD (0.918 [0.814–0.961]), KMD (0.838 [0.685–0.923]), and TMD×KMD−1 (0.940 [0.884–0.872]) showed strong to very strong reliability. Similarly, DSU ST variables did not differ across same day trials and ICC (1, 2) for TMD, KMD, and TMD×KMD−1 displayed very strong reliability. Conclusion: These ST variables exhibited excellent test-retest reliability of discriminating between community-dwelling older adults with and without supportive care. [ABSTRACT FROM AUTHOR]

Industry 5.0 emphasises collaborative work between humans, advanced technology, and artificial intelligence robots, focusing on human-centric principles and integrating flexibility and sustainability to enhance workflows. The advancement of human-robot interaction services can significantly improve the operations efficiency of digital twin manufacturing cells. Motivated by the above background, a gesture-driven interaction architecture for digital twin manufacturing cells is proposed, including data acquisition layer, data processing layer, and application service layer. Secondly, deep learning algorithms are employed for recognising predefined gestures. An improved YOLOv5 algorithm is used to solve the problem of low accuracy in static gesture recognition; while a 3D-CNN-based multimodal data fusion algorithm is used to solve the problem of continuity, diversity, and dimensionality in dynamic gesture recognition. Ultimately, the prototype system is developed utilising Kinect 2.0 and Unity 3D, which involves linking the gesture recognition to the digital twin model, and linking the digital twin model to the physical manufacturing cells. This study is expected to provide theoretical and practical insights to empower human-robot interaction technology in manufacturing cells. [ABSTRACT FROM AUTHOR]

In order to enable rescue robots to accurately detect human joint points in dangerous building environments to replace rescuers into dangerous scenes. A machine learning based human joint point detection method was proposed. Firstly, human joint detection data was obtained through Kinect cameras, and normalized methods were used to smooth and preprocess the joint data. Secondly, human joint length constraints and joint rotation angle constraints were added to the preprocessed data to generate the spatial positions of each human joint point. Finally, the Tsai method was used for hand eye calibration to obtain the distance between the robotic arm and the injured person. The experimental results show that the method accurately measures the position of the casualty joint point in the rescue environment of a dangerous building, which verifies the effectiveness of the proposed detection method. [ABSTRACT FROM AUTHOR]

This study aimed to identify quantitative biomarkers of motor function for cerebellar ataxia by evaluating gait and postural control using an RGB-depth camera-based motion analysis system. In 28 patients with degenerative cerebellar ataxia and 33 age- and sex-matched healthy controls, motor tasks (short-distance walk, closed feet stance, and stepping in place) were selected from a previously reported protocol, and scanned using Kinect V2 and customized software. The Clinical Assessment Scale for the Assessment and Rating of Ataxia (SARA) was also evaluated. Compared with the normal control group, the cerebellar ataxia group had slower gait speed and shorter step lengths, increased step width, and mediolateral trunk sway in the walk test (all P < 0.001). Lateral sway increased in the stance test in the ataxia group (P < 0.001). When stepping in place, the ataxia group showed higher arrhythmicity of stepping and increased stance time (P < 0.001). In the correlation analyses, the ataxia group showed a positive correlation between the total SARA score and arrhythmicity of stepping in place (r = 0.587, P = 0.001). SARA total score (r = 0.561, P = 0.002) and gait subscore (ρ = 0.556, P = 0.002) correlated with mediolateral truncal sway during walking. These results suggest that the RGB-depth camera-based motion analyses on mediolateral truncal sway during walking and arrhythmicity of stepping in place are useful digital motor biomarkers for the assessment of cerebellar ataxia, and could be utilized in future clinical trials. [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]

In today's digital era, human-computer interaction interfaces evolve and increase together with the needs of the users. However, the existing technologies have their limitations, which can hinder the efficiency of modern input devices like the Kinect sensor or other similar sensors. In this paper we improved our previous algorithm by extending it with two algorithms that aim to help telerehabilitation for individuals with movement disabilities. These two algorithms are based on the k-Nearest Neighbors, the k-Means algorithms. The algorithms are designed to accommodate the needs of the patients by adapting to their gestures based on their previous three. Using these gestures, the algorithms create multiple gesture acceptance domains around each coordinate of the gesture. Consequently, they decide whether the next user-input gesture can be considered the same movement. The accuracy of these algorithms was evaluated in three acceptance domains by comparing gesture descriptors with either the Euclidean or the Manhattan distance calculation methods. The results show that k-Nearest Neighbors algorithm yields better results in larger acceptance domains, while the k-Means algorithm can provide a better gesture acceptance rate in the smaller ones. The results show that both algorithms can be used in the telerehabilitation process, although the k-Means algorithm is more accurate than the k-Nearest Neighbors algorithm. [ABSTRACT FROM AUTHOR]

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]

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]

BACKGROUND: Evaluation of the working postures and development of new techniques are paramount in reducing the awkward postures and occurrence of musculoskeletal disorders (MSDs). The Kinect sensor, a portable and cost-effective device, appears to be a promising alternative to study work postures. OBJECTIVE: The current study aimed to evaluate the validity of Kinect against the gold-standard instrument (electro-goniometers) for body joint angle measurements. METHODS: A unique software application was developed to measure the critical body joint angles for postural evaluation by using the Kinect's skeletal tracking feature. The body joint angle data of ten volunteers were measured simultaneously by both Kinect and electro-goniometers. The validation analysis was conducted in both static and dynamic domains of application. RESULTS: Minimal variation was observed between the two techniques, and the Kinect correlated well for upper-arm joint angles of 45 ∘ , 60 ∘ and 90 ∘ ; lower-arm joint angles of 30 ∘ , 45 ∘ , 60 ∘ , and 90 ∘ ; straight neck position, neck joint angle at maximum possible flexion; straight trunk position, trunk bend angle at full flexion. In dynamic analysis, four out of five ICC values were > 0.75 except for the upper arm. Discrepancies in the results indicated the disapproval of Kinect for only wrist measurements. CONCLUSION: The results of the static and dynamic studies gave a sufficient basis to consider the Kinect tool as an alternative to contemporary posture-based ergonomic evaluation methods. [ABSTRACT FROM AUTHOR]

In this article, we propose a three-dimensional (3D) face recognition approach for depth data captured by Kinect based on a combination of speeded up robust features (SURF) and k-nearest neighbor (KNN) algorithms. First, the shape index maps of the preprocessed 3D faces of the training gallery are computed, then the SURF feature vectors are extracted and used to form the dictionary. In the recognition process, we propose an improved KNN classifier to find the best match. The evaluation was performed using CurtinFaces and KinectFaceDB data sets, achieving rank-1 recognition rates of 96.78% and 94.23%, respectively, when using two samples per person for training. [ABSTRACT FROM AUTHOR]

Epidemics are severe around the world, and people must wear face masks and authenticate in and out of public places to reduce the risk of virus transmission. However, wearing a face mask cannot detect facial features and affect the accuracy of the recognition system, so limitations need to be overcome. Considering that identity recognition does not rely solely on facial information, we use KINECT to get a wider range of skeleton information for judgment. At the same time, it is proposed to replace the diffractive optical elements of the KINECT sensor with a holographic optical element. The problem of uneven intensity and distortion of the light field can be improved to improve the accuracy of skeleton detection. Next, clear scattering information and images are provided to MTCNN and FaceNet models for whole-body imaging identification and pre-training. Besides, we only keep the skeleton features to represent the standing posture and ignore the difference in appearance, which can reduce the changing appearance features. The experimental results show that the proposed method and model with the improved KINECT can solve the problem that is difficult to identify with the mask, and its accuracy is 99.5%. [ABSTRACT FROM AUTHOR]

Based on building the overall architecture of virtual assembly and processing on the Unity3D platform, to make the operation more immersive, this paper adds the related functions of Kinect somatosensory equipment on the basis of the original functions, realizes some functions of this system through gesture operation, and develops a virtual simulation system for metal cutting petroleum machinery assembly and processing based on this. The application results show that the application of human-computer interaction technology has significantly improved. Through several groups of experimental data, it is verified that the accuracy of gesture recognition is not 100%. Through SVM algorithm, it is verified that the angle between the middle finger and the thumb is detected with error, especially when the hand is moving. [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. [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]

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors' mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained 0.29 – 0.36 fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between 0.01 and 0.05 on average and the spectral angle between 1.5 and 3.2 degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker. [ABSTRACT FROM AUTHOR]

Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called "principal movements" (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM1, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM3, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion. [ABSTRACT FROM AUTHOR]

The multi-fingered dexterous robotic hand is increasingly used to achieve more complex and sophisticated human-like manipulation tasks on various occasions. This paper proposes a hybrid Surface Electromyography (SEMG) and Kinect-based human in-hand motion (HIM) capture system architecture for recognizing complex motions of the humans by observing the state information between an object and the human hand, then transferring the manipulation skills into bionic multi-fingered robotic hand realizing dexterous in-hand manipulation. First, an Adaptive Directed Acyclic Graph (ADAG) algorithm for recognizing HIMs is proposed and optimized based on the comparison of multi-class support vector machines; second, ten representative complex in-hand motions are demonstrated by ten subjects, and SEMG and Kinect signals are obtained based on a multi-modal data acquisition platform; then, combined with the proposed algorithm framework, a series of data preprocessing algorithms are realized. There is statistical symmetry in similar types of SEMG signals and images, and asymmetry in different types of SEMG signals and images. A detailed analysis and an in-depth discussion are given from the results of the ADAG recognizing HIMs, motion recognition rates of different perceptrons, motion recognition rates of different subjects, motion recognition rates of different multi-class SVM methods, and motion recognition rates of different machine learning methods. The results of this experiment confirm the feasibility of the proposed method, with a recognition rate of 95.10%. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, a bottle counting system is developed based on processing of depth images. 3D depth images are captured by Kinect-2 sensor, which was mounted at a facility recycling line. Based on the facility's demand, three different types of bottles were considered in the bottle counting system. Boxes contain either 20 or 30 bottles depending on the bottle type. The facility recycling line moves at 1 m/s velocity and at least one box crosses from facility line per second. The bottle counting system consists of an image processing algorithm for processing captured depth images. The first step of the algorithm includes camera calibration routine so as to convert sensor measurements from camera frame to a fixed world frame. Once the measurement is triggered, morphological operations are applied to the binary image to remove noise and close the gaps. Connected component analysis, min bounding rectangle is performed to detect objects in the image. The cycle time for the developed image processing scheme is 45 ms: 33 ms for image capturing (30 fps), 5 ms for the pre-processing and 7ms for the bottle detection steps. It is observed from the results that the developed algorithm can count the bottles with 99% accuracy for each bottle type. [ABSTRACT FROM AUTHOR]

Children sometimes show positive emotions in response to seeing others being helped, yet it remains poorly understood whether there is a strategic value to such emotional expressions. Here, we investigated the influence seeing a peer receive deserving help or not on children's emotions, which were assessed while the peer was present or not. To measure children's emotional expression, we used a motion depth sensor imaging camera, which recorded children's body posture. Five‐year‐old children (N = 122) worked on a task which yielded greater rewards for them compared to their peer, rendering the peer to be in greater need of help. An adult—who was unaware of the different levels of neediness—then either helped the child who had a lesser need for help (less deserving outcome) or helped the needier peer (deserving outcome). Overall, both children showed a lowered body posture, a more negative emotional expression, after not being helped and an elevated body posture, a more positive emotional expression, after being helped. Seeing their peer (less deservedly) not receive help, and to a lesser extent being observed, blunted children's otherwise positive emotions in response to receiving help. These results are discussed in the broader theoretical context of how children's emotions sometimes reflect their commitment to cooperative relationships with peers. [ABSTRACT FROM AUTHOR]

Musculoskeletal disorders (MSDs) are associated with sitting postures. The assessment and prevention of risk factors for workplace exposure are indispensable aspects of reducing the occurrence of MSDs. This paper proposes an ergonomic assessment method of risk factors for MSDs associated with sitting postures in the actual working conditions. A Kinect sensor with the RULA method was primarily used to collect the data and evaluate the relevant postures. The results obtained were compared with the evaluation results by a human expert. Additionally, we verified the capability and effectiveness of this method. A program system for human joint recognition and acquisition was implemented. The results indicated that the Kinect joint data is generally accurate and can adequately complete the RULA evaluation table. The results from the front and right-hand side obtained by the Kinect were consistent with the results of the expert evaluation, and no significant difference was observed between them (p > 0. 0 5). However, when the participants faced the Kinect, the sensor performed better, and the evaluation result was more accurate. A high consistency was observed between the evaluation results obtained from the front and the expert (proportion agreement index = 0. 6 5 , Cohen's kappa = 0. 7 7). Only a slight consistency was observed between the evaluation results obtained from the right-hand side and the expert (proportion agreement index = 0. 4 1 , Cohen's kappa = 0. 0 8). This research created a new ergonomic method for the risk assessment of MSDs associated with sitting postures. The combination of theory and practice is crucial in the risk assessment of sitting postures in workplaces. [ABSTRACT FROM AUTHOR]

Background: Gait analysis is burdened by time and equipment costs, interpretation, and accessibility of three-dimensional motion analysis systems. Evidence suggests growing adoption of gait testing in the shift toward evidence-based medicine. Further developments addressing these barriers will aid its efficacy in clinical practice. Previous research aiming to develop gait analysis systems for kinetics estimation using the Kinect V2 have provided promising results yet modified approaches using the latest hardware may further aid kinetics estimation accuracy RESEARCH QUESTION: Can a single Azure Kinect sensor combined with a musculoskeletal modeling approach provide kinetics estimations during gait similar to those obtained from marker-based systems with embedded force platforms?Methods: Ten subjects were recruited to perform three walking trials at their normal speed. Trials were recorded using an eight-camera optoelectronic system with two embedded force plates and a single Azure Kinect sensor. Marker and depth data were both used to drive a musculoskeletal model using the AnyBody Modeling System. Predicted kinetics from the Azure Kinect-driven model, including ground reaction force (GRF) and joint moments, were compared to measured values using root meansquared error (RMSE), normalized RMSE, Pearson correlation, concordance correlation, and statistical parametric mapping RESULTS: High to very high correlations were observed for anteroposterior GRF (ρ = 0.889), vertical GRF (ρ = 0.940), and sagittal hip (ρ = 0.805) and ankle (ρ = 0.876) moments. RMSEs were 1.2 ± 2.2 (%BW), 3.2 ± 5.7 (%BW), 0.7 ± 0.1.3 (%BWH), and 0.6 ± 1.0 (%BWH) SIGNIFICANCE: The proposed approach using the Azure Kinect provided higher accuracy compared to previous studies using the Kinect V2 potentially due to improved foot tracking by the Azure Kinect. Future studies should seek to optimize ground contact parameters and focus on regions of error between predicted and measured kinetics highlighted currently for further improvements in kinetic estimations. [ABSTRACT FROM AUTHOR]

Obtaining accurate and objective assessments of an individual's personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee's personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual's personality through his or her movements and open up pathways for several research. [ABSTRACT FROM AUTHOR]

This paper proposes a human body orientation estimation method using the Kinect camera depth data. The input of our system consists of three one-dimensional distance-based signals which reflect the body’s surface contours of the human upper body portion, i.e., the upper chest, upper abdomen, and lower abdomen. Such signals are then normalized using their distances to achieve the same amount of the lower parts. All normalized signals are concatenated to provide a mix of contour features. We used Support Vector Regression (SVR) to classify the feature and Kalman Filter to estimate the continuous orientations instead of using discrete orientations. We also extend our work by adding human motion direction to the robust estimate of human body orientation when walking. We conducted two evaluation schemes, i.e., body orientation at static position and body orientation when moving. The experimental results show that our system achieves impressive results by achieving mean average of angle error (MAAE) of 0. 097 ∘ and 5. 82 ∘ for estimating body continuous orientation at static position and estimating body continuous orientation when moving, respectively. Therefore, it is very promising to be applied in real implementations. [ABSTRACT FROM AUTHOR]

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment. [ABSTRACT FROM AUTHOR]

Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the tracking limitations of the Kinect. [ABSTRACT FROM AUTHOR]

Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach. [ABSTRACT FROM AUTHOR]

The high rate of recurrence and residual symptoms of depression continuously motivate the search for new treatments addressing this challenging condition. In this single-case study, we reported the application of a cognitive rehabilitation skills training based on remote kinematics as a treatment for residual depression by targeting deficits of mental simulations of action. We found that the administration of Kinect-based rehabilitation training resulted in important improvements showed by significant mean baseline reductions (MBLR) of difficulty in imagining positive actions (MBLR= 54 %), negative emotions (MBLR = 36.3 %), cognitive flexibility (MBLR= 69.7 %), depressive symptoms (MBLR= 80%), and physical retardation (MBLR= 50 %). Similarly, improvements in positive affect (MBLR = 107 %) and vividness of motor imagery for positive actions were registered (MBLR= 100 %). We also found unique effects of our intervention such as reports of involuntary action simulations to distant stimuli or extended affordances. The training was well-accepted and the patient considered it was an entertaining way to do physical exercises and to get in physical and mental shape. We concluded that further scientific research of remote Kinematic interventions in depression may be warranted. [ABSTRACT FROM AUTHOR]

In this study, an image processing based volume measurement system is presented. The proposed scheme provides high accuracy, low cost and ease of calibration. There a r e n o additional requirements such as alignment of objects during measurement, positioning the objects at a certain position in the measuring field, and these advantages reduce the labor effort in real-time. In the first stage, a problem-specific calibration process was performed and then Hough transform was applied in the image processing phase. Although the proposed method has been developed primarily to measure the volume of box-type objects, it also provides an approximate volume measurement for objects of random geometry. The system tested under various external disturbances has produced satisfactory results. [ABSTRACT FROM AUTHOR]

To investigate the effectiveness of commercial exergames treatments on people with Parkinson's disease (PD), a systematic review and network meta-analysis (NMA) of randomised controlled trials (RCT) was performed to evaluate functional locomotion effects in patients with PD. A comprehensive search on PubMed, Embase, Cochrane Controlled Trials Register, the Cochrane, and other databases without language restrictions was conducted. References and reviews were cross-checked for possible studies. RCTs from 2012 were included. Seven studies with 236 participants were included in the NMA that used a random-effect Frequentist model to investigate the standard mean difference of pooled effect sizes change in functional locomotion between baseline and delayed retention tests on six comparisons. The direct comparisons Kinect: Control (n = 4), Wii: Control (n = 5) and the indirect-network comparison Kinect: Wii (n = 1) determine that Nintendo Wii™ may be the best choice in functional locomotion improvements. These network meta-analytic findings show that Kinect and Wii show immediate positive effects on functional locomotion in people with PD. Compared with Kinect RCTs, Wii may be considered the best therapy for providing functional recovery in PD. Exergames rehabilitation reveals intervention benefits for functional locomotion in people with PD. In patients with cognitive disorders, Wii's platform can be considered a visual reference and a cue to facilitate patients with freezing. Nintendo Wii seems to be a better device for locomotion rehabilitation in people with PD than Xbox Kinect. [ABSTRACT FROM AUTHOR]

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered. [ABSTRACT FROM AUTHOR]

Background: Motor impairment is widely acknowledged as a core feature in children with autism spectrum disorder (ASD), which can affect adaptive behavior and increase severity of symptoms. Low-cost motion capture and virtual reality (VR) game technologies hold a great deal of promise for providing personalized approaches to motor intervention in ASD. The present study explored the feasibility, acceptability and potential efficacy of a custom-designed VR game-based intervention (GaitWayXR™) for improving gross motor skills in youth with ASD.Methods: Ten children and adolescents (10-17 years) completed six, 20-min VR-based motor training sessions over 2 weeks while whole-body movement was tracked with a low-cost motion capture system. We developed a methodology for using motion tracking data to quantify whole-body movement in terms of efficiency, synchrony and symmetry. We then studied the relationships of the above quantities with standardized measures of motor skill and cognitive flexibility.Results: Our results supported our presumption that the VR intervention is safe, with no adverse events and very few minor to moderate side-effects, while a large proportion of parents said they would use the VR game at home, the most prohibitive reasons for adopting the system for home therapy were cost and space. Although there was little evidence of any benefits of the GaitWayXR™ intervention in improving gross motor skills, we showed several positive correlations between the standardized measures of gross motor skills in ASD and our measures of efficiency, symmetry and synchrony from low-cost motion capture.Conclusions: These findings, though preliminary and limited by small sample size, suggest that low-cost motion capture of children with ASD is feasible with movement exercises in a VR-based game environment. Based on these preliminary findings, we recommend conducting larger-scale studies with methods for improving adherence to VR gaming interventions over longer periods. [ABSTRACT FROM AUTHOR]

In this paper, several physical activity-based human–computer interaction (HCI) games which are developed and implemented for the improvement of attention, emotion, and sensory–motor coordination will be presented. The interface and the difficulty levels of these games are specially designed for the use of people with different age groups and disabilities. The games involve physical activities for the fulfillment of some basic HCI tasks which require hand and arm motion for control, such as fruit picking and air hockey, with adaptive difficulty levels based on varying parameters of the games and human performance. In the fruit picking game, several fruit images are moving from top to the bottom of the screen. Objective is to collect apples while avoiding the pears. The player's hand will control the basket that collects the fruits. In the air hockey game, the player will try to score goals against computer-controlled opponent. The player's hand will control the paddle to hit the puck to score or to defend his/her goal area. The player's hand is recognized by Kinect RGB-D sensors in both games. Aim of the adaptive difficulty-based system is keeping the players engaged in the games. The games are tested with a group of deaf children (3.5–5 years) as a part of an ongoing project,1 to decrease the stress of the children and increase their positive emotions, attention, and sensory–motor coordination before the audiology tests. The game performances and the evaluation of the therapists show that the games have a positive impact on the children. The games are also tested with a group of adults as a control group, where a mobile EEG device is employed to detect the attention levels. For this purpose, the adults also attended a third game featuring a maze and controlled with Myo sensors. [ABSTRACT FROM AUTHOR]

Background: Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson's disease (PD). The combination of depth camera and machine learning makes this purpose possible.Methods: Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors' MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale) scores. An objective function was implanted to further improve the human-machine consistency.Results: The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine's and doctors' score was 0.940 (95%CI, 0.905-0.962), meaning the machine was highly consistent with the doctors' judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity.Conclusions: We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD. [ABSTRACT FROM AUTHOR]

The present study aims to develop an automated ergonomic evaluation system, based on the OWAS method, for assessing a steel bar bending process performed at a construction company in Mexico. The system evaluates production operators' postures and movements by using Microsoft's Kinect sensor. To perform each ergonomic evaluation, the software processes information captured by the sensor, which detects movement of human body joints. The proposed methodology specifies the creation, development, and testing of the system during a repetitive operation, which is known historically for causing the highest number of injuries and work-related disabilities. The result is an automated tool capable of developing ergonomic evaluations in real time. Reliability and effectiveness is demonstrated statistically (95% confidence) by comparing the software's automated evaluation with that of an expert (traditional evaluation). It is concluded that the automated system developed here delivers ergonomic evaluations that yield reliable and safe results that can improve decisionmaking processes. [ABSTRACT FROM AUTHOR]

Kinect has been utilized as a cost-effective, easy-to-use motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks' positions restrict Kinect's capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion's kinematic parameters were calculated using the landmarks' positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin's concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460°) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843°). [ABSTRACT FROM AUTHOR]

An accurate modeling of three-dimensional (3D) point cloud has been a crucial step to extract the physical phenotyping parameters of plants, such as the plant height, leaf quantity, and leaf area. In this study, an automatic registration of point clouds with multiple calibration balls was proposed to realize the 3D modeling of plants under a complex background in the field. A low-cost depth sensor (such as Kinect V3) was also selected to capture the images. The performance of the registration was then evaluated using the multiple angles of several plants under the indoor and in-field scenes. Four procedures were included the point cloud filtering and down sampling, multiple calibration balls extraction, correspondence points matching, as well as the calculation and registration of transformation matrix. 1) The pass-through filtering with only several boundary thresholds was used to reduce the noises, while the bounding box compression was used to down sampling the point clouds. 2) A Random Sampling Consistency (RANSAC) was also used in the multiple calibration balls extraction. Furthermore, a concept of point cloud subtraction was proposed to combine with the RANSAC, in order to form an automatic extraction of multiple calibration balls. Among them, the RANSAC was utilized to extract one single object at one time. As such, the center coordinates of each calibration ball were then calculated as the featured points. 3) The distances of any two feature points were calculated in the correspondence points matching. An automatic matching of correspondence points using the distance information was also proposed to realize the self-matching of the calibration balls. 4) The singular value decomposition was adopted to solve the transformation (rotation and translation) matrix, and then the registration of two pieces of point clouds was realized eventually. The experiments were carried out in two scenes: one was the indoor scene with the flat and clean surface, and another was the field scene with the uneven and complex conditions. Five plants were chosen as the research objects, including one sugarcane (indoor), one sorghum (indoor), and three young banana plants (in-field). The Kinect V3 sensor was utilized to capture the point clouds of each object in five orientations (the horizontal orientation of 0º, 90º, 180º, 270º, and one vertical orientation). A commercial point cloud processing software named LiDAR360 was adopted for comparison with the man-aid registration. After that, the object point clouds (horizontal 90°, 180°, 270°, and vertical) of each plant was used to implement the registration into the coordinate system of the source point cloud (horizontal 0°). The registration performance was evaluated to calculate the axial errors and point errors of the transformed coordinates, as well as the source coordinates of the centers of all the calibration balls. The results showed that the registration of different point clouds was successfully implemented in the different orientations, no matter in the indoor or in-field environment. Particularly, the generated 3D model of the plant was clear in shape. Specifically, there were an average axial error of 5.8-17.4 mm and an average point position error of 13.1-28.9 mm for the registration in the different scenes, similar to the registration in the manual registration software LiDAR360. In the required calculation time, the automatic matching took about 50 s on average for the process from the extraction of calibration sphere to the registration, whereas, the LiDAR360 took about 150 s with the manual selection of the correspondence points, indicating the efficiency increased by 67%. Consequently, it can be widely expected to serve a high-precision and automatic registration of plant point clouds acquired by low-cost depth cameras in complex field environments. The finding can provide a low-cost feasible solution for the 3D modeling and the extraction of phenotyping parameters of field plants. [ABSTRACT FROM AUTHOR]

Classify human movement has become a technological necessity, where defining the position of a subject requires identifying the trajectory of the limbs and trunk of the body, having the ability to differentiate this position from other subjects or movements, which generates the need to have data and algorithms that help their classification. Therefore, the discriminant capacity of motion capture data in physical rehabilitation is evaluated, where the position of the subjects is acquired with the Microsoft Kinect and optical markers. Attributes of the movement generated with the Frenet Serret framework. Evaluating their discriminant capacity by means of support vector machines, neural networks, and k nearest neighbors algorithms. The obtained results present an accuracy of 93.5% in the classification with data obtained from the Kinect, and success of 100% for movements where the position is defined with optical markers. [ABSTRACT FROM AUTHOR]

In the field of human-computer interaction, the position information of human hands is often directly used in the interpretation of interactive instructions and the calculation of interactive results, therefore, high-precision real-time hand position detection is an important basis for non-contact and natural human-computer interaction.In order to solve the problem of fluctuation and error in the 3 D coordinates acquired by Kinect 2.0,a hand position detection algorithm based on mean processing of correlation points is proposed in this paper.Based on the depth information, the hand image is segmented using the human wrist as the segmentation threshold, and the human hand position information is processed by spatial and temporal average of the related points to improve the accuracy of position detection.The experimental results show that the algorithm based on correlation points mean processing is effective and the detection error is less than 5 mm, which can meet the basic requirements of human-computer interaction. [ABSTRACT FROM AUTHOR]

In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method with Kinect sensor. To comply with the external torque from the environment, this paper presents a sensorless admittance control approach in joint space based on an observer approach, which is used to estimate external torques applied by the operator. To deal with the tracking problem of the uncertain manipulator, an adaptive controller combined with the radial basis function NN (RBFNN) is designed. The RBFNN is used to compensate for uncertainties in the system. In order to achieve the prescribed tracking precision, an error transformation algorithm is integrated into the controller. The Lyapunov functions are used to analyze the stability of the control system. The experiments on the Baxter robot are carried out to demonstrate the effectiveness and correctness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Background: Active video games have been embraced for the rehabilitation of mobility and promotion of physical activity for persons post-stroke. This study seeks to compare carefully matched standard of care stepping activities, off-the-shelf (non-custom) active video games and custom active video games that are either self-paced or game-paced for promoting neuromuscular intensity and accuracy, cardiovascular intensity, enjoyment and perceived effort.Methods: Fifteen persons (ages 38-72) with mild to moderate severity in the chronic phase post-stroke (average 8 years) participated in a single group counter balanced repeated measures study. Participants were included if they were greater than 6 months post-stroke, who could walk 100 feet without assistance and stand unsupported for three continuous minutes. They were excluded if they had cardiac, musculoskeletal or neurologic conditions that could interfere with repeated stepping and follow instructions. In a single session located in a laboratory setting, participants executed for 8.5 min each: repeated stepping, the Kinect-light race game, two custom stepping games for the Kinect, one was repeated and self-paced and the other was random and game paced. Custom video games were adjusted to the participants stepping volume. Ten-minute rest periods followed the exercise during which time participants rested and completed the PACES an enjoyment questionnaire. Participants were instrumented with a metabolic cart and heart rate sensor for collection of cardiovascular intensity (METs and % of max HR) data. Stepping frequency, accuracy and pattern were acquired via video. Data were analyzed using a RMANOVA and post-hoc comparison with a Holm's/Sidak correction.Results: Neuromuscular intensity (repetitions) was significantly greater for the off-the-shelf and self-paced custom game, however accuracy was greater for the custom games. Cardiovascular intensity for all activities took place in the moderate intensity exercise band. Enjoyment (measured with a questionnaire and rankings) was greater for the custom active video games and rate of perceived exertion was lower for the custom active video games.Conclusions: Custom active video games provided comparable intensity but better accuracy, greater enjoyment and less perceived exertion than standard of care stepping activities and a carefully matched off-the-shelf (non-custom) video game. There were no differences between the game-paced and self-paced custom active video games.Trial Registration: NCT04538326. [ABSTRACT FROM AUTHOR]

Background: Postural balance can be considered a conjoined parameter of gross motor performance. It is acquired in early childhood and honed until adolescence, but may also be influenced by various conditions. A simplified clinical assessment of balance and posture could be helpful in monitoring motor development or therapy particularly in pediatric patients. While analogue scales are considered unprecise and lab-based force-plate posturography lacks accessibility, we propose a novel kinematic balance assessment based on markerless 3D sensor technology.Research Question: Can balance and posture be assessed by tracking kinematic data using a single 3D motion tracking camera and are the results representative of normal motor development in a healthy pediatric cohort?Methods: A proprietary algorithm was developed and tested that uses skeletal data from the Microsoft Kinect™ V2 3D motion capture camera to calculate and track the center of mass in real time during a set of balance tasks. The algorithm tracks the distance of the COM traveled over time to calculate a balance score (COM speed). For this study, 432 school children aged 4-18 years performed 5 balance tasks and the resulting balance scores were analyzed and correlated with demographic data.Results: Preliminary experiments demonstrated that the system was able to reliably detect differences in COM speed during different balance tasks. The method showed moderate correlation with age and sex. Athletic activity positively correlated with balance skill in the age group < 8 years, but not in older children. Body mass appeared not to be correlated with balance ability.Significance: This study demonstrates that markerless 3D motion analysis can be used for the clinical assessment of coordination and balance and could potentially be used to monitor gross motor performance at the point-of-care. [ABSTRACT FROM AUTHOR]

The Sit-to-Stand (STS) is a widely used test of physical function to screen older people at risk of falls and frailty and is also one of the most important components of standard screening for sarcopenia. There have been many recent studies in which instrumented versions of the STS (iSTS) have been developed to provide additional parameters that could improve the accuracy of the STS test. This systematic review aimed to identify whether an iSTS is a viable alternative to a standard STS to identify older people at risk of falling, frailty, and sarcopenia. A total of 856 articles were found using the search strategy developed, with 12 articles retained in the review after screening based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six studies evaluated the iSTS in fallers, five studies in frailty and only one study in both fallers and frailty. The results showed that power and velocity parameters extracted from an iSTS have the potential to improve the accuracy of screening when compared to a standard STS. Future work should focus on standardizing the segmentation of the STS into phases to enable comparison between studies and to develop devices integrated into the chair used for the test to improve usability. [ABSTRACT FROM AUTHOR]

The Microsoft Kinect is a motion‐sensing device that enables users to interact with a computer through body movements. The Kinect was initially developed for video game users (e.g., Microsoft Xbox) although another important use may be as an automated system for measuring human behavior. To demonstrate the potential utility of the Kinect for behavioral measurement, we asked adults to perform various repetitive behaviors while in view of the Kinect sensor. Data collected from Kinect was analyzed via the Matrix Laboratory (MATLAB) program generating frequency of occurrences of repetitive behavior. To assess validity of the automated recording measurement, Kinect data were compared to frequency data obtained via direct human observation. Overall, there was a high‐level of agreement (92%) between measurement procedures (automated vs. human), although the automated system tended to result in slightly lower frequencies (false negatives) than was captured via human observation. Based on these findings, the Kinect technology showed solid potential as a tool for the automatic measurement of behavior. Suggestions for methodical refinement and future evaluation are discussed. [ABSTRACT FROM AUTHOR]

This work deals with the likelihood of merging a 3D sensor into a robotic manipulator, with an objective to automatically detect, track and grasp an object, placing it in another location. To enhance the flexibility and easy functionality of the robot, MATLAB, a versatile and powerful programming language is used to control the robot. For this work, a common industrial task in many factories of pick and place is implemented. A robotic system consisting of an ABB IRB120 robot equipped with a gripper and a 3D Kinect for Windows camera sensor is used. The threedimensional data acquisition, image processing and some different parameters of the camera are investigated. The information in the image acquired from the camera is used to determine the robot's working space and to recognize workpieces. This information is then used to calculate the position of the objects. Using this information, an automatic path to grasp an object was designed and developed to compute the possible trajectory to an object in real time. To be able to detect the workpieces, object recognition techniques are applied using available algorithms in MATLAB's Computer Vision Toolbox and Image Acquisition Toolbox. These give information about the position of the object of interest and its orientation. The information is therefore sent to the robot to create a path through a server-to-client connection over a computer network in real time. [ABSTRACT FROM AUTHOR]