Search

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]

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]

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]

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]

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]

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]

The capturing and development of a real time 3D face recognition system is always a big challenge and attracts huge popularity nowadays. This is because, in most security systems employing face recognition, it is always crucial to capture live images and recognize them. This should save time and cost as well, and incredibly is acceptable across all areas of security concerns. This paper deals with the development of an entirely new 3D face recognition system which was developed in Jadavpur University with six subjects captured fully in real time. The main problem which we have tried to address in the present work is that, how various issues like camera calibration, alignment of the camera, distance of the camera from the subject affects the facial recognition rate. We will also analyze how facial registration helps to increase the recognition rates which have been imposed by the above factors. The problem is relevant, because, it's always a challenge to capture and correctly predict a real time system. The necessary problems needed for setting up a real time system is always a matter to be investigated by the researchers in face recognition domain. The main overview of our present proposed method is to capture the subjects in real-time taking into consideration all the calibration issues like camera alignment, distance of camera from the subject, and several other factors. We have also discussed how these factors affect the recognition rate. Once captured, we have tried to justify the varying recognition rate of the subjects due to the changes in calibration, alignment and other issues. Now, in order to improve the performances of the subjects, we have proposed a new 3D face registration algorithm termed as FaRegAvFM8, which was tested on the subjects from our database acquired in real-time, as well as on Frav3D and GavabDB databases. Our system attained a recognition rate of 95.83% after registration on frontal subjects using Haar wavelet as the feature extraction method, which depicts the robustness of the present system. Not only that, we have improved our recognition rate up to 96% using the Deep Convolutional Neural Network (DCNN). [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) geometrical information that depth maps contain is useful in many applications such as 3D reconstruction or simultaneous localization and mapping (SLAM). Kinect is widely used in depth image acquisition due to its low cost and good real-time performance. However, the quality of depth images obtained by Kinect is influenced by holes which make depth image inadequate for further applications. To suppress the influence of holes on a subsequent application, a fixing algorithm of Kinect depth image based on non-local means (NLM) is proposed in this paper. The holes in depth image are filled using the weights which are calculated on the corresponding gray image by distance factor and value consistent factor. And the experiment results demonstrate that the proposed method achieves good performance in both evaluation in metrics and subjectively visual effect. This research provides a solution idea for depth image fixing algorithm with low complexity. [ABSTRACT FROM AUTHOR]

Human Activity Recognition is the act of recognizing activities performed by humans in real-time. This can be done using video data or more advanced forms of data like- inertial, depth maps, or human skeletal joint trajectories. In this work, we perform human action recognition through skeletal joint tracking of the human body using a deep recurrent neural network. Our proposed method was then tested on two standard databases, namely UTD-MHAD and MSR- Daily Activity 3D-Datasets. The judgement on the efficiency of our proposed model was made by comparing it to various, recently published, State-Of-The-Art (SOTA) methods.The evaluations of our model show that our method performs well on both the datasets and achieves an accuracy of 99.07%, and 91%, on UTD-MHAD and MSR Daily Activity databases respectively, and can recognize human activities from a variety of domains. [ABSTRACT FROM AUTHOR]

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]

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]

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers. [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: Frailty is common in Parkinson's disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention. Aims: To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system. Methods: PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group. Results: Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P = 0.005), hand grasping (P = 0.002), toe tapping (P = 0.002), and leg agility (P = 0.019) alongside reduced hand grasping speed (P = 0.030), lifting (P < 0.001) and falling speed (P < 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P = 0.048) and amplitude decrement rate (P = 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00–85.00%, 68.75–100%, and 0.701–0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637–0.913, P < 0.001) for upper limb tasks and 0.909 (95% CI 0.832–0.987, P < 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886–0.999, P < 0.001). Conclusions: Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients. [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]

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]

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]

Whether active gaming is an appropriate method to facilitate moderate-intensity physical activity in older adults remains unclear. The purpose of this study was to evaluate the intensity of physical activity and enjoyment while playing three active video games in older adults compared with younger adults. Ten younger and 10 older adults played three active games on separate days. Participants played two 15-min periods per game: one period at a self-selected intensity and one period with structured instructions to maximize the movement. Physical activity intensity and enjoyment were measured during gameplay. The results indicated that older adults played games at significantly higher intensities (5.3 + 1.8 vs. 3.6 + 1.8 metabolic equivalents), spent less time in whole-body sedentary activity, and rated games more enjoyable compared with younger adults. With physical activity intensity being consistent with moderate-to-vigorous intensity for older adults during gameplay, the results suggest that active video games could be used as a cardiovascular tool for older adults. [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]

Copyright of Health & Society is the property of Instituto de Ensino e Pesquisa Periodicojs and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Simultaneous Localization And Mapping (SLAM) is the ability of an autonomous robot to localize itself within the surroundings, while also constructing a map. It has several applications in a variety of industries, especially in indoor navigation, Autonomous driving, and robotics. This research uses an omnidirectional mobile robot to present the SLAM algorithm based on Extended Kalman Filter (EKF) in an indoor environment. Usually, SLAM combines data from odometry and other sensors, in order to overcome the accumulating error due to the wheel sliding that usually appears when using the odometry alone. Thus, in this study a Kinect sensor is used along with an odometer sensor in an unknown environment to reach a predefined point with a free collusion path using three main fuzzy controllers as part of its navigation system. The first fuzzy controller is responsible for the goal-seeking problem, where the robot moves towards the goal, the second and third controllers are responsible for static and dynamic obstacles avoidance. The robot changes its direction to avoid the static obstacles and its direction and speed to avoid the dynamic obstacles. An EV3 Lego robot is used to verify the effectiveness of the proposed study. Results shows the effectiveness of the used algorithm; the decentralized algorithm simplifies the control process, which decreases the error with the help of EKF. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Anterior cruciate ligament (ACL) injury is one of the most common knee injuries among adolescent athletes. Majority of the ACL injuries occur due to pivoting, sudden deceleration, and direction change without contact with any player. Preventive interventions can reduce risks of the ACL injury, thus developing a clinician friendly biomechanical assessment tool to identify athletes with such risk factors is crucial. In this study, the authors investigated the concurrent validity of a commercially available depth sensor, Microsoft Kinect, as a cost-effective alternative to the gold-standard 3-dimensional motion analysis systems in noncontact ACL screening for adolescent athletes during side-cut maneuvers. Study participants performed 45° side-cut maneuvers while collecting data from both systems concurrently. The sagittal and frontal plane kinematics were analyzed during the full stance phase and the first 20% of the stance (early deceleration). Absolute agreement (range: ICC =.767–.989) and consistency (range: ICC =.799–.992) were excellent for all measures except early deceleration frontal plane hip angle, which displayed good absolute agreement (ICC =.643) and consistency (ICC =.625). Findings showed that the Kinect has the potential to be an effective clinical assessment tool for sagittal and frontal plane trunk, hip, and knee kinematics during the side-cut maneuvers. [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]

Background: Exergames can provide encouraging exercise options. Currently, there is limited evidence regarding home-based exergaming in the postoperative phase of total knee replacement (TKR). Objective: This study aimed to investigate the effects of a 4-month postoperative home-based exergame intervention with an 8-month follow-up on physical function and symptoms among older persons undergoing TKR compared with home exercise using a standard protocol. In addition, a concurrent embedded design of a mixed methods study was used by including a qualitative component within a quantitative study of exergame effects. Methods: This was a dual-center, nonblinded, two-arm, parallel group randomized controlled trial with an embedded qualitative approach. This study aimed to recruit 100 patients who underwent their first unilateral TKR (aged 60-75 years). Participants were randomized to the exergame or standard home exercise arms. Participants followed a custom-made exergame program independently at their homes daily for 4 months. The primary outcomes at 4 months were function and pain related to the knee using the Oxford Knee Score questionnaire and mobility using the Timed Up and Go test. Other outcomes, in addition to physical function, symptoms, and disability, were game user experience, exercise adherence, physical activity, and satisfaction with the operated knee. Assessments were performed at the preoperative baseline and at 2, 4, and 12 months postoperatively. Exergame adherence was followed from game computers and using a structured diary. Self-reported standard exercise was followed for 4 months of intervention and physical activity was followed for 12 months using a structured diary. Qualitative data on patients' perspectives on rehabilitation and exergames were collected through laddering interviews at 4 and 12 months. Results: This study was funded in 2018. Data collection began in 2019 and was completed in January 2022. The COVID-19 pandemic caused an unavoidable situation in the study for recruitment, data collection, and statistical analysis. As of November 2020, a total of 52 participants had been enrolled in the study. Primary results are expected to be published by the end of 2022. Conclusions: Our study provides new knowledge on the effects of postoperative exergame intervention among older patients with TKR. In addition, this study provides a new understanding of gamified postoperative rehabilitation, home exercise adherence, physical function, and physical activity among older adults undergoing TKR. [ABSTRACT FROM AUTHOR]

Hereditary Amyloidosis associated with variant Transthyretin (ATTRv Amyloidosis) is a progressive and highly disabling neurological disorder that affects gait. Quantitative motion analysis is useful for assessing motor function, including gait, in diseases affecting movement. A single markerless RGB-D camera enables 3D full-body motion capture in a less expensive and intrusive, and more portable way than multi-camera marker-based systems. In this study, we examine whether a gait analysis system based on an RGB-D camera can be used to detect significant changes in the gait of ATTRv amyloidosis patients over time, when compared with a 12-camera system. We acquired 3D data provided by both systems from six ATTRv amyloidosis patients, while performing a simple gait task, once (T0) and 18 months later (T1). A direct comparison of systems has already been conducted. In this work, however, for each patient, we investigated if the RGB-D camera system detects statistically significant differences between the two different acquisitions in a similar way to the reference system, and whether it is reliable to use during patients' follow-up. The obtained results show that the differences detected between T0 and T1 for both systems follow the same tendency for 65% of the spatiotemporal gait parameters, and for 38% of the kinematic parameters (38%). The most reliable parameters were: stride duration/length, gait speed (and its variability), and arm/foot swing velocity, all with an almost perfect strength of agreement. [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]

This paper presents a modular approach to generic exergame design that combines custom physical exercises in a meaningful and motivating story. This aims to provide a tool that can be individually tailored and adapted to people with different needs, making it applicable to different diseases and states of disease. The game is based on motion capturing and integrates four example exercises that can be configured via our therapeutic web platform "Blexer-med". To prove the feasibility for a wide range of different users, evaluation tests were performed on 14 patients with various types and degrees of neuromuscular disorders, classified into three groups based on strength and autonomy. The users were free to choose their schedule and frequency. The game scores and three surveys (before, during, and after the intervention) showed similar experiences for all groups, with the most vulnerable having the most fun and satisfaction. The players were motivated by the story and by achieving high scores. The average usage time was 2.5 times per week, 20 min per session. The pure exercise time was about half of the game time. The concept has proven feasible and forms a reasonable basis for further developments. The full 3D exercise needs further fine-tuning to enhance the fun and motivation. [ABSTRACT FROM AUTHOR]

Objective: Head motions during brain PET scan cause degradation of brain images, but head fixation or external-maker attachment become burdensome on patients. Therefore, we have developed a motion correction method that uses a 3D face-shape model generated by a range-sensing camera (Kinect) and by CT images. We have successfully corrected the PET images of a moving mannequin-head phantom containing radioactivity. Here, we conducted a volunteer study to verify the effectiveness of our method for clinical data.Methods: Eight healthy men volunteers aged 22-45 years underwent a 10-min head-fixed PET scan as a standard of truth in this study, which was started 45 min after 18F-fluorodeoxyglucose (285 ± 23 MBq) injection, and followed by a 15-min head-moving PET scan with the developed Kinect based motion-tracking system. First, selecting a motion-less period of the head-moving PET scan provided a reference PET image. Second, CT images separately obtained on the same day were registered to the reference PET image, and create a 3D face-shape model, then, to which Kinect-based 3D face-shape model matched. This matching parameter was used for spatial calibration between the Kinect and the PET system. This calibration parameter and the motion-tracking of the 3D face shape by Kinect comprised our motion correction method. The head-moving PET with motion correction was compared with the head-fixed PET images visually and by standard uptake value ratios (SUVRs) in the seven volume-of-interest regions. To confirm the spatial calibration accuracy, a test-retest experiment was performed by repeating the head-moving PET with motion correction twice where the volunteer's pose and the sensor's position were different.Results: No difference was identified visually and statistically in SUVRs between the head-moving PET images with motion correction and the head-fixed PET images. One of the small nuclei, the inferior colliculus, was identified in the head-fixed PET images and in the head-moving PET images with motion correction, but not in those without motion correction. In the test-retest experiment, the SUVRs were well correlated (determinant coefficient, r2 = 0.995).Conclusion: Our motion correction method provided good accuracy for the volunteer data which suggested it is useable in clinical settings. [ABSTRACT FROM AUTHOR]

Exercise games (exergames) that combine both exercise and video gaming train people in a fun and competitive manner to lead a healthy lifestyle. Exergames promote more physical exertion and help users exercise more easily and independently in any place. Many studies have been conducted to evaluate the positive effects of exergames. However, in most studies, heart rate was mainly used to measure the effect of exercise. In this study, we evaluate the effects of exercise according to the exercise type (rest, walking, tennis, and running) and gameplay mode (single, competition, and cooperation) of exergaming via quantitative measurements using electrocardiogram (ECG) and Kinect. The multiple comparison results reveal that physical activity measured with Kinect was statistically significant even in exergames that did not show statistically significant differences according to ECG. Running was statistically significant compared to other exercise types, and there was a significant difference in competition compared to other gameplay modes. [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]