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

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

This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815, p < 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R 2 of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.

Introduction: Clinicians typically observe and describe abnormal head postures (AHPs) and may also measure them. Depth cameras have been suggested as a reliable measurement device for measuring head position using face-tracking technology. This study compared a depth camera (Microsoft Kinect) to a gold standard electromagnetic tracking system (Polhemus device) to measure head position., Method: Twenty healthy volunteers (mean age 21 years) had their head position simultaneously recorded using the depth camera (Kinect) and the electromagnetic tracking system (Polhemus). Participants were asked to make 30-degree head movements into chin up, chin down, head turn and head tilt positions. The head movement made and the stability of the head at each position were recorded and analysed., Results: Compared to the electromagnetic tracking system (Polhemus), the depth camera (Kinect) always measured a smaller head movement. Measurements with the two devices were not statistically significantly different for turn right (P = 0.3955, p > 0.05), turn left (P = 0.4749, p > 0.05), tilt right (P = 0.7086, p > 0.05) and tilt left (P = 0.4091, p > 0.05) head movements. However, the smaller depth camera measurement of chin up and chin down head movements were statistically significant, chin up (P = 0.0001, p < 0.01) and chin down (P = 0.0005, p < 0.001). At each eccentric position, the depth camera (Kinect) recordings were more variable than the electromagnetic tracking system (Polhemus)., Conclusions: Compared to the electromagnetic tracking system (Polhemus), the depth camera (Kinect) was comparable for measuring head turns and tilts but was less accurate at measuring chin up and chin down head positions. Further research is needed before the depth cameras are considered for clinical recordings of head position., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2021 The Author(s).)

The ever-growing and widespread use of touch, face, full-body, and 3D mid-air gesture recognition sensors in domestic and industrial settings is serving to highlight whether interactive gestures are sufficiently inclusive, and whether or not they can be executed by all users. The purpose of this study was to analyze full-body gestures from the point of view of user experience using the Microsoft Kinect sensor, to identify which gestures are easy for individuals living with Down syndrome. With this information, app developers can satisfy Design for All (DfA) requirements by selecting suitable gestures from existing lists of gesture sets. A set of twenty full-body gestures were analyzed in this study; to do so, the research team developed an application to measure the success/failure rates and execution times of each gesture. The results show that the failure rate for gesture execution is greater than the success rate, and that there is no difference between male and female participants in terms of execution times or the successful execution of gestures. Through this study, we conclude that, in general, people living with Down syndrome are not able to perform certain full-body gestures correctly. This is a direct consequence of limitations resulting from characteristic physical and motor impairments. As a consequence, the Microsoft Kinect sensor cannot identify the gestures. It is important to remember this fact when developing gesture-based on Human Computer Interaction (HCI) applications that use the Kinect sensor as an input device when the apps are going to be used by people who have such disabilities.

Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect) is a widely used, low-cost depth sensor that can be used to detect human behaviors in real time. The purpose of this study is to investigate the usage of the Kinect data for the classification and analysis of hemiplegia gait. We first acquire the gait data by using a MS Kinect and extract a set of gait features including the stride length, gait speed, left/right moving distances, and up/down moving distances. With the gait data of 60 subjects including 20 hemiplegia patients and 40 healthy subjects, we employ a random forest-based classification approach to analyze the importances of different gait features for hemiplegia classification. Thanks to the over-fitting avoidance nature of the random forest approach, we do not need to have a careful control over the percentage of patients in the training data. In our experiments, our approach obtained the averaged classification accuracy of 90.65% among all the combinations of the gait features, which substantially outperformed state-of-the-art methods. The best classification accuracy of our approach is 95.45%, which is superior than all existing methods. Additionally, our approach also correctly reveals the importance of different gait features for hemiplegia classification. Our random forest-based approach outperforms support vector machine-based method and the Bayesian-based method, and can effectively extract gait features of subjects with hemiplegia for the classification and analysis of hemiplegia. Graphical Abstract Random Forest based Classsification and Analysis of Hemiplegia Gait using Low-cost Depth Cameras. Left: Motion capture with MS Kinect; Top-right: Random Forest Classsification based on the extracted gait features; Bottom-right: Sensitivity and specificity evaluation of the proposed classification approach.

Low back pain is one of the most common physical symptom and is frequently related with an abnormal body posture. It may be caused by poor upper body and limb coordination; repetitive lifting of heavy objects or poor working are ergonomics. This study analysis the consequence of repetitive heavy lifting on the normal standing posture of factory workers. To asses the posture malformations the Microsoft Kinect sensor was used to obtain postural data from 88 factory workers. The study has shown that more than 90% of the study group has some sort of postural malformation and lower back pain.

Purpose: In order to evaluate the movement strategy in sit-to-stand (STS) motion, it is necessary to measure the center of gravity (COG). However, there is no established method for enabling this using convenience device in the clinical setting. The purpose of this study was to validate the measurement of the COG during the STS motion using an inexpensive, portable and markerless motion capture system (MLS)., Method: Eighteen healthy adults participated in our study. The coordinates of the joint centers during the STS motion were collected using the Microsoft Kinect system (as a MLS) and the Vicon system (as a marker-based motion capture system [MBS]). The center of mass of each segment-which was calculated based on the segmental mass and length-were synthesized to calculate the COG. The displacement, velocity, acceleration of the COG during the STS motion were calculated from the data obtained using each system and compared between systems., Results: The two systems showed significant difference in their measurements of displacement in both the vertical and anteroposterior directions. However, in the anteroposterior direction, there was no significant difference in the measurements of either velocity or acceleration., Conclusion: Our results suggested the validity of the COG in the anteroposterior direction during the STS motion measured using the MLS. The method developed in the present study enables the evaluation of a patient's movement strategy., (Copyright © 2019 IPEM. Published by Elsevier Ltd. All rights reserved.)

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson's correlation coefficient (r), and a Lin's concordance correlation coefficient (r c ). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; r c  = 0.97; 95% LOA = -8.0 mm - 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields., Competing Interests: The authors declare no conflict of interest.

Measurement of energy expenditure is an important tool in sport science and medicine, especially when trying to estimate the extent and intensity of physical activity. However, most approaches still rely on sensors or markers, placed directly on the body. In this paper, we present a novel approach using a fully contact-less, fully automatic method, that relies on computer vision algorithms and widely available and inexpensive imaging sensors. We rely on the estimation of the optical and scene flow to calculate Histograms of Oriented Optical Flow (HOOF) descriptors, which we subsequently augment with the Histograms of Absolute Flow Amplitude (HAFA). Descriptors are fed into regression model, which allows us to estimate energy consumption, and to a lesser extent, the heart rate. Our method has been tested both in lab environment and in realistic conditions of a sport match. Results confirm that these energy expenditures could be derived from purely contact-less observations. The proposed method can be used with different modalities, including near infrared imagery, which extends its future potential.

Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR&rsquo;s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method.

A neurological illness is t he disorder in human nervous system that can result in various diseases including the motor disabilities. Neurological disorders may affect the motor neurons, which are associated with skeletal muscles and control the body movement. Consequently, they introduce some diseases in the human e.g. cerebral palsy, spinal scoliosis, peripheral paralysis of arms/legs, hip joint dysplasia and various myopathies. Vojta therapy is considered a useful technique to treat the motor disabilities. In Vojta therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements which the patient is unable to perform in a normal manner. The repetition of stimulation ultimately brings forth the previously blocked connections between the spinal cord and the brain. After few therapy sessions, the patient can perform these movements without external stimulation. In this paper, we propose a computer vision-based system to monitor the correct movements of the patient during the therapy treatment using the RGBD data. The proposed framework works in three steps. In the first step, patient's body is automatically detected and segmented and two novel techniques are proposed for this purpose. In the second step, a multi-dimensional feature vector is computed to define various movements of patient's body during the therapy. In the final step, a multi-class support vector machine is used to classify these movements. The experimental evaluation carried out on the large captured dataset shows that the proposed system is highly useful in monitoring the patient's body movements during Vojta therapy., (Copyright © 2018 Elsevier B.V. All rights reserved.)

This work proposes a real-time monitoring tool aimed to support clinicians for remote assessing exercise performances during home-based rehabilitation. The study relies on clinician indications to define kinematic features, that describe five motor tasks (i.e., the lateral tilt of the trunk, lifting of the arms, trunk rotation, pelvis rotation, squatting) usually adopted in the rehabilitation program for axial disorders. These features are extracted by the Kinect v2 skeleton tracking system and elaborated to return disaggregated scores, representing a measure of subjects performance. A bell-shaped function is used to rank the patient performances and to provide the scores. The proposed rehabilitation tool has been tested on 28 healthy subjects and on 29 patients suffering from different neurological and orthopedic diseases. The reliability of the study has been performed through a cross-sectional controlled design methodology, comparing algorithm scores with respect to blinded judgment provided by clinicians through filling a specific questionnaire. The use of task-specific features and the comparison between the clinical evaluation and the score provided by the instrumental approach constitute the novelty of the study. The proposed methodology is reliable for measuring subject's performance and able to discriminate between the pathological and healthy condition., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The shoulder's range of motion (ROM) is an important measurement for the diagnostic process and course of treatment for patients with shoulder disorders or injuries. Visual estimation to assess a shoulder's ROM is a fast measuring method, and therefore routinely used in clinical practice. Studies already proved this method as very subjective and unreliable. Misestimating the severity of a patient's disability can lead to improper treatment and should be avoided. Modern technology may help measuring the ROM more reliable, objective, non-invasive and still fast. In this paper we present a computer-based prototype to semi-automatically assess the patient's shoulder ROM. Still photography is one of the most accurate ways to determine the extent to which a shoulder can be moved. Thus, a marker-less motion sensing device is used to capture movements of patient. A study with n=9 healthy adults was conducted to validate the results of the computer-based system against a physician using goniometry. The results show great potential of this technique for abduction, adduction, anteversion and retroversion with an intraclass correlation coefficient ranging between 0.77 and 0.86 for the best measuring method. Using the system would enhance daily practice. Patients could measure their ROM during their waiting time in advance to the visit, optionally supported by a nurse. Due to the more reliable and objective result the physician can instantly start diagnosing the patient or discussing therapy options. Time for investigation is saved and more time to treat the patient with objective and reliable measurement results would be available.

One of the key technologies that lays behind the human-machine interaction and human motion diagnosis is the limbs motion tracking. To make the limbs tracking efficient, it must be able to estimate a precise and unambiguous position of each tracked human joint and resulting body part pose. In recent years, body pose estimation became very popular and broadly available for home users because of easy access to cheap tracking devices. Their robustness can be improved by different tracking modes data fusion. The paper defines the novel approach-orientation based data fusion-instead of dominating in literature position based approach, for two classes of tracking devices: depth sensors (i.e., Microsoft Kinect) and inertial measurement units (IMU). The detailed analysis of their working characteristics allowed to elaborate a new method that let fuse more precisely limbs orientation data from both devices and compensates their imprecisions. The paper presents the series of performed experiments that verified the method's accuracy. This novel approach allowed to outperform the precision of position-based joints tracking, the methods dominating in the literature, of up to 18%., Competing Interests: The authors declare no conflict of interest.

Introduction: Child occupant safety in motor-vehicle crashes is evaluated using Anthropomorphic Test Devices (ATD) seated in optimal positions. However, child occupants often assume suboptimal positions during real-world driving trips. Head impact to the seat back has been identified as one important injury causation scenario for seat belt restrained, head-injured children (Bohman et al., 2011). There is therefore a need to understand the interaction of children with the Child Restraint System to optimize protection., Method: Naturalistic driving studies (NDS) will improve understanding of out-of-position (OOP) trends. To quantify OOP positions, an NDS was conducted. Families used a study vehicle for two weeks during their everyday driving trips. The positions of rear-seated child occupants, representing 22 families, were evaluated. The study vehicle - instrumented with data acquisition systems, including Microsoft Kinect™ V1 - recorded rear seat occupants in 1120 driving 26 trips. Three novel analytical methods were used to analyze data. To assess skeletal tracking accuracy, analysts recorded occurrences where Kinect™ exhibited invalid head recognition among a randomly-selected subset (81 trips). Errors included incorrect target detection (e.g., vehicle headrest) or environmental interference (e.g., sunlight). When head data was present, Kinect™ was correct 41% of the time; two other algorithms - filtering for extreme motion, and background subtraction/head-based depth detection are described in this paper and preliminary results are presented. Accuracy estimates were not possible because of their experimental nature and the difficulty to use a ground truth for this large database. This NDS tested methods to quantify the frequency and magnitude of head positions for rear-seated child occupants utilizing Kinect™ motion-tracking., Results: This study's results informed recent ATD sled tests that replicated observed positions (most common and most extreme), and assessed the validity of child occupant protection on these typical CRS uses., Summary: Optimal protection in vehicles requires an understanding of how child occupants use the rear seat space. This study explored the feasibility of using Kinect™ to log positions of rear seated child occupants. Initial analysis used the Kinect™ system's skeleton recognition and two novel analytical algorithms to log head location., Practical Applications: This research will lead to further analysis leveraging Kinect™ raw data - and other NDS data - to quantify the frequency/magnitude of OOP situations, ATD sled tests that replicate observed positions, and advances in the design and testing of child occupant protection technology., (Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.)

The present paper describes the design and test of a low-cost Microsoft Kinect-based system for delivering adaptive visual feedback to stroke patients during the execution of an upper limb exercise. Eleven sub-acute stroke patients with varying degrees of upper limb function were recruited. Each subject participated in a control session (repeated twice) and a feedback session (repeated twice). In each session, the subjects were presented with a rectangular pattern displayed on a vertical mounted monitor embedded in the table in front of the patient. The subjects were asked to move a marker inside the rectangular pattern by using their most affected hand. During the feedback session, the thickness of the rectangular pattern was changed according to the performance of the subject, and the color of the marker changed according to its position, thereby guiding the subject's movements. In the control session, the thickness of the rectangular pattern and the color of the marker did not change. The results showed that the movement similarity and smoothness was higher in the feedback session than in the control session while the duration of the movement was longer. The present study showed that adaptive visual feedback delivered by use of the Kinect sensor can increase the similarity and smoothness of upper limb movement in stroke patients.

Background: Noncontact anterior cruciate ligament (ACL) injury in adolescent female athletes is an increasing problem. The knee-ankle separation ratio (KASR), calculated at initial contact (IC) and peak flexion (PF) during the drop vertical jump (DVJ), is a measure of dynamic knee valgus. The Microsoft Kinect V2 has shown promise as a reliable and valid marker-less motion capture device., Hypothesis: The Kinect V2 will demonstrate good to excellent correlation between KASR results at IC and PF during the DVJ, as compared with a "gold standard" Vicon motion analysis system., Study Design: Descriptive laboratory study., Level of Evidence: Level 2., Methods: Thirty-eight healthy volunteer subjects (20 male, 18 female) performed 5 DVJ trials, simultaneously measured by a Vicon MX-T40S system, 2 AMTI force platforms, and a Kinect V2 with customized software. A total of 190 jumps were completed. The KASR was calculated at IC and PF during the DVJ. The intraclass correlation coefficient (ICC) assessed the degree of KASR agreement between the Kinect and Vicon systems., Results: The ICCs of the Kinect V2 and Vicon KASR at IC and PF were 0.84 and 0.95, respectively, showing excellent agreement between the 2 measures. The Kinect V2 successfully identified the KASR at PF and IC frames in 182 of 190 trials, demonstrating 95.8% reliability., Conclusion: The Kinect V2 demonstrated excellent ICC of the KASR at IC and PF during the DVJ when compared with the Vicon system. A customized Kinect V2 software program demonstrated good reliability in identifying the KASR at IC and PF during the DVJ., Clinical Relevance: Reliable, valid, inexpensive, and efficient screening tools may improve the accessibility of motion analysis assessment of adolescent female athletes.

Assessments using dynamic postural control tests, like the Star Excursion Balance Test (SEBT), in combination with three-dimensional (3D) motion analysis can yield critical information regarding a subject's lower limb movement patterns. 3D analysis can provide a clear understanding of the mechanisms that lead to specific outcome measures on the SEBT. Currently, the only technology for 3D motion analysis during such tests is expensive marker-based motion analysis systems, which are impractical for use in clinical settings. In this study we validated the use of the Microsoft Kinect as a cost-effective and marker-less alternative to more complex and expensive gold-standard motion analysis systems. Ten healthy subjects performed the SEBT while their lower limb kinematics were measured concurrently using a traditional motion capture system and a single Kinect v2 sensor. Analyses revealed errors in lower limb kinematics of less than 5°, except for the knee frontal-plane angle (5.7°) in the posterior-lateral direction. Ensemble curve analyses supported these findings, showing minimal between-system differences in all directions. Additionally, we found that the Kinect displayed excellent agreement (ICC 3,k =0.99) and consistency (ICC 2,k =0.99) when assessing reach distances in all directions. These results indicate that this low-cost and easy to implement technology may provide to clinicians a simple tool to simultaneously assess reach distances while developing a clearer understanding of the lower extremity movement patterns associated with SEBT performance in healthy and injured populations., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The Microsoft Kinect has been used in studies examining posture and gait. Despite the advantages of portability and low cost, this device has not been used to assess interlimb coordination. Fundamental insights into movement control, variability, health, and functional status can be gained by examining coordination patterns. In this study, we investigated the efficacy of the Microsoft Kinect to capture bimanual coordination relative to a research-grade motion capture system. Twenty-four healthy adults performed coordinated hand movements in two patterns (in-phase and antiphase) at eight movement frequencies (1.00-3.33 Hz). Continuous relative phase (CRP) and discrete relative phase (DRP) were used to quantify the means (mCRP and mDRP) and variability (sdCRP and sdDRP) of coordination patterns. Between-device agreement was assessed using Bland-Altman bias with 95 % limits of agreement, concordance correlation coefficients (absolute agreement), and Pearson correlation coefficients (relative agreement). Modest-to-excellent relative and absolute agreements were found for mCRP in all conditions. However, mDRP showed poor agreement for the in-phase pattern at low frequencies, due to large between-device differences in a subset of participants. By contrast, poor absolute agreement was observed for both sdCRP and sdDRP, while relative agreement ranged from poor to excellent. Overall, the Kinect captures the macroscopic patterns of bimanual coordination better than coordination variability.

Background: The present paper describes the design and evaluation of an automated version of the Modified Jebsen Test of Hand Function (MJT) based on the Microsoft Kinect sensor., Methods: The MJT was administered twice to 11 chronic stroke subjects with varying degrees of hand function deficits. The test times of the MJT were evaluated manually by a therapist using a stopwatch, and automatically using the Microsoft Kinect sensor. The ground truth times were assessed based on inspection of the video-recordings. The agreement between the methods was evaluated along with the test-retest performance., Results: The results from Bland-Altman analysis showed better agreement between the ground truth times and the automatic MJT time evaluations compared to the agreement between the ground truth times and the times estimated by the therapist. The results from the test-retest performance showed that the subjects significantly improved their performance in several subtests of the MJT, indicating a practice effect., Conclusions: The results from the test showed that the Kinect can be used for automating the MJT.

Functionally stable and robust interpersonal motor coordination has been found to play an integral role in the effectiveness of social interactions. However, the motion-tracking equipment required to record and objectively measure the dynamic limb and body movements during social interaction has been very costly, cumbersome, and impractical within a non-clinical or non-laboratory setting. Here we examined whether three low-cost motion-tracking options (Microsoft Kinect skeletal tracking of either one limb or whole body and a video-based pixel change method) can be employed to investigate social motor coordination. Of particular interest was the degree to which these low-cost methods of motion tracking could be used to capture and index the coordination dynamics that occurred between a child and an experimenter for three simple social motor coordination tasks in comparison to a more expensive, laboratory-grade motion-tracking system (i.e., a Polhemus Latus system). Overall, the results demonstrated that these low-cost systems cannot substitute the Polhemus system in some tasks. However, the lower-cost Microsoft Kinect skeletal tracking and video pixel change methods were successfully able to index differences in social motor coordination in tasks that involved larger-scale, naturalistic whole body movements, which can be cumbersome and expensive to record with a Polhemus. However, we found the Kinect to be particularly vulnerable to occlusion and the pixel change method to movements that cross the video frame midline. Therefore, particular care needs to be taken in choosing the motion-tracking system that is best suited for the particular research.

Background: The possibility of using a computer by a disabled person is one of the difficult problems of the human-computer interaction (HCI), while the professional activity (employment) is one of the most important factors affecting the quality of life, especially for disabled people. The aim of the project has been to propose a new HCI system that would allow for resuming employment for people who have lost the possibility of a standard computer operation., Material and Methods: The basic requirement was to replace all functions of a standard mouse without the need of performing precise hand movements and using fingers. The Microsoft's Kinect motion controller had been selected as a device which would recognize hand movements. Several tests were made in order to create optimal working environment with the new device. The new communication system consisted of the Kinect device and the proper software had been built., Results: The proposed system was tested by means of the standard subjective evaluations and objective metrics according to the standard ISO 9241-411:2012. The overall rating of the new HCI system shows the acceptance of the solution. The objective tests show that although the new system is a bit slower, it may effectively replace the computer mouse., Conclusions: The new HCI system fulfilled its task for a specific disabled person. This resulted in the ability to return to work. Additionally, the project confirmed the possibility of effective but nonstandard use of the Kinect device. Med Pr 2017;68(1):1-21., (This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.)

Objective: At present, there are no well-validated biomarkers for attention-deficit/hyperactivity disorder (ADHD). The present study used an infrared motion tracking system to monitor and record the movement intensity of children and to determine its diagnostic precision for ADHD and its possible associations with ratings of ADHD symptom severity., Methods: A Microsoft motion sensing camera recorded the movement of children during a modified Go/No-Go Task. Movement intensity measures extracted from these data included a composite measure of total movement intensity (TMI measure) and a movement intensity distribution (MID measure) measure across 15 frequency bands (FB measures). In phase 1 of the study, 30 children diagnosed with ADHD or at subthreshold for ADHD and 30 matched healthy controls were compared to determine if measures of movement intensity successfully distinguished children with ADHD from healthy control children. In phase 2, associations between measures of movement intensity and clinician-rated ADHD symptom severity (Clinical Global Impression Scale [CGI] and the ADHD-Rating Scale IV [ADHD-RS]) were examined in a subset of children with ADHD (n = 14) from the phase I sample., Results: Both measures of movement intensity were able to distinguish children with ADHD from healthy controls. However, only the measures linked to the 15 pre-determined 1 Hz frequency bands were significantly correlated with both the CGI scores and ADHD-RS total scores., Conclusions: Preliminary findings suggest that measures of movement intensity, particularly measures linked to the 10-11 and 12-13 Hz frequency bands, have the potential to become valid biomarkers for ADHD.

Background: Quick protective reactions such as reaching or stepping are important to avoid a fall or minimize injuries. We developed Kinect-based choice reaching and stepping reaction time tests (Kinect-based CRTs) and evaluated their ability to differentiate between older fallers and non-fallers and the feasibility of administering them at home., Methods: A total of 94 community-dwelling older people were assessed on the Kinect-based CRTs in the laboratory and were followed-up for falls for 6 months. Additionally, a subgroup (n = 20) conducted the Kinect-based CRTs at home. Signal processing algorithms were developed to extract features for reaction, movement and the total time from the Kinect skeleton data., Results: Nineteen participants (20.2 %) reported a fall in the 6 months following the assessment. The reaction time (fallers: 797 ± 136 ms, non-fallers: 714 ± 89 ms), movement time (fallers: 392 ± 50 ms, non-fallers: 358 ± 51 ms) and total time (fallers: 1189 ± 170 ms, non-fallers: 1072 ± 109 ms) of the reaching reaction time test differentiated well between the fallers and non-fallers. The stepping reaction time test did not significantly discriminate between the two groups in the prospective study. The correlations between the laboratory and in-home assessments were 0.689 for the reaching reaction time and 0.860 for stepping reaction time., Conclusion: The study findings indicate that the Kinect-based CRT tests are feasible to administer in clinical and in-home settings, and thus represents an important step towards the development of sensor-based fall risk self-assessments. With further validation, the assessments may prove useful as a fall risk screen and home-based assessment measures for monitoring changes over time and effects of fall prevention interventions.

The paper deals with the comparison between a high-end marker-based acquisition system and a low-cost marker-less methodology for the assessment of the human posture during working tasks. The low-cost methodology is based on the use of a single Microsoft Kinect V1 device. The high-end acquisition system is the BTS SMART that requires the use of reflective markers to be placed on the subject's body. Three practical working activities involving object lifting and displacement have been investigated. The operational risk has been evaluated according to the lifting equation proposed by the American National Institute for Occupational Safety and Health. The results of the study show that the risk multipliers computed from the two acquisition methodologies are very close for all the analysed activities. In agreement to this outcome, the marker-less methodology based on the Microsoft Kinect V1 device seems very promising to promote the dissemination of computer-aided assessment of ergonomics while maintaining good accuracy and affordable costs. PRACTITIONER’S SUMMARY: The study is motivated by the increasing interest for on-site working ergonomics assessment. We compared a low-cost marker-less methodology with a high-end marker-based system. We tested them on three different working tasks, assessing the working risk of lifting loads. The two methodologies showed comparable precision in all the investigations.

Concurrent validity and test-retest reliability of the Microsoft Kinect in quantification of manual wheelchair propulsion were examined. Data were collected from five manual wheelchair users on a roller system. Three Kinect sensors were used to assess test-retest reliability with a still pose. Three systems were used to assess concurrent validity of the Kinect to measure propulsion kinematics (joint angles, push loop characteristics): Kinect, Motion Analysis, and Dartfish ProSuite (Dartfish joint angles were limited to shoulder and elbow flexion). Intraclass correlation coefficients revealed good reliability (0.87-0.99) between five of the six joint angles (neck flexion, shoulder flexion, shoulder abduction, elbow flexion, wrist flexion). ICCs suggested good concurrent validity for elbow flexion between the Kinect and Dartfish and between the Kinect and Motion Analysis. Good concurrent validity was revealed for maximum height, hand-axle relationship, and maximum area (0.92-0.95) between the Kinect and Dartfish and maximum height and hand-axle relationship (0.89-0.96) between the Kinect and Motion Analysis. Analysis of variance revealed significant differences (p < 0.05) in maximum length between Dartfish (mean 58.76 cm) and the Kinect (40.16 cm). Results pose promising research and clinical implications for propulsion assessment and overuse injury prevention with the application of current findings to future technology.

There is a large amount of research currently being done on autonomous micro-aerial vehicles (MAV), such as quadrotor helicopters or quadcopters. The ability to create a working autonomous MAV depends mainly on integrating a simultaneous localization and mapping (SLAM) solution with the rest of the system. This paper provides an introduction for creating an autonomous MAV for enclosed environments, aimed at students and professionals alike. The standard autonomous system and MAV automation are discussed, while we focus on the core concepts of SLAM systems and trajectory planning algorithms. The advantages and disadvantages of using remote processing are evaluated, and recommendations are made regarding the viability of on-board processing. Recommendations are made regarding best practices to serve as a guideline for aspirant MAV designers.

Objective: To test the reliability and validity of shoulder joint angle measurements from the Microsoft Kinect™ for virtual rehabilitation., Design: Test-retest reliability and concurrent validity, feasibility study., Setting: Motion analysis laboratory., Participants: A convenience sample of 10 healthy adults., Methods: Shoulder joint angle was assessed in four static poses, two trials for each pose, using: (1) the Kinect; (2) a three-dimensional motion analysis system; and (3) a clinical goniometer. All poses were captured with the Kinect from the frontal view. The two poses of shoulder flexion were also captured with the Kinect from the sagittal view., Main Outcome Measures: Absolute and relative test-retest reliability of the Kinect for the measurement of shoulder angle was determined in each pose with intraclass correlation coefficients (ICCs), standard error of the measure and minimal detectable change. The 95% limits of agreement (LOA) between the Kinect and the standard methods for measuring shoulder angle were computed to determine concurrent validity., Results: While the Kinect provided to be highly reliable (ICC 0.76-0.98) for measuring shoulder angle from the frontal view, the 95% LOA between the Kinect and the two measurement standards were greater than ±5° in all poses for both views., Conclusions: Before the Kinect is used to measure movements for virtual rehabilitation applications, it is imperative to understand its limitations in precision and accuracy for the measurement of specific joint motions., (Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.)

N95 filtering facepiece respirator (FFR) dead space is an important factor for respirator design. The dead space refers to the cavity between the internal surface of the FFR and the wearer's facial surface. This article presents a novel method to estimate the dead space volume of FFRs and experimental validation. In this study, six FFRs and five headforms (small, medium, large, long/narrow, and short/wide) are used for various FFR and headform combinations. Microsoft Kinect Sensors (Microsoft Corporation, Redmond, WA) are used to scan the headforms without respirators and then scan the headforms with the FFRs donned. The FFR dead space is formed through geometric modeling software, and finally the volume is obtained through LS-DYNA (Livermore Software Technology Corporation, Livermore, CA). In the experimental validation, water is used to measure the dead space. The simulation and experimental dead space volumes are 107.5-167.5 mL and 98.4-165.7 mL, respectively. Linear regression analysis is conducted to correlate the results from Kinect and water, and R(2) = 0.85.

Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices-Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows-for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20-30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

Commercially available interactive video games are commonly used in rehabilitation to aide in physical recovery from a variety of conditions and injuries, including burns. Most video games were not originally designed for rehabilitation purposes and although some games have shown therapeutic potential in burn rehabilitation, the physical demands of more recently released video games, such as Microsoft Xbox Kinect™ (Kinect) and Sony PlayStation 3 Move™ (PS Move), have not been objectively evaluated. Video game technology is constantly evolving and demonstrating different immersive qualities and interactive demands that may or may not have therapeutic potential for patients recovering from burns. This study analyzed the upper extremity motion demands of Kinect and PS Move using three-dimensional motion analysis to determine their applicability in burn rehabilitation. Thirty normal children played each video game while real-time movement of their upper extremities was measured to determine maximal excursion and amount of elevation time. Maximal shoulder flexion, shoulder abduction and elbow flexion range of motion were significantly greater while playing Kinect than the PS Move (p≤0.01). Elevation time of the arms above 120° was also significantly longer with Kinect (p<0.05). The physical demands for shoulder and elbow range of motion while playing the Kinect, and to a lesser extent PS Move, are comparable to functional motion needed for daily tasks such as eating with a utensil and hair combing. Therefore, these more recently released commercially available video games show therapeutic potential in burn rehabilitation. Objectively quantifying the physical demands of video games commonly used in rehabilitation aides clinicians in the integration of them into practice and lays the framework for further research on their efficacy., (Copyright © 2013 Elsevier Ltd and ISBI. All rights reserved.)

Objectives: Existing methods for practically evaluating musculoskeletal exposures such as posture and repetition in workplace settings have limitations. We aimed to automate the estimation of parameters in the revised United States National Institute for Occupational Safety and Health (NIOSH) lifting equation, a standard manual observational tool used to evaluate back injury risk related to lifting in workplace settings, using depth camera (Microsoft Kinect) and skeleton algorithm technology., Methods: A large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset., Results: Error-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates., Conclusions: Our results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training.

Background: The Microsoft Kinect sensor (Kinect) is potentially a low-cost solution for clinical and home-based assessment of movement symptoms in people with Parkinson's disease (PD). The purpose of this study was to establish the accuracy of the Kinect in measuring clinically relevant movements in people with PD., Methods: Nine people with PD and 10 controls performed a series of movements which were measured concurrently with a Vicon three-dimensional motion analysis system (gold-standard) and the Kinect. The movements included quiet standing, multidirectional reaching and stepping and walking on the spot, and the following items from the Unified Parkinson's Disease Rating Scale: hand clasping, finger tapping, foot, leg agility, chair rising and hand pronation. Outcomes included mean timing and range of motion across movement repetitions., Results: The Kinect measured timing of movement repetitions very accurately (low bias, 95% limits of agreement <10% of the group mean, ICCs >0.9 and Pearson's r>0.9). However, the Kinect had varied success measuring spatial characteristics, ranging from excellent for gross movements such as sit-to-stand (ICC=.989) to very poor for fine movement such as hand clasping (ICC=.012). Despite this, results from the Kinect related strongly to those obtained with the Vicon system (Pearson's r>0.8) for most movements., Conclusions: The Kinect can accurately measure timing and gross spatial characteristics of clinically relevant movements but not with the same spatial accuracy for smaller movements, such as hand clasping., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Markerless motion capture systems have developed in an effort to evaluate human movement in a natural setting. However, the accuracy and reliability of these systems remain understudied. Therefore, the goals of this study were to quantify the accuracy and repeatability of joint angles using a single camera markerless motion capture system and to compare the markerless system performance with that of a marker-based system. A jig was placed in multiple static postures with marker trajectories collected using a ten camera motion analysis system. Depth and color image data were simultaneously collected from a single Microsoft Kinect camera, which was subsequently used to calculate virtual marker trajectories. A digital inclinometer provided a measure of ground-truth for sagittal and frontal plane joint angles. Joint angles were calculated with marker data from both motion capture systems using successive body-fixed rotations. The sagittal and frontal plane joint angles calculated from the marker-based and markerless system agreed with inclinometer measurements by <0.5°. The systems agreed with each other by <0.5° for sagittal and frontal plane joint angles and <2° for transverse plane rotation. Both systems showed a coefficient of reliability <0.5° for all angles. These results illustrate the feasibility of a single camera markerless motion capture system to accurately measure lower extremity kinematics and provide a first step in using this technology to discern clinically relevant differences in the joint kinematics of patient populations., (© 2013 Published by Elsevier Ltd.)

Background: Non-invasive brain stimulation (NIBS) is a promising tool for facilitating motor function. NIBS therapy in conjunction with training using postural feedback may facilitate physical rehabilitation following posture disorders (e.g., Pusher Syndrome)., Objectives: The objectives of this study were, 1) to develop a low-cost point-of-care-testing (POCT) system for standing posture, 2) to investigate the effects of anodal tDCS on functional reach tasks using the POCT system., Methods: Ten community-dwelling elderly (age >50 years) subjects evaluated the POCT system for standing posture during functional reach tasks where their balance score on Berg Balance Scale was compared with that from Center-of-Mass (CoM) - Center-of-Pressure (CoP) posturography. Then, in a single-blind, sham-controlled study, five healthy right-leg dominant subjects (age: 26.4 ± 5.3 yrs) were evaluated using the POCT system under two conditions - with anodal tDCS of primary motor representations of right tibialis anterior muscle and with sham tDCS., Results: The maximum CoP-CoM lean-angle was found to be well correlated with the BBS score in the elderly subjects The anodal tDCS strongly (p = 0.0000) affected the maximum CoP excursions but not the return reaction time in healthy., Conclusion: It was concluded that the CoM-CoP lean-line could be used for posture feedback and monitoring during tDCS therapy in conjunction with balance training exercises.