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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.

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.

In physiotherapy, rehabilitation outcome is majorly dependent on the patient continuing exercises at home. To support a continuous and correct execution of exercises composed by the physiotherapist it is important that the patient stays motivated. With the emergence of game consoles such as Nintendo Wii, Sony PlayStation or Microsoft Xbox360 that employ special controllers or camera based motion recognition as means of user input those technologies have also been found to be interesting for other real-life applications. We present a concept to employ the Microsoft Kinect system as means to support patients during physiotherapy exercises at home. The system is intended to allow a physiotherapist to compose an individual set of exercises and to control the correct execution of those exercises through tracking the patient’s motions.

In radiotherapy of abdominal and thoracic tumors, respiratory motion is a problem for an accurate treatment. Most current motion compensation techniques rely on externally acquired breathing signals of the patient. The systems in clinical use usually work with 1D surface motion signals to describe internal structure respiratory motion patterns. As a 1D signal is not able to describe complex motion patterns and breathing variations, in this work the Microsoft Kinect, which can record multidimensional respiratory surface motion signals, is proposed to be used instead. For the Kinect, a clinically acceptable measurement setup is designed and Kinect measurements are compared to the Varian RPM system (clinical standard). The results show that the signals are well aligned. An additional comparison of Kinect signals from different regions of interest on the chest further reveals variations between them. This illustrates that the use of a system that provides multidimensional signals is worthwhile; the knowledge about breathing variations could be applied for optimization of current clinical workflows.

In physiotherapy, rehabilitation outcome is majorly dependent on the patient to continue exercises at home. To support a continuous and correct execution of exercises composed by the physiotherapist it is important that the patient stays motivated. With the emergence of game consoles such as Nintendo Wii, PlayStation Eye or Microsoft Kinect that employ special controllers or camera based motion recognition as means of user input those technologies have also been found to be interesting for other real-life applications such as providing individual physiotherapy exercises and an encouraging rehabilitation routine. Due to the intended use of those motion tracking systems in a computer-game environment it remains questionable if the accuracy of the skeleton joint tracking hardware and algorithms is suflicient for physiotherapy applications. We present a basic evaluation of the joint tracking accuracy where angles between various body extremities calculated by a Kinect system were compared with a high resolution motion capture system. Results show promising results with tracking deviations between 2.7° and 14.2° with a mean of the absolute deviations of 8.7°.

Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a need for RGB-depth adapted tools. We present a new interactive labeling tool that partially automates image labeling, with two major contributions. First, the method extends the concept of image segmentation from RGB to RGB-depth using Fuzzy C-Means clustering, connected component labeling and superpixels, and generates bounding pixels to extract the desired objects. Second, it minimizes the interaction time needed for object extraction by doing an efficient segmentation in RGB-depth space. Very few clicks are needed for the entire procedure compared to existing, tools. When the desired object is the closest object to the camera, which is often the case in robotics applications, no clicks at all are required to accurately extract the object.

This study assessed the cervical spine range of motion in asymptomatic individuals using the universal goniometer and the Microsoft Kinect™ device, respectively and also determined the validity and test-retest reliability of the Microsoft Kinect™ device with a view to establishing the accuracy and reproducibility of an alternative but a valid tool for the assessment of cervical spine range of motion. This cross-sectional study involved 420 apparently healthy undergraduates from the Colleges of Health Sciences, Obafemi Awolowo University Ile-Ife and Ladoke Akintola University of Technology, Osogbo Campus, who were recruited consecutively after obtaining ethical clearance from the Health Research and Ethics Committee, Institute of Public Health, Obafemi Awolowo University, Ile-Ife, Nigeria. Cervical motions were evaluated using the universal goniometer and the Kinect™ device. The Microsoft Software Development Kit (SDK), version 1.8 and the Markus Bader Software solution were used to calculate the cervical motion based on Kinect skeleton tracking data. Anthropometric characteristics (age, height and weight) were recorded and body mass index was calculated. Every measurement was made twice and the average value used for statistical analysis. The intraclass correlation coefficient was used to investigate correlation and reliability between measurements obtained from the two techniques. An alpha level was set at p<0.05. The cervical spine range of motion for flexion, extension, right and left lateral rotation and right and left lateral flexion for the goniometer were 36.71 ± 6.34°, 43.11 ± 5.54°, 49.15 ± 5.88°, 47.69 ± 5.11°, 23.22 ± 3.95° and 22.67 ± 3.83° while for the Microsoft Kinect™ were 48.80 ± 6.76°, 15.34 ± 2.94°, 15.73 ± 3.04°, 15.09 ± 3.09°, 23.49 ± 4.59° and 24.49 ± 4.72°, respectively. There was no significant correlation in all of the cervical spine range of motion measurements obtained using the Microsoft Kinect (extension r = 0.09, p = 0.571; right lateral rotation r = 0.01, p = 0.614; left lateral rotation r = 0.008, p = 0.437; right lateral flexion r = 0.09, p = 0.571; left lateral flexion r = 0.01 p = 0.591) with the exception of cervical flexion (r = 0.198, p = 0.001). The intra-rater reliability of both the Microsoft Kinect™ and goniometer in the assessment of cervical spine range of motion was excellent (r > 0.75). The intra-rater reliability values for flexion, extension, right and left lateral rotation and right and left lateral flexion for the Microsoft Kinect™ were 0.98, 0.93, 0.95, 0.96, 0.97, and 0.98 while for the goniometer were 0.98, 0.98, 0.98, 0.98, 0.96, and 0.96, respectively. The Microsoft Kinect™ was found to be a reliable tool yet showing weak concurrent validity when compared with the universal goniometer in the cervical spine range of motion assessment, except for cervical flexion, among apparently healthy undergraduates. [ABSTRACT FROM AUTHOR]

Automatic emotion recognition from gaits information is discussed in this paper, which has been investigated widely in the fields of human-machine interaction, psychology, psychiatry, behavioral science, etc. The gaits information is non-contact, collected from Microsoft kinects, and contains 3-dimensional coordinates of 25 joints per person. These joints coordinates vary with the time. So, by the discrete Fourier transform and statistic methods, some time-frequency features related to neutral, happy and angry emotion are extracted and used to establish the classification model to identify these three emotions. Experimental results show this model works very well, and time-frequency features are effective in characterizing and recognizing emotions for this non-contact gait data. In particular, by the optimization algorithm, the recognition accuracy can be further averagely improved by about 13.7 percent. [ABSTRACT FROM AUTHOR]

This article describes how physical therapy rehabilitation promotes functional ability of the disabled people to improve quality of life using Range of Motion exercises. The conventional rehabilitation seems to be effective; however, the efficiency of the treatment sessions is not guaranteed resulting in longer recovery period. Thus, there is a need of self-motivating and engaging training solution to support rehabilitation and enhance continuous assessment of disabled patients. The proposed framework is "AR-NUI-REHAB-MDSS," augmented reality (AR) using natural user interface (NUI) based physical therapy rehabilitation with personalized exercise rendering and monitoring system for patients and mobile decision support system (MDSS) for therapists leading to a global solution for remote assistance. NUI allows human computer interaction intuitively through human body gestures. AR provides an entertaining environment for treatments with less-assistance. MDSS enables therapist to customize treatment plans at dynamic environments. Upper limb is considered as its functional recovery is significant and more challenging. [ABSTRACT FROM AUTHOR]

In this paper, a prototype lifelogging system for monitoring people with cog-nitive disabilities and elderly people as well as a method for the automatic de-tection of dangerous activities are presented. The system allows for the remote monitoring of observed people via an Internet website and respects the pri-vacy of the people by displaying their silhouettes instead of their actual ima-ges. The application allows for the viewing of both real-time and historical data. The lifelogging data (skeleton coordinates) needed for posture and activity re-cognition are acquired using Microsoft Kinect 2.0. Several activities are marked as potentially dangerous and generate alarms sent to caregivers upon detection. Recognition models are developed using Averaged Hidden Markov Models with multiple learning sequences. Action recognition includes methods for differenti-ating between normal and potentially dangerous activities (e.g., self-aggressive autistic behavior) using the same motion trajectory. Some activity recognition examples and results are presented. [ABSTRACT FROM AUTHOR]

Distributed collaborations between two or more participants on a task involving tangible artifacts (e.g., a machine, a patient, a tool) have become increasingly common in recent years due to rapid development in information and communication technologies. In this paper we focus on a specific type of remote-collaboration system where a remote helper guides a local worker using audio communication and hand gestures to perform a repair or a maintenance task. An established ICT approach to supporting this type of collaboration is to provide a shared visual space and some forms of remote gesture. The shared space typically consists of a video capture of the remote workspace which is then displayed on a 2D screen. However, this type of approach has its limitations. Firstly, it does not provide the helper with sufficient understanding of the spatial relationships between objects in the remote workspace. Secondly, it does not allow the helper to gesture in 3D. In an attempt to address these issues, we propose a Mixed Reality multimodal system that improves on previous 2D systems by introducing 3D real-time capturing and rendering of both the remote workspace and the helping hands and by creating a 3D shared visual space as a result of co-locating the remote workspace with the helping hands. In this system, we explore the possibility of increasing the feeling of immersion and co-presence by using head tracking, stereoscopic rendering, inter-occlusion handling and virtual shadowing. In this paper, we introduce HandsIn3D, a system that has been developed for the purpose of the proof of concept. We also present the results of experiments to verify the feasibility of our approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The need for measuring energy expenditure using non-wearable devices in sports science is a complex task involving strict protocols of measurement. Such protocols and measurement involving indirect calorimetry or inertial measurement unit (IMU) based measurement are expensive to setup, too inaccurate or force the subjects being measured to modify their actions in a significant manner. In this paper, we explored the concept of using a parallel Kalman filter setup being used in simulation. The simulation used was an iterative one using a dynamic model, featuring the use of two different types of setup- structured or non structured movement and the use of one or two Kalman filters. The results from the simulations performed showed that structured movement using a dual Kalman filter setup was the best performer when using root mean square error as the metric for performance. These results will help influence our work utilising the Microsoft Kinect and estimating weights of human joints and the energy expenditure attained from that. [ABSTRACT FROM AUTHOR]

With the thriving of different natural interaction paradigms—such as gesture-based interfaces—it becomes important to understand how these novel interfaces can influence users’ performance when it comes to their age. Recent advances made in human-computer interaction allow us to manipulate digital contents more intuitively; however, no work has yet been reported that systematically evaluates how gestural interfaces may influence the performance of different user groups. Different optical sensors, which allow human body acquisition with reliable accuracy, have been released, and with the appearance of such controllers for gesture recognition, it becomes important to understand if different age-related groups display similar performance levels concerning gestural interaction, or, on the other hand, if specific sensors could induce better results than others when dealing with users of different age brackets. In this article, we compare two gesture-sensing devices (Microsoft Kinect and Leap Motion) using the Fitts’ law model to evaluate target acquisition performance, with relation to three user groups: children, young adults and older adults. This case study involved 60 participants that were asked to perform a simple continuous selection task as quickly and accurately as possible using one of the devices for gestural recognition. Indeed, performance results showed statistically significant differences among the age groups in the selection task accomplished. However, when considering the users’ performance with regard to both input devices compared side by side, there were no significant differences in each group of users. We believe this situation could imply that the device itself might not have influenced the users’ performance, but actually the users’ age might. The participants feedback was interesting on account of their behaviors and preferences: Although there are no significant differences in performance, there could be when it comes to user preference. [ABSTRACT FROM AUTHOR]

One of the main trends in industrial robotics is collaboration between human and robot. Collaboration is applied especially in the field of assembly tasks. The current automation technology is not at the level that could ensure demanding tasks. These tasks are often associated with actions requiring human skills. Many manufacturers of industrial robot offer a solution in a form of a collaborative robot. These robots represent new opportunities for industry. In order to further develop the area of collaborative robotics, it is necessary to look for new technologies. This article deals with design of method which is based on open source platform. The method is applied to solve a real assembly task. [ABSTRACT FROM AUTHOR]

Each year 152,000 people in the UK have a stroke. Almost all have an initial facial weakness. Many resolve in the first few days but it is estimated that 26,000 people experience some kind of long-term paralysis in their face. A survey of 107 United Kingdom based clinicians found that routine treatment of facial weakness was provision of exercises with a written instruction sheet. A multidisciplinary team, which includes patients, researchers and therapists have produced a working prototype system to improve facial weakness. It is called Face to Face and includes a Kinect sensor, a small form PC and a monitor. Patients follow exercises given by a therapist on the screen; the system records and simultaneously gives feedback, with a facial recognition algorithm providing tracking data for each captured frame of the user's face. Results from our small clinical trial indicate that the system is more successful at getting patients to complete their exercises than using a mirror, patients liked it, and they said it had helped improve their facial symmetry. Therapists said Face to Face encouraged patients to exercise daily, they liked the fact that it could be individually programmed and could record how much the patient had exercised. Based on the initial project work and positive outcomes Face to Face aims to help patients practice their facial muscle exercises to speed their recovery, providing direct benefits in terms of costs and time, and offering patients significant improvements. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) is a well-known concept and has been proven to be beneficial in various areas. However, several disadvantages inherent in VR prevent its broad deployment in the educational arena. These limitations include non-realistic representation, lack of customizability and flexibility, financial feasibility, physical and psychological discomforts of the users, simulator sickness, etc. In this paper, an innovative method that uses the Microsoft Kinect as an essential component for developing game-based VR educational laboratories is presented. This technique addresses three different aspects. First, it represents an efficient method for creating the VE using the Kinect as a measuring tool. Second, the Kinect is employed as a substitute DAQ system for acquiring range data and tracking the motion of objects of interest. At last, the Kinect serves as a novel human-computer interface for tracking the user's entire body motion and recognizing their voices. Using the method described here, three major aspects of educational VR development can be accomplished with an inexpensive and commercially available Kinect. [ABSTRACT FROM AUTHOR]

Automation, driven by informatics, enables manufacturing companies to increase productivity and meet market demands for cost-effective and high-quality products. However, many manufacturing operations across industry verticals continue to be manual even today. One such example is the manual assembly of the final trim and wheels in an automotive production line where there is heavy reliance on human decision-making pertaining to when, where and how to install components on and inside a constantly moving vehicle body. The main aim of this work is to develop a rule-based decision support system that will enable an automation solution to make human-like decisions in moving assembly operations. The wheel loading operation is chosen as a case study and a decision support framework and tool is developed and successfully tested using multiple assembly scenarios generated from experimental data provided by gaming interface sensors installed on the laboratory-based shopfloor. The resulting decision support system has the potential to enable the automation of moving assembly operations in various sectors of the manufacturing industry. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory.

The Microsoft Kinect is arguably the most popular RGB-D camera currently on the market, partially due to its low cost. It offers many advantages for the measurement of dynamic phenomena since it can directly measure three-dimensional coordinates of objects at video frame rate using a single sensor. This paper presents the results of an investigation into the development of a Microsoft Kinect-based system for measuring the deflection of reinforced concrete beams subjected to cyclic loads. New segmentation methods for object extraction from the Kinect’s depth imagery and vertical displacement reconstruction algorithms have been developed and implemented to reconstruct the time-dependent displacement of concrete beams tested in laboratory conditions. The results demonstrate that the amplitude and frequency of the vertical displacements can be reconstructed with submillimetre and milliHz-level precision and accuracy, respectively.

Detailed 3D plant architectural data have numerous applications in plant science, but many existing approaches for 3D data collection are time-consuming and/or require costly equipment. Recently, there has been rapid growth in the availability of low-cost, 3D cameras and related open source software applications. 3D cameras may provide measurements of key components of plant architecture such as stem diameters and lengths, however, few tests of 3D cameras for the measurement of plant architecture have been conducted. Here, we measured Salix branch segments ranging from 2–13 mm in diameter with an Asus Xtion camera to quantify the limits and accuracy of branch diameter measurement with a 3D camera. By scanning at a variety of distances we also quantified the effect of scanning distance. In addition, we also test the sensitivity of the program KinFu for continuous 3D object scanning and modeling as well as other similar software to accurately record stem diameters and capture plant form (6 mm, Asus Xtion may provide a novel method for the collection of 3D data on the branching architecture of woody plants. Improvements in camera measurement accuracy and available software are likely to further improve the utility of 3D cameras for plant sciences in the future.

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.

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 (ICC3,k=0.99) and consistency (ICC2,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. [ABSTRACT FROM AUTHOR]

This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp-and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients. [ABSTRACT FROM AUTHOR]

Extensive research has been done on various techniques for navigation of automobiles and robots using diverse motion sensing input devices. This paper presents a unique approach of using Kinect for navigation with options of controlling manually or autonomously or with combination of both. A rover with the navigation control through human gestures, voice and self (adaptive) is presented. The prime application of this project is in the automotive industries. A prototype is developed and tested. The main objective is to facilitate the users by introducing gesture and voice control automobile model. The Kinect (optical sensor) allows users to experience a human robot interaction. It is able to provide an intuitive, robust and fun form of interaction It can control a wide range of operations such as acceleration, braking, steering, media, volumes etc. just from gesture or voice. This rover can be used in home, industries, hospitals, restaurants etc. The system is intelligent and adaptive to avoid obstacles on its path. Gesture and voice controlling is very helpful for handicapped people to achieve certain tasks, such as driving etc. Rover can also be used in auto mode to follow the points that are given through Google/Bing map. The auto mode converts the rover into a self-driving car. [ABSTRACT FROM AUTHOR]

The use of motion controllers and cameras such as Microsoft Kinect as a gaming peripheral have increased the immersion of computer games today. Moving to using gestures instead of conventional handheld game controllers has increased the interest of players. Xbox 360, Xbox-One and the games that have been developed to be used with Kinect enable users to play using gestures, but this limits us to play our old favorite games and the new non-Kinect based games with same conventional peripherals. To overcome this limitation, we have developed a user friendly interface in MATLAB that replaces keyboard and mouse controls with our own designed gestures and enables us to play all non-Kinect based games using Kinect. For testing and demonstration purpose, we have applied the developed interface to three games which are Counter-Strike Global Offense, Candy Crush and Asphalt 8 Airborne. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Weight training leads to muscle injury for inexperience exercise. The deadlift form is one of high risk posture of weight training. Therefore, the prevention of incorrect deadlift form needs an expert trainer. Some people are not be able to effort the private trainer cost. This research presents the deadlift form analysis system using Microsoft Kinect. Microsoft Kinect can be applied to detect the deadlift form with its full body motion capability the Chaffin’s biomechanical modeling. The Chaffin’s model is used to calculate two forces, compression force and shear force, on lumbosacral disc (L5/S1). Results of this research can help the people to practice the corrected deadlift form and reducing injuries on lumbosacral disc (L5/S1) using Microsoft Kinect. The accuracy of this system is 80.9%. [ABSTRACT FROM AUTHOR]

A robot must have a good understanding of the environment for autonomous navigation.Mobile robot using fixed laser range scanner can only detect obstacle on a plane level. This may cause important obstacles not to be appropriately detected. This will cause the map generated to be inaccurate and collision may actually occur during autonomous navigation. Microsoft Kinect is known to provide a low-cost 3-D data which can be used for mobile robot navigation. Many researchers focused on obstacles above ground level, and not negative obstacles such as holes or stairs. This article proposes the usage of Kinect sensor to detect negative obstacles and converts it into laser scan data. Positive obstacle is defined as the obstacle above the floor surface and negative obstacle is defined as the obstacle below the floor surface. Projection method is used to convert positive obstacle data from Kinect sensor to laser scan data. For negative obstacles detection, farthest point method and virtual floor projection method are used. The laser scan data from positive and negative obstacles are then combined to get an improved laser scan data, which includes all obstacles that are important for a robot to see. The negative obstacle detection methods are tested in simulated indoor environment and also experimental in a real environment. The simulation and experimental results have demonstrated the effectiveness of our proposed method to detect and map negative obstacles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]