The efficacy of the Microsoft KinectTM to assess human bimanual coordination

Validity, reliability and accuracy of inertial measurement units (IMUs) to measure angles: application in swimming

The first objective was to test the validity, reliability and accuracy of paired inertial measurement units (IMUs) to assess absolute angles relative to Vicon and OptiTrack systems. The potential impacts of slow vs. rapid and intermittent vs. continuous movements were tested during 2D laboratory analyses and 3D ecological context analysis. The second objective was to test the IMUs alone in an ecological activity (i.e., front crawl) that encompassed the previous independent variables to quantify inter-cyclic variability. Slow and intermittent motion ensured high to reasonable validity, reliability and accuracy. Rapid motion revealed an out-of-phase pattern for temporal reliability and lower validity, which was also visible in 3D. Also, spatial reliability and accuracy decreased in 3D, mainly due to discrepancies in local maximums, whereas temporal reliability remained in-phase. For the second objective, inter-cyclic variability did not exceed 12° based on root mean square error (RMSE). Therefore, IMUs should be considered valuable supplements to optoelectronic systems if users carefully position the sensors in rigid clusters and calibrate them to integrate potential offsets. Drift correction by spline interpolation or normalisation of the absolute data should also be considered as additional techniques that increase IMU performance in ecological contexts of performance.

Upper limb joint coordination preserves hand kinematics after a traumatic brachial plexus injury

Background

Traumatic brachial plexus injury (TBPI) causes a sensorimotor deficit in upper limb (UL) movements.

Objective

Our aim was to investigate the arm–forearm coordination of both the injured and uninjured UL of TBPI subjects.

Methods

TBPI participants (n = 13) and controls (n = 10) matched in age, gender, and anthropometric characteristics were recruited. Kinematics from the shoulder, elbow, wrist, and index finger markers were collected, while upstanding participants transported a cup to their mouth and returned the UL to a starting position. The UL coordination was measured through the relative phase (RP) between arm and forearm phase angles and analyzed as a function of the hand kinematics.

Results

For all participants, the hand transport had a shorter time to peak velocity (p < 0.01) compared to the return. Also, for the control and the uninjured TBPI UL, the RP showed a coordination pattern that favored forearm movements in the peak velocity of the transport phase (p < 0.001). TBPI participants' injured UL showed a longer movement duration in comparison to controls (p < 0.05), but no differences in peak velocity, time to peak velocity, and trajectory length, indicating preserved hand kinematics. The RP of the injured UL revealed altered coordination in favor of arm movements compared to controls and the uninjured UL (p < 0.001). Finally, TBPI participants' uninjured UL showed altered control of arm and forearm phase angles during the deceleration of hand movements compared to controls (p < 0.05).

Conclusion

These results suggest that UL coordination is reorganized after a TBPI so as to preserve hand kinematics.

Virtual and Augmented Reality in Cardiovascular Care: State-of-the-Art and Future Perspectives

Applications of virtual reality (VR) and augmented reality (AR) assist both health care providers and patients in cardiovascular education, complementing traditional learning methods. Interventionalists have successfully used VR to plan difficult procedures and AR to facilitate complex interventions. VR/AR has already been used to treat patients, during interventions in rehabilitation programs and in immobilized intensive care patients. There are numerous additional potential applications in the catheterization laboratory. By using AR, interventionalists could combine visual fluoroscopy information projected and registered on the patient body with data derived from preprocedural imaging and live fusion of different imaging modalities such as fluoroscopy with echocardiography. Persistent technical challenges to overcome include the integration of different imaging modalities into VR/AR and the harmonization of data flow and interfaces. Cybersickness might exclude some patients and users from the potential benefits of VR/AR. Critical ethical considerations arise in the application of VR/AR in vulnerable patients. In addition, digital applications must not distract physicians from the patient. It is our duty as physicians to participate in the development of these innovations to ensure a virtual health reality benefit for our patients in a real-world setting. The purpose of this review is to summarize the current and future role of VR and AR in different fields within cardiology, its challenges, and perspectives.

Motion tracking in developmental research: Methods, considerations, and applications

In this chapter, we explore the use of motion tracking methodology in developmental research. With motion tracking, also called motion capture, human movements can be precisely recorded and analyzed. Motion tracking provides developmental researchers with objective measurements of motor and (socio-)cognitive development. It can further be used to create carefully-controlled stimuli videos and can offer means of measuring development outside of the lab. We discuss three types of motion tracking that lend themselves to developmental applications. First, marker-based systems track optical or electromagnetic markers or sensors placed on the body and offer high accuracy measurements. Second, markerless methods entail image processing of videos to track the movement of bodies without participants being hindered by physical markers. Third, inertial motion tracking measures three-dimensional movements and can be used in a variety of settings. The chapter concludes by examining three example topics from developmental literature in which motion tracking applications have contributed to our understanding of human development.

Three-dimensional cameras and skeleton pose tracking for physical function assessment: A review of uses, validity, current developments and Kinect alternatives

BACKGROUND

Three-dimensional camera systems that integrate depth assessment with traditional two-dimensional images, such as the Microsoft Kinect, Intel Realsense, StereoLabs Zed and Orbecc, hold great promise as physical function assessment tools. When combined with point cloud and skeleton pose tracking software they can be used to assess many different aspects of physical function and anatomy. These assessments have received great interest over the past decade, and will likely receive further study as the integration of depth sensing and augmented reality smartphone cameras occurs more in everyday life.

RESEARCH QUESTION

The aim of this review is to discuss how these devices work, what options are available, the best methods for performing assessments and how they can be used in the future.

METHODS

Firstly, a review of the Microsoft Kinect devices and associated artificial intelligence, automated skeleton tracking algorithms is provided. This includes a narrative critique of the validity and clinical utility of these devices for assessing different aspects of physical function including spatiotemporal, kinematic and inverse dynamics data derived from gait and balance trials, and anatomical assessments performed using the depth sensor information. Methods for improving the accuracy of data are examined, including multiple-camera systems and sensor fusion with inertial monitoring units, model fitting, and marker tracking. Secondly, alternative hardware, including other structured light and time of flight methods, stereoscopic cameras and augmented reality leveraging smartphone and tablet cameras to perform measurements in three-dimensional space are summarised. Software options related to depth sensing cameras are then discussed, focussing on recent advances such as OpenPose and web-based methods such as PoseNet.

RESULTS AND SIGNIFICANCE

The clinical and non-laboratory utility of these devices holds great promise for physical function assessment, and recent developments could strengthen their ability to provide important and impactful health-related data.

Evaluation of the Leap Motion Controller during the performance of visually-guided upper limb movements

Kinematic analysis of upper limb reaching provides insight into the central nervous system control of movements. Until recently, kinematic examination of motor control has been limited to studies conducted in traditional research laboratories because motion capture equipment used for data collection is not easily portable and expensive. A recently developed markerless system, the Leap Motion Controller (LMC), is a portable and inexpensive tracking device that allows recording of 3D hand and finger position. The main goal of this study was to assess the concurrent reliability and validity of the LMC as compared to the Optotrak, a criterion-standard motion capture system, for measures of temporal accuracy and peak velocity during the performance of upper limb, visually-guided movements. In experiment 1, 14 participants executed aiming movements to visual targets presented on a computer monitor. Bland-Altman analysis was conducted to assess the validity and limits of agreement for measures of temporal accuracy (movement time, duration of deceleration interval), peak velocity, and spatial accuracy (endpoint accuracy). In addition, a one-sample t-test was used to test the hypothesis that the error difference between measures obtained from Optotrak and LMC is zero. In experiment 2, 15 participants performed a Fitts’ type aiming task in order to assess whether the LMC is capable of assessing a well-known speed-accuracy trade-off relationship. Experiment 3 assessed the temporal coordination pattern during the performance of a sequence consisting of a reaching, grasping, and placement task in 15 participants. Results from the t-test showed that the error difference in temporal measures was significantly different from zero. Based on the results from the 3 experiments, the average temporal error in movement time was 40±44 ms, and the error in peak velocity was 0.024±0.103 m/s. The limits of agreement between the LMC and Optotrak for spatial accuracy measures ranged between 2–5 cm. Although the LMC system is a low-cost, highly portable system, which could facilitate collection of kinematic data outside of the traditional laboratory settings, the temporal and spatial errors may limit the use of the device in some settings.