Background Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the assessment of 17 kinematic variables commonly used in the analysis of upper limb reaching in stroke. Methods 26 healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce a behaviour similar to that of a person with a stroke. 3D upper limb and trunk motion were simultaneously recorded with the Kinect v2 (Microsoft, USA) and with the VICON (OxfordMetrics, UK), the latter being the reference system. For each kinematic outcome, the validity of the Kinect was assessed with ICC, linear regression and Bland & Altman plots. Results The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability, indicating that these variables should be interpreted with caution. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess Cartesian and angular kinematics over time, rendering variables such as the number of velocity peaks and the peak hand velocity unusable. Conclusions Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the limitations of the Kinect for the instantaneous tracking of the hand and elbow.

/pdf/validity-and-reliability-of-kinect-v2-for-quantifying-upper-1bfuji5w.pdf

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Shoulder Range of Motion (ROM) has been studied with several devices and methods in recent years. Accurate tracking and assessment of shoulder movements could help us to understand the pathogenetic mechanism of specific conditions in quantifying the improvements after rehabilitation. The assessment methods can be classified as subjective and objective. However, self-reported methods are not accurate, and they do not allow the collection of specific information. Therefore, developing measurement devices that provide quantitative and objective data on shoulder function and range of motion is important. A comprehensive search of PubMed and IEEE Xplore was conducted. The sensor fusion algorithm used to analyze shoulder kinematics was described in all studies involving wearable inertial sensors. Eleven articles were included. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias (QUADAS-2). The finding showed that the Kalman filter and its variants UKF and EKF are used in the majority of studies. Alternatives based on complementary filters and gradient descent algorithms have been reported as being more computationally efficient. Many approaches and algorithms have been developed to solve this problem. It is useful to fuse data from different sensors to obtain a more accurate estimation of the 3D position and 3D orientation of a body segment. The sensor fusion technique makes this integration reliable. This systematic review aims to redact an overview of the literature on the sensor fusion algorithms used for shoulder motion tracking. 

/pdf/motion-tracking-algorithms-based-on-wearable-inertial-sensor-2udykbve.pdf

Motion Tracking Algorithms Based on Wearable Inertial Sensor: A Focus on Shoulder

To provide stroke patients with good rehabilitation training, the rehabilitation robot should ensure that each joint of the limb of the patient does not exceed its joint range of motion. Based on the machine vision combined with an RGB-Depth (RGB-D) camera, a convenient and quick human-machine interaction method to measure the lower limb joint range of motion of the stroke patient is proposed. By analyzing the principle of the RGB-D camera, the transformation relationship between the camera coordinate system and the pixel coordinate system in the image is established. Through the markers on the human body and chair on the rehabilitation robot, an RGB-D camera is used to obtain their image data with relative position. The threshold segmentation method is used to process the image. Through the analysis of the image data with the least square method and the vector product method, the range of motion of the hip joint, knee joint in the sagittal plane, and hip joint in the coronal plane could be obtained. Finally, to verify the effectiveness of the proposed method for measuring the lower limb joint range of motion of human, the mechanical leg joint range of motion from a lower limb rehabilitation robot, which will be measured by the angular transducers and the RGB-D camera, was used as the control group and experiment group for comparison. The angle difference in the sagittal plane measured by the proposed detection method and angle sensor is relatively conservative, and the maximum measurement error is not more than 2.2 degrees. The angle difference in the coronal plane between the angle at the peak obtained by the designed detection system and the angle sensor is not more than 2.65 degrees. This paper provides an important and valuable reference for the future rehabilitation robot to set each joint range of motion limited in the safe workspace of the patient.

/pdf/measurement-method-of-human-lower-limb-joint-range-of-motion-2yaq2p7v8i.pdf

Measurement Method of Human Lower Limb Joint Range of Motion Through Human-Machine Interaction Based on Machine Vision.

Abstract Background Range of motion (ROM) measurements are essential for diagnosing and evaluating upper extremity conditions. Clinical goniometry is the most commonly used methods but it is time-consuming and skill-demanding. Recent advances in human tracking algorithm suggest potential for automatic angle measuring from RGB images. It provides an attractive alternative for at-distance measuring. However, the reliability of this method has not been fully established. The purpose of this study is to evaluate if the results of algorithm are as reliable as human raters in upper limb movements. Methods Thirty healthy young adults (20 males, 10 females) participated in this study. Participants were asked to performed a 6-motion task including movement of shoulder, elbow and wrist. Images of movements were captured by commercial digital cameras. Each movement was measured by a pose tracking algorithm (OpenPose) and compared with the surgeon-measurement results. The mean differences between the two measurements were compared. Pearson correlation coefficients were used to determine the relationship. Reliability was investigated by the intra-class correlation coefficients. Results Comparing this algorithm-based method with manual measurement, the mean differences were less than 3 degrees in 5 motions (shoulder abduction: 0.51; shoulder elevation: 2.87; elbow flexion:0.38; elbow extension:0.65; wrist extension: 0.78) except wrist flexion. All the intra-class correlation coefficients were larger than 0.60. The Pearson coefficients also showed high correlations between the two measurements ( p &lt; 0.001). Conclusions Our results indicated that pose estimation is a reliable method to measure the shoulder and elbow angles, supporting RGB images for measuring joint ROM. Our results presented the possibility that patients can assess their ROM by photos taken by a digital camera. Trial registration This study was registered in the Clinical Trials Center of The First Affiliated Hospital, Sun Yat-sen University (2021–387). 

https://bmcmusculoskeletdisord.biomedcentral.com/counter/pdf/10.1186/s12891-022-05826-4

Reliability of a human pose tracking algorithm for measuring upper limb joints: comparison with photography-based goniometry

The interruption of rehabilitation activities caused by the COVID-19 lockdown has significant health negative consequences for the population with physical disabilities. Thus, measuring the range of motion (ROM) using remotely taken photographs, which are then sent to specialists for formal assessment, has been recommended. Currently, low-cost Kinect motion capture sensors with a natural user interface are the most feasible implementations for upper limb motion analysis. An active range of motion (AROM) measuring system based on a Kinect v2 sensor for upper limb motion analysis using Fugl-Meyer Assessment (FMA) scoring is described in this paper. Two test groups of children, each having eighteen participants, were analyzed in the experimental stage, where upper limbs’ AROM and motor performance were assessed using FMA. Participants in the control group (mean age of 7.83 ± 2.54 years) had no cognitive impairment or upper limb musculoskeletal problems. The study test group comprised children aged 8.28 ± 2.32 years with spastic hemiparesis. A total of 30 samples of elbow flexion and 30 samples of shoulder abduction of both limbs for each participant were analyzed using the Kinect v2 sensor at 30 Hz. In both upper limbs, no significant differences (p < 0.05) in the measured angles and FMA assessments were observed between those obtained using the described Kinect v2-based system and those obtained directly using a universal goniometer. The measurement error achieved by the proposed system was less than ±1° compared to the specialist’s measurements. According to the obtained results, the developed measuring system is a good alternative and an effective tool for FMA assessment of AROM and motor performance of upper limbs, while avoiding direct contact in both healthy children and children with spastic hemiparesis.

/pdf/kinect-v2-assisted-semi-automated-method-to-assess-upper-2cd724ay.pdf

Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children

Background: Objective assessment of shoulder joint active range of motion (AROM) is critical to monitor patient progress after conservative or surgical intervention. Advancements in miniature devices have led researchers to validate inertial sensors to capture human movement. This study investigated the construct validity as well as intra- and inter-rater reliability of active shoulder mobility measurements using a coupled system of inertial sensors and the Microsoft Kinect (HumanTrak). Methods: 50 healthy participants with no history of shoulder pathology were tested bilaterally for fixed and free ROM: (1) shoulder flexion, and (2) abduction using HumanTrak and goniometry. The repeat testing of the standardised protocol was completed after seven days by two physiotherapists. Results: All HumanTrak shoulder movements demonstrated adequate reliability (intra-class correlation (ICC) ≥ 0.70). HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.93 and 0.85) than goniometry (ICCs: 0.75 and 0.53) for measuring free shoulder flexion and abduction AROM, respectively. Similarly, HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.81 and 0.94) than goniometry (ICCs: 0.70 and 0.93) for fixed flexion and abduction AROM, respectively. Construct validity between HumanTrak and goniometry was adequate except for free abduction. The differences between raters were predominately acceptable and below ±10°. Conclusions: These results indicated that the HumanTrak system is an objective, valid and reliable way to assess and track shoulder ROM.

https://www.mdpi.com/1424-8220/20/24/7238/pdf

The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion.

There is limited literature exploring the relationship between simulation training and extracorporeal cardiopulmonary resuscitation (ECPR) outcomes. We examined whether there was an association between the implementation of an in situ simulation training program and ECPR utilisation, time to extracorporeal membrane oxygenation (ECMO), and neurologically intact survival.In this retrospective pre-post study of in-hospital cardiac arrests (IHCA) and out-of-hospital cardiac arrests (OHCA), we analysed data for all patients recorded as receiving ECPR from September 2009 to December 2020 at our institution, relative to the implementation of an in situ ECPR simulation training program and a standardised procedure for high-quality ECPR. The primary outcome was Cerebral Performance Category (CPC) 1 or 2 at hospital discharge.There were 27 patients in the pre-intervention period and 39 patients in the post-intervention period. The median ECPR rate per year was 2 pre-intervention and 7 post-intervention (p = 0.073). There was an association between the implementation of the program and decreased median time from OHCA to ECMO flow, from 87 (IQR 78-95) minutes pre-intervention to 70 (IQR 69-72) minutes post-intervention (p = 0.002). Median time from IHCA to ECMO flow was 40 (IQR 20-75) minutes pre-intervention and 28 (IQR 16-41) minutes post-intervention (p = 0.134). Survival with CPC 1 or 2 was 7/27 (25.9%) pre-intervention and 15/39 (38.5%) post-intervention (p = 0.288).We observed an association between the implementation of an ECPR-specific simulation program and decreased time from OHCA to ECMO flow. There was no association between the implementation of the program and neurologically intact survival at hospital discharge. 

https://doi.org/10.1016/j.resplu.2022.100234

Andrew C. Read

Papers

The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion.

The effect of a structured ECPR protocol aided by specific simulation training in a quaternary ECMO centre: A retrospective pre-post study