Modifying Kinect placement to improve upper limb joint angle measurement accuracy

The elbow is the load-bearing joint during arm swing

BACKGROUND

Arm swing plays a role in gait by accommodating forward movement through trunk balance. This study evaluates the biomechanical characteristics of arm swing during gait.

METHODS

The study performed computational musculoskeletal modeling based on motion tracking in 15 participants without musculoskeletal or gait disorder. A three-dimensional (3D) motion tracking system using three Azure Kinect (Microsoft) modules was used to obtain information in the 3D location of shoulder and elbow joints. Computational modeling using AnyBody Modeling System was performed to calculate the joint moment and range of motion (ROM) during arm swing.

RESULTS

The mean ROM of the dominant elbow was 29.7°±10.2° and 14.2°±3.2° in flexion-extension and pronation-supination, respectively. The mean joint moment of the dominant elbow was 56.4±12.7 Nm, 25.6±5.2 Nm, and 19.8±4.6 Nm in flexion-extension, rotation, and abduction-adduction, respectively.

CONCLUSIONS

The elbow bears the load created by gravity and muscle contracture in dynamic arm swing movement.

Static and dynamic validation of kinect for ergonomic postural analysis using electro-goniometers as a gold standard:A preliminary study

BACKGROUND

Evaluation of the working postures and development of new techniques are paramount in reducing the awkward postures and occurrence of musculoskeletal disorders (MSDs). The Kinect sensor, a portable and cost-effective device, appears to be a promising alternative to study work postures.

OBJECTIVE

The current study aimed to evaluate the validity of Kinect against the gold-standard instrument (electro-goniometers) for body joint angle measurements.

METHODS

A unique software application was developed to measure the critical body joint angles for postural evaluation by using the Kinect's skeletal tracking feature. The body joint angle data of ten volunteers were measured simultaneously by both Kinect and electro-goniometers. The validation analysis was conducted in both static and dynamic domains of application.

RESULTS

Minimal variation was observed between the two techniques, and the Kinect correlated well for upper-arm joint angles of 45∘, 60∘ and 90∘; lower-arm joint angles of 30∘, 45∘, 60∘, and 90∘; straight neck position, neck joint angle at maximum possible flexion; straight trunk position, trunk bend angle at full flexion. In dynamic analysis, four out of five ICC values were > 0.75 except for the upper arm. Discrepancies in the results indicated the disapproval of Kinect for only wrist measurements.

CONCLUSION

The results of the static and dynamic studies gave a sufficient basis to consider the Kinect tool as an alternative to contemporary posture-based ergonomic evaluation methods.

Construct validation of a general movement competence assessment utilising active video gaming technology

Introduction: The assessment of children's motor competence is an important concern as physical inactivity has been linked with poor movement quality and aspects of well-being such as low self-esteem. The General Movement Competence Assessment (GMCA) is a new instrument that was developed using active video gaming technology. Methods: Confirmatory factor analysis was conducted to examine the internal validity of the GMCA in a sample of 253 typically developing children (135 boys and 118 girls), aged 7-12 years old (9.9 ± 1.6 years). Further, a second-order confirmatory factor analysis examined how the four constructs fit onto the higher-order variable of movement competence. Results: Results revealed that the first-order four-construct model of the GMCA was a good fit (CFI 0.98; TLI 0.98; RMSEA 0.05). The second-order confirmatory factor analysis revealed that the four constructs loaded directly onto movement competence. It accounted for 95.44% of the variance which is approximately 20% more than the first-order model. The internal structure of the GMCA identified four constructs of movement competence (i.e., stability, object-control, locomotion and dexterity) based on the study sample. Discussion: Performance trends in the general movement competence assessment support empirical evidence that movement competence improves as children age. Results suggest that active video games have considerable potential to help assess general motor competency in the wider population. Future work may consider the sensitivity of motion-sensing technologies in detecting developmental changes over time.

Validity and Reliability of a Depth Camera-Based Quantitative Measurement for Joint Motion of the Hand

Purpose

Quantitative measurement of hand motion is essential in evaluating hand function. This study aimed to investigate the validity and reliability of a novel depth camera-based contactless automatic measurement system to assess hand range of motion and its potential benefits in clinical applications.

Methods

Five hand gestures were designed to evaluate the hand range of motion using a depth camera-based measurement system. Seventy-one volunteers were enrolled in performing the designed hand gestures. Then, the hand range of motion was measured with the depth camera and manual procedures. System validity was evaluated based on 3 dimensions: repeatability, within-laboratory precision, and reproducibility. For system reliability, linear evaluation, the intraclass correlation coefficient, paired t -test and bias were employed to test the consistency and difference between the depth camera and manual procedures.

Results

When measuring phalangeal length, repeatability, within-laboratory precision, and reproducibility were 2.63%, 12.87%, and 27.15%, respectively. When measuring angles of hand motion, the mean repeatability and within-laboratory precision were 1.2° and 3.3° for extension of 5 digits, 2.7° and 10.2° for flexion of 4 fingers, and 3.1° and 5.3° for abduction of 4 metacarpophalangeal joints, respectively. For system reliability, the results showed excellent consistency (intraclass correlation coefficient = 0.823; P < .05) and good linearity with the manual procedures (r = 0.909-0.982, approximately; P < .001). Besides, 78.3% of the measurements were clinically acceptable.

Conclusions

Our depth camera-based evaluation system provides acceptable validity and reliability in measuring hand range of motion and offers potential benefits for clinical care and research in hand surgery. However, further studies are required before clinical application.

Clinical relevance

This study suggests a depth camera-based contactless automatic measurement system holds promise for assessing hand range of motion in hand function evaluation, diagnosis, and rehabilitation for medical staff. However, it is currently not adequate for all clinical applications.

A Simple Method to Optimally Select Upper-Limb Joint Angle Trajectories from Two Kinect Sensors during the Twisting Task for Posture Analysis

A trunk-twisting posture is strongly associated with physical discomfort. Measurement of joint kinematics to assess physical exposure to injuries is important. However, using a single Kinect sensor to track the upper-limb joint angle trajectories during twisting tasks in the workplace is challenging due to sensor view occlusions. This study provides and validates a simple method to optimally select the upper-limb joint angle data from two Kinect sensors at different viewing angles during the twisting task, so the errors of trajectory estimation can be improved. Twelve healthy participants performed a rightward twisting task. The tracking errors of the upper-limb joint angle trajectories of two Kinect sensors during the twisting task were estimated based on concurrent data collected using a conventional motion tracking system. The error values were applied to generate the error trendlines of two Kinect sensors using third-order polynomial regressions. The intersections between two error trendlines were used to define the optimal data selection points for data integration. The finding indicates that integrating the outputs from two Kinect sensor datasets using the proposed method can be more robust than using a single sensor for upper-limb joint angle trajectory estimations during the twisting task.

[Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand]

OBJECTIVE

To validate the use of key point matrix technology based contactless automatic measurement for evaluation of joint motion of hand.

METHODS

Thirty-three volunteers were enrolled to evaluate the extension and flexion of hand joints between May 2021 and November 2021. There were 20 males and 13 females, the age ranged from 16 to 70 years with an average of 30.2 years. The extension angles of 14 joints of 5 fingers (including hyperextension) and the flexion angles of 12 joints of 4 fingers (excluding thumb) of volunteers were measured by key point matrix technology and manual goniometer, respectively. Then 5 participants and repeated measurement experiment were employed to test the system repeatability and accuracy; 28 participants and paired measurement experiment were employed to test the system accuracy.

RESULTS

The average repeatability of finger joint motion measured by the key point matrix technology was 1.801° (extension) and 7.823° (flexion), respectively. Compared with manual measurement, the average differences of each finger joint measured by the key point matrix technology were 3.225° in extension and 14.145° in flexion, respectively. The key point matrix technology based contactless automatic evaluation system offered excellent consistency with the manual goniometers ( ICC=0.875). While most of the consistency with manual goniometer of individual joints were at moderate levels (median of ICC, 0.440). The correlation coefficients between the measurement results of the two methods were mainly positive in the extension of the joint ( P<0.05) and negative in the flexion of the joints ( P<0.05).

CONCLUSION

The key point matrix technology based contactless automatic evaluation provides sufficient measurement repeatability and accuracy in evaluation for the joint motion of hand.

Use of the Azure Kinect to measure foot clearance during obstacle crossing: A validation study

Obstacle crossing is typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggest that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.

Promoting endoscopists' health through cutting-edge motion analysis technology: Accuracy and precision of ergonomic motion tracking system for endoscopy suite (EMTES)

OBJECTIVES

Endoscopists often suffer from musculoskeletal disorders due to posture-specific workloads imposed by precise maneuvering or long procedural duration. An ergonomic motion tracking system for endoscopy suite (EMTES) was developed using Azure Kinect sensors to evaluate the occlusion, accuracy, and precision, focusing mainly on upper and lower limb movements.

METHODS

Three healthy male participants pointed the prescribed points for 5 s on the designated work envelopes and their coordinates were measured. The mean occlusion rate (%) of the 32 motion tracking landmarks, standard deviation (SD) of distance and orientation, and partial regression coefficient (β) and R2 model fit for accuracy were calculated using the time series of coordinates data of the upper/lower limb movements.

RESULTS

The mean occlusion rate was 5.2 ± 10.6% and 1.6 ± 1.4% for upper and lower limb movements, respectively. Of the 32 landmarks, 28 (87.5%) had occlusion rates of 10% or less. The mean SDs of 4.2 mm for distance and 1.2° for orientation were found. Most of the R2 values were over 0.9. In the case of right upper/lower limb measurement for orientation, β coefficients ranged from 0.82 to 1.36.

CONCLUSION

EMTES is reliable in calculating occlusion, precision, and accuracy for practical motion-tracking measurements in endoscopists.

Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system's performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor

IMPORTANCE

Along with growth in telerehabilitation, a concurrent need has arisen for standardized methods of tele-evaluation.

OBJECTIVE

To examine the feasibility of using the Kinect sensor in an objective, computerized clinical assessment of upper limb motor categories.

DESIGN

We developed a computerized Mallet classification using the Kinect sensor. Accuracy of computer scoring was assessed on the basis of reference scores determined collaboratively by multiple evaluators from reviewing video recording of movements. In addition, using the reference score, we assessed the accuracy of the typical clinical procedure in which scores were determined immediately on the basis of visual observation. The accuracy of the computer scores was compared with that of the typical clinical procedure.

SETTING

Research laboratory.

PARTICIPANTS

Seven patients with stroke and 10 healthy adult participants. Healthy participants intentionally achieved predetermined scores.

OUTCOMES AND MEASURES

Accuracy of the computer scores in comparison with accuracy of the typical clinical procedure (immediate visual assessment).

RESULTS

The computerized assessment placed participants' upper limb movements in motor categories as accurately as did typical clinical procedures.

CONCLUSIONS AND RELEVANCE

Computerized clinical assessment using the Kinect sensor promises to facilitate tele-evaluation and complement telehealth applications.

WHAT THIS ARTICLE ADDS

Computerized clinical assessment can enable patients to conduct evaluations remotely in their homes without therapists present.

Kinect-based assessment of proximal arm non-use after a stroke

Background

After a stroke, during seated reaching with their paretic upper limb, many patients spontaneously replace the use of their arm by trunk compensation movements, even though they are able to use their arm when forced to do so. We previously quantified this proximal arm non-use (PANU) with a motion capture system (Zebris, CMS20s). The aim of this study was to validate a low-cost Microsoft Kinect-based system against the CMS20s reference system to diagnose PANU.

Methods

In 19 hemiparetic stroke individuals, the PANU score, reach length, trunk length, and proximal arm use (PAU) were measured during seated reaching simultaneously by the Kinect (v2) and the CMS20s over two testing sessions separated by two hours.

Results

Intraclass correlation coefficients (ICC) and linear regression analysis showed that the PANU score (ICC = 0.96, r² = 0.92), reach length (ICC = 0.81, r² = 0.68), trunk length (ICC = 0.97, r² = 0.94) and PAU (ICC = 0.97, r² = 0.94) measured using the Kinect were strongly related to those measured using the CMS20s. The PANU scores showed good test-retest reliability for both the Kinect (ICC = 0.76) and CMS20s (ICC = 0.72). Bland and Altman plots showed slightly reduced PANU scores in the re-test session for both systems (Kinect: − 4.25 ± 6.76; CMS20s: − 4.71 ± 7.88), which suggests a practice effect.

Conclusion

We showed that the Kinect could accurately and reliably assess PANU, reach length, trunk length and PAU during seated reaching in post stroke individuals. We conclude that the Kinect can offer a low-cost and widely available solution to clinically assess PANU for individualised rehabilitation and to monitor the progress of paretic arm recovery.

Trial registration

The study was approved by The Ethics Committee of Montpellier, France (N°ID-RCB: 2014-A00395–42) and registered in Clinical Trial (N° NCT02326688, Registered on 15 December 2014, https://clinicaltrials.gov/ct2/show/results/NCT02326688).

3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application

Optoelectronic devices are the gold standard for 3D evaluation in clinics, but due to the complexity of this kind of hardware and the lack of access for patients, affordable, transportable, and easy-to-use systems must be developed to be largely used in daily clinics. The Kinect^TM sensor has various advantages compared to optoelectronic devices, such as its price and transportability. However, it also has some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points, which makes 3D evaluation impossible. To overcome these limitations, a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises, allowing functional evaluation while performing physical rehabilitation. To validate this new approach, a two-step method was used. The first step was a laboratory validation where the results obtained with the Kinect^TM were compared with the results obtained with an optoelectronic device; 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of this kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns could be observed. The new methodology and the different steps of the validations are presented in this paper.