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

Three-dimensional kinematic analysis can improve the efficacy of acupoint selection for post-stroke patients with upper limb spastic paresis: A randomized controlled trial

BACKGROUND

China is seeing a growing demand for rehabilitation treatments for post-stroke upper limb spastic paresis (PSSP-UL). Although acupuncture is known to be effective for PSSP-UL, there is room to enhance its efficacy.

OBJECTIVE

This study explored a semi-personalized acupuncture approach for PSSP-UL that used three-dimensional kinematic analysis (3DKA) results to select additional acupoints, and investigated the feasibility, efficacy and safety of this approach.

DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS

This single-blind, single-center, randomized, controlled trial involved 74 participants who experienced a first-ever ischemic or hemorrhagic stroke with spastic upper limb paresis. The participants were then randomly assigned to the intervention group or the control group in a 1:1 ratio. Both groups received conventional treatments and acupuncture treatment 5 days a week for 4 weeks. The main acupoints in both groups were the same, while participants in the intervention group received additional acupoints selected on the basis of 3DKA results. Follow-up assessments were conducted for 8 weeks after the treatment.

MAIN OUTCOME MEASURES

The primary outcome was the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) response rate (≥ 6-point change) at week 4. Secondary outcomes included changes in motor function (FMA-UE), Brunnstrom recovery stage (BRS), manual muscle test (MMT), spasticity (Modified Ashworth Scale, MAS), and activities of daily life (Modified Barthel Index, MBI) at week 4 and week 12.

RESULTS

Sixty-four participants completed the trial and underwent analyses. Compared with control group, the intervention group exhibited a significantly higher FMA-UE response rate at week 4 (χ2 = 5.479, P = 0.019) and greater improvements in FMA-UE at both week 4 and week 12 (both P < 0.001). The intervention group also showed bigger improvements from baseline in the MMT grades for shoulder adduction and elbow flexion at weeks 4 and 12 as well as thumb adduction at week 4 (P = 0.007, P = 0.049, P = 0.019, P = 0.008, P = 0.029, respectively). The intervention group showed a better change in the MBI at both week 4 and week 12 (P = 0.004 and P = 0.010, respectively). Although the intervention group had a higher BRS for the hand at week 12 (P = 0.041), no intergroup differences were observed at week 4 (all P > 0.05). The two groups showed no differences in MAS grades as well as in BRS for the arm at weeks 4 and 12 (all P > 0.05).

CONCLUSION

Semi-personalized acupuncture prescription based on 3DKA results significantly improved motor function, muscle strength, and activities of daily living in patients with PSSP-UL.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2200056216. Please cite this article as: Huang XY, Liao OP, Jiang SY, Tao JM, Li Y, Lu XY, Li YY, Wang C, Li J, Ma XP. Three-dimensional kinematic analysis can improve the efficacy of acupoint selection for post-stroke patients with upper limb spastic paresis: A randomized controlled trial. J Integr Med. 2025; 23(1): 15-24.

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

Kinematic analysis in post-stroke patients with moderate to severe upper limb paresis and non-disabled controls

BACKGROUND

Kinematic analysis has been recommended to quantify the upper limb motor function after stroke. However, previous studies have rarely reported the kinematic data of the post-stroke patients with moderate to severe upper limb paresis due to the poor accomplishment of the complex tasks.

METHODS

27 post-stroke individuals and 20 non-disabled people participated in the study. The trunk and upper limb movements during the Hand-to-mouth task were captured by the motion capture system and upper extremity kinematic analysis software automatically. The subgroup analysis within stroke group were conducted layering by the Fugl-Meyer Assessment for Upper Extremity scores (severe: 16-31; moderate: 32-50).

FINDINGS

The paretic upper limbs in the stroke group tended to use more trunk and shoulder compensatory strategies to offset the impact of spasticity and weakness compared with non-disabled controls. The less-affected limbs in the stroke group also showed abnormal kinematic data. There were significant differences between the kinematic metrics of severe and moderate subgroups.

INTERPRETATION

The Hand-to-mouth task is a good and feasible option for kinematic analysis of these patients. It is essential to layer the severity of the paresis and put more emphasis on trunk movements in the future kinematic studies.

Digital therapeutics-based lumbar core exercise for patients with low back pain: A prospective exploratory pilot study

Objective

This study aimed to implement a digital therapeutics-based approach based on motion detection technology and analyze the clinical results for patients with chronic low back pain (LBP).

Methods

A prospective, single-arm clinical trial was conducted with 22 patients who performed mobile app-based sitting core twist exercise for 12 weeks. Clinical outcomes were assessed using the visual analog scale (VAS) for LBP, Oswestry Disability Index-Korean version (K-ODI), and EuroQol-5 dimension 5-level version (EQ-5D-5L) every 4 weeks after the initiation of treatment. Laboratory tests for factors associated with muscle metabolism, plain X-ray for evaluating sagittal balance, and magnetic resonance imaging for calculating cross-sectional area (CSA) of back muscles were performed at pretreatment and 12 weeks post-treatment.

Results

The study population included 20 female patients with an average age of 45.77 ± 15.45 years. The clinical outcomes gradually improved throughout the study period in the VAS for LBP (from 6.05 ± 2.27 to 2.86 ± 1.86), K-ODI (from 16.18 ± 6.19 to 8.64 ± 5.58), and EQ-5D-5L (from 11.09 ± 3.24 to 7.23 ± 3.89) (p < 0.001, respectively). The laboratory test results did not show significant changes. Pelvic incidence (from 53.99 ± 9.70° to 50.80 ± 9.20°, p = 0.002) and the mismatch between pelvic incidence and lumbar lordosis (from 8.97± .67° to 5.28 ± 8.57°, p = 0.027) decreased significantly. Additionally, CSA of erector spinae and total back muscles increased by 5.20% (p < 0.001) and 3.08% (p = 0.013), respectively.

Conclusions

The results of this study suggest that the efficacy of digital therapy-based lumbar core exercise for LBP is favorable. However, further large-scale randomized controlled studies are necessary.

Azure Kinect performance evaluation for human motion and upper limb biomechanical analysis

Human motion tracking is a valuable task for many medical applications. Marker-based optoelectronic systems are considered the gold standard in human motion tracking. However, their use is not always feasible in clinics and industrial environments. On the other hand, marker-less sensors became valuable tools, as they are inexpensive, noninvasive and easy to use. However, their accuracy can depend on many factors including sensor positioning, light conditions and body occlusions. In this study, following previous works on the feasibility of marker-less systems for human motion monitoring, we investigate the performance of the Microsoft Azure Kinect sensor in computing kinematic and dynamic measurements of static postures and dynamic movements. According to our knowledge, it is the first time that this sensor is compared with a Vicon marker-based system to assess the best camera positioning while observing the upper body part movements of people performing several tasks. Twenty-five healthy volunteers were monitored to evaluate the effects of the several testing conditions, including the Azure Kinect positions, the light conditions, and lower limbs occlusions, on the tracking accuracy of kinematic, dynamic, and motor control parameters. From the statistical analysis of the performed measurements, the camera in the frontal position was the most reliable, the lighting conditions had almost no effects on the tracking accuracy, while the lower limbs occlusion worsened the accuracy of the upper limbs. The assessment of human static postures and dynamic movements based on experimental data proves the feasibility of applying the Azure Kinect to the biomechanical monitoring of human motion in several fields.

Dynamic valgus knee revealed with single leg jump tests in soccer players

BACKGROUND

Dynamic valgus knee occurs in sports that involve jumps and landing such as soccer and pose an increased risk for anterior cruciate ligament injury. Visual estimation is biased by the athlete's body type, the experience of the evaluator and the movement phase at which the valgus is assessed - thus the result is highly variable. The aim of our study was to accurately assess dynamic knee positions during single and double leg tests through a video-based movement analysis system.

METHODS

Young soccer players (U15, N.=22) performed single leg squat, single leg jump, and double leg jump tests while the knee medio-lateral movement was monitored with a Kinect Azure camera. Jumping and landing phases of the movement were determined within the continuous recording of the knee medio-lateral position over the ankle and the hip vertical position. Kinect measurements were validated by Optojump (Microgate, Bolzano, Italy).

RESULTS

Soccer players retained their predominantly varus knee positions in all phases of double-leg jumps, which was far less prominent in single leg tests. Interestingly, a marked dynamic valgus was observed in athletes who participated in traditional strengthening exercises, while this valgus shift was mostly prevented in those who participated in antivalgus training regimes. All these differences were only revealed during single leg tests, while the double leg jump tests masked all valgus tendencies.

CONCLUSIONS

We propose to use single-leg tests and movement analysis systems for evaluating dynamic valgus knee in athletes. These methods can reveal valgus tendencies even in soccer players who have a characteristic varus knee while standing.

How the Processing Mode Influences Azure Kinect Body Tracking Results

The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.

Effectiveness and usability of the system for assessment and intervention of frailty for community-dwelling pre-frail older adults: A pilot study

Background

Effective multicomponent interventions in the community targeted at preventing frailty in at-risk older adults can promote healthy ageing. However, there is a lack of studies exploring the effectiveness of technology-enabled autonomous multi-domain community-based interventions for frailty. We developed a novel end-to-end System for Assessment and Intervention of Frailty (SAIF) with exercise, nutrition, and polypharmacy components. This pilot study aimed to explore SAIF's effectiveness in improving frailty status, physical performance and strength, and its usability in pre-frail older adults.

Materials and methods

This is a single arm 8-week pilot study in 20 community-dwelling older adults who were pre-frail, defined using the Clinical Frailty Scale (CFS) as CFS 3 + (CFS 3 and FRAIL positive) or CFS 4. For outcomes, we assessed frailty status using the modified Fried Frailty Phenotype (FFP) and CFS; physical performance using Short Physical Performance Battery (SPPB); and Hand Grip Strength (HGS) at baseline and 8-week. User experience was explored using the System Usability Scale (SUS), interest-enjoyment subscale of the Intrinsic Motivation Inventory and open-ended questions. We analyzed effectiveness using repeated-measures tests on pre-post scores, and usability using a convergent mixed-method approach via thematic analysis of open-ended responses and descriptive statistics of usability/interest-enjoyment scales.

Results

Sixteen participants (71.8 ± 5.5 years) completed the 8-week study. There was a significant improvement in FFP score (-0.5, p < 0.05, effect size, r = 0.43), but not CFS (-1.0, p = 0.10, r = 0.29). Five (31.3%) improved in frailty status for both FFP and CFS. SPPB (+1.0, p < 0.05, r = 0.42) and HGS (+3.5, p < 0.05, r = 0.45) showed significant improvements. Three themes were identified: "Difficulty in module navigation" (barriers for SAIF interaction); "User engagement by gamification" (facilitators that encourage participation); and "Perceived benefits to physical health" (subjective improvements in physical well-being), which corroborated with SUS (68/100) and interest-enjoyment (3.9/5.0) scores. Taken together, user experience results cohere with the Senior Technology Acceptance and Adoption Model.

Conclusion

Our pilot study provides preliminary evidence of the effectiveness of SAIF in improving frailty status, physical performance and strength of pre-frail older adults, and offers user experience insights to plan the follow-up large-scale randomized controlled trial.

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.

Deep Learning Networks for View-independent Knee and Elbow Joint Angle Estimation

Vision-based human joint angle estimation is essential for remote and continuous health monitoring. Most vision-based angle estimation methods use the locations of human joints extracted using optical motion cameras, depth cameras, or human pose estimation models. This study aimed to propose a reliable and straightforward approach with deep learning networks for knee and elbow flexion/extension angle estimation from the RGB video. Fifteen healthy participants performed four daily activities in this study. The experiments were conducted with four different deep learning networks, and the networks took nine subsequent frames as input while output was knee and elbow joint angles extracted from an optical motion capture system for each frame. The BiLSTM network-based joint angles estimator can estimate both joint angles with a correlation of 0.955 for knee and 0.917 for elbow joints regardless of the camera view angles.

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

Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it 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 analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. 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. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. 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 tracking limitations of the Kinect.

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.