Robust skeletal motion tracking using temporal and spatial synchronization of two video streams

Abstract

Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and impractical for home rehabilitation, whereas marker-less methods often suffer from depth estimation errors and occlusions. Recent studies have explored various computer vision and deep learning approaches for human pose estimation, yet challenges remain in ensuring robust depth accuracy and tracking under occlusion conditions. This study proposes a three-dimensional human skeleton tracking system for upper limb activities that integrates temporal and spatial synchronization to improve depth estimation accuracy for rehabilitation exercises. The proposed system combines a 90° secondary camera to compensate for the depth prediction inaccuracies inherent in single-camera systems, reducing error margins by up to 0.4 m. In addition, a linear regression-based depth error correction model is implemented to refine depth coordinates, further improving tracking precision. The Kalman filtering framework is employed to enhance temporal consistency, allowing real-time interpolation of missing joint positions. Experimental results demonstrate that the proposed method significantly reduces depth estimation errors of the elbow and wrist joint (p < 0.001) compared to single camera setups, particularly in scenarios involving occlusions and non-frontal perspectives. This study provides a cost-effective and scalable solution for remote patient monitoring and motor function evaluation.

Introduction

Human pose estimation is emerging in rehabilitation patient monitoring, using advances in artificial intelligence and computer vision to improve patient care. The tracking of human movement kinematics offers significant potential to improve motor assessment and telerehabilitation for stroke patients [1]. Various methodologies have been explored, including using YOLOv4-tiny for patient detection and Mediapipe for pose estimation, achieving high accuracy in classifying patient poses such as sleeping, sitting, walking and standing [2]. Despite the challenges posed by complex environmental changes and diverse body shapes, advanced techniques such as coarse-to-fine heatmap shrinking and spatial-temporal perception networks have shown promise in enhancing the accuracy of 2D and 3D pose estimations in clinical settings [3,4]. Virtual rehabilitation systems, such as those based on PoseNet, enable patients to perform rehabilitation exercises at home, with real-time tracking of joint movements to assess recovery progress [5]. The integration of low-cost and high-end sensor data, as demonstrated by datasets containing extensive motion sequences, further supports the development of robust inertial pose estimation algorithms for rehabilitation applications [6]. Furthermore, the fusion of binocular stereo vision and convolutional neural networks has been explored to improve the accuracy of 3D pose estimation, potentially reducing costs and limitations associated with traditional motion capture systems [7], also capable of converting limited angle sensor data to a full 3D image of the pose tracked [8]. Smart walkers with RGB+D cameras and neural network frameworks offer a comprehensive approach to estimating full-body poses, facilitating real-time monitoring and human-robot interaction in rehabilitation settings [9]. The combination of inertial measurement units with computer vision techniques has been identified as a promising direction for future research, with the aim of improving the effectiveness of rehabilitation therapy [10]. In addition, Virtual Reality approaches and sensors-based techniques can lead to more precise prose tracking, further improving the precision of therapy [11].

Over the past decade, deep learning has significantly advanced in detecting and tracking human body parts from images or videos to build a representation of the human body, often based on convolutional neural networks (CNNs) and other architectures to improve accuracy and efficiency. Various datasets, such as the MPII Human Pose dataset, have been collected to train and evaluate these models, with notable implementations, including ResNet50 and VGG16, which achieved 67% and 88.8% accuracy, respectively [12]. Despite these advances, challenges such as insufficient training data, depth ambiguities, and occlusions persist, which requires ongoing research and development [13]. Applications of pose estimation span numerous domains, including human-computer interaction, motion analysis, augmented reality, and virtual reality [14]. Methods for estimating poses typically involve bottom-up or top-down approaches, CNN being a common choice for detecting key points, while movement assessment methods vary widely [15]. Approaches, such as the Ultra Wide-Band (UWB) technology with body-mounted sensors, have also been explored to overcome the limitations of vision-based systems, particularly in complex environments [16]. Furthermore, models such as BlazePose GHUM 3D and HRNet have been developed to enhance the accuracy of 2D and 3D pose estimation, addressing problems related to image resolution and multiscale problems [3,17]. Despite progress, achieving precision of 100% remains elusive due to factors such as environmental changes and various body shapes [18]. The field continues to evolve with ongoing research that focuses on improving model robustness, dataset diversity, and real-time application capabilities [19,20]. Future directions include refining feedback mechanisms and addressing erroneous feedback to enhance user interaction and application effectiveness.

Movement analysis allows for the early detection of deviations from standard movement patterns and provides accurate and objective data on correct limb movements, posture, body balance, and coordination. Objective qualitative and quantitative insights allow the correct treatment plans to be tailored to the specific needs of each subject for objectified movement assessment [21]. However, those that exist are time-consuming and unfriendly to the researcher and the subject. Table 1 summarizes recent research models in human pose estimation on a Human3.6M dataset.

[Figure omitted. See PDF.]

Goal of the work

The research presented in this work is a continuation of our previous study on enhanced human skeleton tracking for rehabilitation exercises [22]. In the previous work, we used a dual-camera setup to mitigate depth prediction errors by combining two synchronized video streams. In this extended study, we further refine the system by integrating Kalman filtering for depth error correction and spatio-temporal synchronization, which enhances pose estimation accuracy across diverse movement patterns. Furthermore, we expanded the evaluation to include benchmark comparisons with state-of-the-art 3D pose estimation models on Human3.6M and private datasets.

The goal of this work is to develop a timeline and a spatially synchronized two-camera system to detect and predict the depth of specific joints, when volunteers perform various exercises for rehabilitation purposes [33] and to evaluate the accuracy of depth coordinate predictions, with concurrent movement tracking achieved through a specialized optical system. In addition, our objective was to identify exercises during which specific joints were difficult to detect and exercises in which the corresponding joints exhibited the highest depth prediction errors.

Building on our single-camera baseline [8], we attach an orthogonal side camera to address the dominant source of error, depth ambiguity. The two RGB streams are brought into the spatiotemporal register with an analytic three-step synchronization; a lightweight linear regression corrector then removes the residual per joint depth bias. Recent multiview works [34–36] focus on heavier networks for pose refinement but leave depth bias largely untreated, whereas our explicit corrective model targets that error directly. Robustness is further strengthened by a Kalman smoother that bridges occlusions when either camera momentarily loses sight of a limb. Furthermore, we repurpose our earlier Pareto optimization scheme to select an operating point on the latency-versus-alignment front, ensuring real-time performance without retraining. In conclusion, we provide a detailed evaluation dedicated to upper limb rehabilitation, where seven shoulder-elbow-wrist movements are compared under occlusion and clothing variation.

Materials and methods

Implementation

Depth error is the biggest problem in predicting the 3D human skeleton from a single image stream. We propose including an additional video stream perpendicular to the first video stream. When filming a person from the side, an additional video stream will provide additional depth information, with the help of which it will be possible to adjust the coordinates of the predicted three-dimensional human skeleton obtained from the frontal video stream; however, it becomes mandatory to synchronize the video streams. Ideally, both video streams should be shot with the same cameras, ensuring the same frame quality, image resolution, and frame rate.

The diagram of the proposed solution algorithm is presented in Fig 1. The algorithm starts with two parallel video streams: one capturing a person from the front and the other from the side. The coordinates of the second skeleton are rotated around the Y-axis to align the viewing positions. Depth error dependence between the skeletons is calculated and missing joint points are inserted from one stream to another, with depth adjustments based on the calculated errors. The final 3D skeleton is formed using the X and Y coordinates from the first stream and the Z coordinates from the second.

[Figure omitted. See PDF.]

Video Stream Recording is defined as follows:

* Let represent the video stream recording a person from the front over time t.

* Let represent the video stream recording a person from the side over time t.

In the 3D human skeleton prediction stage of the proposed algorithm, the MediaPipe Pose system, improved on the work of [37], was selected as an open source backbone due to its real-time processing capability, and was further modified to provide sufficient accuracy and the provision of a higher number of 3D human skeleton coordinates compared to previous solutions [34].

The prediction of depth coordinates from a single image often exhibits errors that correlate with the vertical position of the joints. To address this, we implemented a linear regression model to predict and correct for these errors. Within the "Depth error dependence calculation block", we determined the coefficients k and b for the linear relationship:

(1)

where y represents the vertical coordinate of the human joints and z is the predicted depth coordinate. The calculation of the depth prediction error at a given height y is defined as follows:

(2)

The algorithm aims to minimize prediction error by optimizing coefficients k and b, thus enhancing the overall accuracy and efficiency of the 3D prediction of the human skeleton. Our method ensures that the linear regression model accurately reflects the relationship between the vertical and depth coordinates, facilitating more precise depth predictions. We used this depth coordinate prediction for only visible joints from one camera.

Using the above principle behind the pose estimation algorithms, we predict the 3D human skeleton:

(3)(4)

We perform a rotation on the Y axis to align the side view skeleton S_s with the front view skeleton S_f.

(5)

where is the rotation angle.

Depth error E_d is calculated as the difference between the depths of the corresponding joints in S_f and .

(6)

where n is the number of joints and z denotes the depth coordinate. J_f(i) and represent the joint points in the skeletons S_f and respectively. If a joint point J_f(i) is missing, it is inserted from and vice versa. The insertion process is enhanced using Kalman filtering and depth error correction.

We adapt the state vector for the Kalman filter as suggested by [38] as:

(7)

where are the coordinates of the joint point and are the velocities. The Kalman filter is applied under the assumption that joint trajectories exhibit approximately linear motion over each frame, making a first-order linear model suitable for state prediction. Furthermore, previous studies on pose estimation suggest that measurement noise follows a Gaussian distribution [39].

The prediction step is then defined as:

(8)

where is the state transition matrix and is the control input matrix. We assume the zero control input (, ) and a constant velocity for the state transition matrix , where the position of each joint is updated as a function of its previous position and estimated velocity:

(9)

The update step is defined as:

(10)(11)(12)

where is the Kalman gain, is the predicted error covariance, is the observation matrix, and is the observation noise covariance. The observation model is formulated as a linear mapping between the internal state of the system and the measured joint positions. In this case, an identity transformation is used. This simplifies the measurement update step. was obtained by recoding a static subject for 30 s (no motion), and computing sampled variances of the detected joints:

(13)

In our filter, this fully specifies the measurement update weighting. A small process-noise term was included for numerical stability.

The filter was initialized with

(14)

The sampling interval was set to

(15)

matching the 30 Hz frame rate.

When a missing point is inserted, the depth value z is adjusted based on depth error E_d. We then define the adjusted depth z_adjusted as:

(16)

where is a scaling factor.

Using the Kalman filter and depth error correction, the missing joint point J_missing is interpolated as:

(17)

The final formation of the Final Three-Dimensional Human Skeleton is done by combining S_f, , and J_missing to form the final 3D skeleton S_final:

(18)

The two camera streams and for the skeletons S_f and S_s must be spatially synchronized. To align the coordinate systems of the front view S_f and the side view S_s, a translation vector is introduced. This vector compensates for the different spatial positions of the cameras:

(19)

where is the spatially adjusted side view skeleton and is the translation vector defined as:

(20)

where T_x, T_y, and T_z are the translation offsets in the x, y, and z axes, respectively.

Following translation, the side view skeleton is rotated to align with the front view skeleton S_f, similar to the previous rotation step:

(21)

where is the skeleton after both translation and rotation adjustments.

After applying translation and rotation, a spatial synchronization error E_spatial is calculated to quantify the misalignment between the adjusted side view skeleton and the front view skeleton S_f:

(22)

where and represent the joint points in the skeletons S_f and , respectively, and denotes the Euclidean distance.

To minimize the spatial synchronization error E_spatial, an optimization process is carried out to find the optimal translation vector and rotation angle , so the side view skeleton is spatially aligned as closely as possible with the front view skeleton.:

(23)

With the optimal translation and rotation applied, the side view skeleton is used instead of in the existing pipeline. Finally, the steps involving depth error calculation, Kalman filtering, and joint point interpolation are then carried out using the spatially synchronized skeletons:

(24)(25)

The final 3D skeleton S_final is formed by combining S_f, , and J_missing:

(26)

A synchronization of the timeframe of the two camera streams and is done using a Pareto optimization-based timeframe synchronization method, proposed to improve the accuracy of the final 3D skeleton S_final:

The timeframe adjustment variables are defined as and as timeframe adjustments for and , respectively, representing small shifts in time to synchronize the frames between the two camera streams.

The objective function aims to minimize the synchronization error, which is the difference between the depth coordinates of the corresponding joints in the skeletons S_f and at the adjusted timeframes:

(27)

Computational Cost Minimization is defined as:

(28)

where, and represent the computational cost associated with adjusting the timeframes and for the front and side views, respectively.

Implemented Pareto optimization algorithm flowchat is shown in Fig 2. The Pareto optimization, based on [8] is applied to find the optimal set of timeframe adjustments that simultaneously minimize the synchronization error E_sync and the computational cost C. The Pareto front represents the set of non-dominated solutions where improving one objective (reducing E_sync) results in the deterioration of the other objective (increasing C). From the Pareto front, the final selection of and is made based on the desired trade-off between the synchronization accuracy and the computational cost. Once the optimal timeframe adjustments and are determined, the adjusted streams and are used in the existing pose estimation and depth error correction pipeline:

[Figure omitted. See PDF.]

(29)

The side view skeleton S_s is rotated and aligned with the front view skeleton S_f, and the depth error E_d is calculated as before. As described above, missing joint points are interpolated using Kalman filtering and depth error correction. The final 3D skeleton S_final is formed by combining the synchronized skeletons S_f, , and J_missing.

Dataset

Two datasets were collected in previous research by the authors [8]. Participants were recruited between 2023-06-09 and 2023-10-31. The study was carried out according to the Declaration of Helsinki and was approved by the Institutional Review Board of Vilnius Regional Biomedical Research Ethics Committee (no 2023/6-1439-978). Informed written consent was obtained from the participants. The datasets were collected using Gemini 2 cameras, which features a resolution of 19201080 at 30 frames per second for its RGB sensor. For processing and 3D human skeleton computation, the system relied on a high-performance PC equipped with an Intel i9-9900k processor, an NVIDIA RTX 4090 GPU with 24 GB of VRAM, and 64 GB of DDR4 RAM. The approach presented in this paper was developed using Python 3.11.2, and libraries NumPy 1.25.0, SciPy 1.11.1, and PyTorch 2.0.1.

Both cameras were mounted on tripods at approximately the same height ( cm) and oriented to form a nominal angle. We approximate each camera as an ideal pinhole (principal point in the center of the image, no distortion coefficients) using the manufacturer’s horizontal field of view to infer the focal length in pixels.

The first data set consists of nine pairs of videos of each person doing exercises dressed in black to have a baseline on camera limitations, grouped according to the exercise performed and the trial number, as presented in Table 2. Resolution of of the collected videos were .

[Figure omitted. See PDF.]

Upper arm adduction while standing is performed from the initial position, when the volunteer is standing straight, hands down with palms facing the hips, and feet placed hip-width apart. The first exercise is performed by raising the right arm from the side until it is raised above the head. Then return to the starting position – the hand is lowered to the side, the palm facing the hips.

Standing flexion of the upper arm exercise is performed from the starting position, when the volunteer is standing straight, hands down with palms facing the hips, and feet placed hip-width apart. During the second exercise, the right hand is raised in front until it is raised above the head. The exercise is finished by lowering the arm and returning to the starting position.

Standing brachial adduction exercise is performed from the starting position, when the volunteer is standing straight, hands down with palms facing the hips, and feet placed hip-width apart. In the third exercise, the right hand is raised in front to the shoulder level and bent over the shoulder to the center of the face, the elbow always being extended. The exercise is completed by extending the arm back to shoulder level and lowering it to the side.

The second data set consists of eight pairs of videos per person. The recordings repeat the same exercises as in the first data set and additionally perform an exercise in which both arms are raised and lowered at the same time. The exercises are grouped according to the different outfits and the exercises performed and are presented in Table 3. Resolution of of the collected videos were .

[Figure omitted. See PDF.]

Results

Results using the ‘First’ dataset

The illustration in Fig 3 presents the predicted three-dimensional human skeleton during an arm flexion exercise. The first two rows display five essential frames from the front and side video streams, respectively. Rows 3 and 4 present the reconstructed 3D skeleton: joints from the front camera appear as red filled circles (solid dark lines), whereas joints from the side camera are shown as hollow circles (light grey lines).

[Figure omitted. See PDF.]

The illustration shows these results from three angles: side, front, and diagonal. Initially, the skeleton projected from the frontal video stream exhibits Z-axis estimation errors, evident as the upper body tilts forward and the lower body tilting backward. Additionally, the skeleton projected from the side stream in the second row lacks discernible joints on the left side of the human body. While our final proposed solution successfully straightens the 3D human skeleton and corrects right-hand movements, a depth error persists in the left hand.

Fig 4 illustrates the depth trajectory of the right elbow and wrist joints during an exercise of flexion of the upper arm. We selected these joints because they exhibit the most significant depth changes and are central to the exercise. The data highlight the impact of depth prediction errors in single-camera tracking and how our dual-camera approach with depth correction refines these estimates.

[Figure omitted. See PDF.]

a – right elbow joint; b – right wrist joint.

During the initial synchronization phase (frames 40–90), both video streams are aligned. From frame 100 onward, as the subject begins raising the arm, depth errors become more pronounced, especially at peak flexion. The raw depth estimation from the frontal camera shows a systematic bias, resulting in inconsistent joint positioning and a noticeable forward tilt of the upper body. However, after applying depth correction and Kalman filtering, the refined depth trajectory is smoother and more anatomically accurate, reducing fluctuations and aligning precisely with expected joint movements.

Experimental results of the second part of the data set

The most significant predicted depth error occurs during the adduction and subsequent reextension of the forward-extended upper arm. Although the depth trajectories of the right elbow and wrist joints maintain similar patterns, their absolute depth values frequently diverge. Importantly, the refined joint depth trajectories are smoother and exhibit a reduced systematic bias, shifted closer to the center of the Y-axis.

The means and standard deviations of the predicted joint depth error for the first data set are shown in Table 4. The mean and standard deviation results are given in meters. The standard deviation was calculated as follows:

[Figure omitted. See PDF.]

(30)

where N is the number of video frames; x1, x2 – the absolute error of the three-dimensional human skeleton joint depth in the frame between the flow predicted from the front view and the final solution; μ is the mean of the absolute value of difference of the 3D human skeleton joint depth error over the entire video.

With additional data from the second video stream, the data from the side camera successfully managed to help to adjust the depth coordinates of the human skeleton. Different tests of the same exercises showed that the final results are predicted almost identically and the time variation of the depth coordinates coincides, in contrast to the data from only one video stream.

Results using the ’Second’ dataset

Fig 5 illustrates the predicted three-dimensional human skeleton during an arm flexion exercise. The first two rows display five essential frames from the front and side video streams, respectively. The third and fourth rows present the corresponding 3D skeleton reconstructions: red filled circles represent the front stream projection, aligned empty circles show the side-stream projection, and pink coordinates denote the final output of our proposed solution.

[Figure omitted. See PDF.]

These 3D results are displayed from three angles: side, front, and diagonal. The skeleton projected from the frontal video stream exhibits depth errors, specifically an upper body tilt forward and a lower body tilt backward. Additionally, the skeleton projected from the side stream lacks discernible joints on the left side of the human body. While our final solution successfully straightens the 3D human skeleton and corrects right-hand movements, a depth error persists in the left hand.

Fig 6 presents the variation in depth coordinates for the right elbow and wrist joints during an arm flexion exercise, under different clothing conditions (light vs. dark). These joints, central to right-handed exercise, were selected for their significant depth variations. The results indicate that, while the dual-camera system effectively corrects depth errors, clothing differences introduce minor variations in joint detection accuracy.

[Figure omitted. See PDF.]

a – right elbow joint; b – right wrist joint.

The initial 70 frames represent system synchronization, ensuring view alignment before the exercise begins. As the arm is raised, depth errors increase, particularly with dark clothing, likely due to reduced contrast and feature detection challenges. Despite this, the final corrected depth trajectory remains stable, with its smoothness and consistency demonstrating the algorithm’s robustness in mitigating depth estimation errors and maintaining tracking accuracy across varying visual conditions.

The largest predicted depth error occurs when both hands are raised when a person is dressed in dark clothing. The trajectory of the change in the depth of the right and left elbow and wrist joints remains similar, only the distance differs. The motion trajectory of the final corrected 3D human skeleton remains similar to the test of this exercise in light clothing.

The means and standard deviations of the predicted joint depth error for the second data set are shown in Table 5 and are given in meters.

[Figure omitted. See PDF.]

As with the experiments on the first dataset, additional data from the second image stream successfully corrected the depth coordinates of the 3D human skeleton. Different clothing did not have a significant effect on the change in joint depth trajectories over time, and similar results were obtained compared to the first data set. Both visual 3D space representations and depth comparison plots show that a second image stream perpendicular to the first image stream from the front has a positive effect on the final 3D skeleton prediction. Experiments performed on selected datasets improved depth prediction by an average of 0.1 meters.

Direct performance comparison

Table 6 presents a comparison of our proposed method with state-of-the-art 3D pose estimation models, evaluated on the Human3.6M dataset. Although recent approaches such as SlowFastFormer achieve the lowest MPJPE, our method provides a competitive performance (41.2 mm) while introducing a dual-camera setup with Kalman filtering and depth error correction.

[Figure omitted. See PDF.]

Table 7 summarizes the mean and standard deviation of the depth error for the right elbow and wrist in two upper limb exercise datasets (Tables 4 and 5), comparing our proposed model with three state-of-the-art baselines (PoseFormer, HybrIK, and SlowFastFormer), all of which were benchmarked using only a single frontal video stream. In particular, our proposed model consistently achieves low mean errors (e.g., m elbow error in the first dataset) while maintaining competitive or better results relative to the benchmark methods in both datasets.

[Figure omitted. See PDF.]

Performance analysis and ablation study

The real-time applicability of the proposed approach was evaluated using the same system setup, which was used to collect the data sets. The average computation time of full pipeline was 18.4 ms on desktop PC. Table 8 summarizes an ablation and performance study in which the main algorithmic blocks were replaced by a lightweight alternative. When the Kalman filter was turned off and the missing joints were filled with last held value; this reduces the latency from 18.4 ms to 15.5 ms, but increased the mean elbow error from 7.5 mm to 8.9 mm and the wrist error from 11.3 mm to 13.1 mm. Substituting the linear regression model with raw depths yielded a reduced inference time (17.1 ms) and a small loss of accuracy. Replacement of spatial and temporal synchronization with a nearest-neighbor timestamp match delivered the fastest inference (13.1 ms). However, the estimation error increased to 11.4 mm for the elbow and 15.6 mm for the wrist.

[Figure omitted. See PDF.]

Statistical analysis

To evaluate the effectiveness of our dual-camera approach in reducing depth estimation errors, we conducted a paired t-test comparing depth errors under different conditions:

* Single-camera vs. dual-camera setup;

* Unobstructed vs. partially obstructed joints;

* Frontal vs. non-frontal views.

Table 9 summarizes the mean depth errors for each condition, along with the corresponding p-values and results of statistical significance.

[Figure omitted. See PDF.]

The results show a significant reduction in depth estimation errors when using the dual-camera setup compared to a single-camera approach (p < 0.01). The mean depth error decreased from 0.110 m (single-camera) to 0.078 m (dual-camera), demonstrating the advantage of leveraging a secondary camera for improved depth prediction.

Similarly, occlusion conditions had a significant effect on tracking accuracy (p < 0.01). The mean depth error increased from 0.090 m (unobstructed) to 0.129 m (partially obstructed joints), indicating that occlusions introduce noticeable tracking errors. However, even under occluded conditions, the dual-camera approach helped reduce the impact of missing joint information. The upper limb occlusion conditions were simulated using a dual-camera setup, where the frontal camera captured the full body view of the subject, while the side camera, positioned at 90^°, provided depth information. The joints became partially obstructed due to self-occlusion (e.g., arms crossing the torso) or camera perspective limitations, affecting visibility in one of the views. The system mitigated these occlusions by synchronizing both views and interpolating missing joint positions.

Lastly, the results confirm that the camera perspective plays a key role in depth accuracy. Depth errors were significantly lower when the view was frontal (0.098 m) compared to non-frontal perspectives (0.118 m, p < 0.01). This suggests that while our spatial synchronization method mitigates some perspective-related errors, certain joint positions may still be more accurately estimated from a frontal viewpoint.

These findings highlight the robustness of our method in reducing depth errors and maintaining tracking accuracy under different conditions, supporting its application in rehabilitation and motion analysis scenarios.

Discussion

Markerless motion tracking is a highly promising and in-demand field due to easy progress monitoring and real-time feedback. Continuous monitoring allows monitoring patient progress in real-time, adjusting new protocols as needed. Visual feedback from skeletal tracking systems can motivate patients to remain engaged in their programs and enable remote monitoring and rehabilitation, allowing subjects to perform exercises at home. An essential problem with most existing markerless human posture estimation systems and their application in rehabilitation facilities is their level of accuracy and reliability [21], finding that many factors influence the results, beginning with deviation parameters (joint coordinates, joint angles, joint depth error) and ending with varying levels of accuracy, making it challenging to compare results unambiguously with those obtained by other researchers. However, we also achieved a result similar to other approaches (see Table 6).

Table 7 shows that our proposed model outperforms the latest approaches in terms of mean joint depth error, particularly for the elbow and wrist joints. However, such significant improvement is achieved only in our collected dataset. The key factor contributing to this improvement is our dual-camera setup with temporal and spatial synchronization, which significantly reduces depth ambiguity and errors related to upper limb occlusion, as confirmed in previous research on multiview skeletal tracking [22]. The Kalman-filtering-based depth correction further refines the predictions, mitigating inconsistencies in the estimation of single-camera poses. Interestingly, in the Human3.6M dataset, our model achieves results comparable to other methods (MPJPE: 41.2 mm), despite its enhanced performance in our dataset. This similarity can be attributed to the fact that Human3.6M primarily consists of single-view recordings, limiting the advantages of our multi-camera integration.

Our current benchmarking was performed using a single frontal camera stream. While our proposed model demonstrates strong performance in this context, it is important to acknowledge that the benchmarked baselines, like PoseFormer and HybrIK, could also potentially achieve improved 3D pose estimation by incorporating a multi-camera setup.

In this study, we used linear regression to relate the vertical joint position to the depth error because of its simplicity and real-time efficiency. Despite promising results, human joint trajectories, even for simple rehabilitation exercises, are inherently non-linear [40]. Muscle actuation, joint limits, and multi-segment kinematics produce velocity and acceleration profiles that vary smoothly but not linearly over time. For future work, and given the need for real-time performance in rehabilitation, simple non-linear methods such as Gaussian process regression [41] or kernel ridge regression [42] could be investigated. Alternatively, small multilayer perceptrons or temporal convolutional networks can learn non-linear mappings directly from data [43].

A key advantage of our timeframe-synchronisation module is that it is tuned with Pareto optimisation, which treats alignment accuracy (E_sync) and computational cost (C) as competing objectives rather than forcing a single weighted sum. As a result, the optimizer returns a front of non-dominated solutions that expose the inherent trade-off: every millisecond shaved from processing latency pushes synchronization error up, and vice versa. This explicit trade-off view also guards against hidden overfitting: if future deployments involve slower hardware or denser video streams, the same Pareto front can be recomputed, and a new operating point selected without retraining the full model. Achieved 18 ms latency at 0.078 m mean depth error proved to be the most practical, but the choice of any point ultimately depends on the application or clinical priorities.

Despite promising results, certain factors limit its accuracy. Different subject profiles, such as children, frail elderly people, or orthotic wearers, can distort limb proportions and cause pose errors. Secondly, fluctuating illumination or cluttered backgrounds may suppress key-point confidence and could bias depth estimation. Our configuration assumes the ideal angle between cameras, precise orientation and height consistency (cm), any actual deviation or incomplete perspective distortion may lead to systematic errors. Furthermore, our synchronization method mainly compensates for rotations around the Y-axis and corrects potential out-of-plane rotations with insufficient compensation. Unmodeled image blurs can also affect the accuracy of joint detection and depth estimate. Therefore, future work could consider using explicit calibration methods with established methods (such as Zhang’s method [44] or fiducial marker-based calibration like AprilTags [45]) to mitigate distortion and alignment errors. However, our simplified approach is practical and emphasizes the ease of reproducibility and deployment, especially for clinical or home rehabilitation.

Furthermore, performing intrinsic camera calibration under simplified assumptions can introduce inaccuracies due to inherent lens distortion effects (specifically, radial and tangential distortions) [46]. Thus, inferring focal length from the manufacturer’s stated horizontal field of view and image width contributes to additional error. Small discrepancies between the nominal field of view and the actual in-scene focal length can result in scale errors of up to % [47]. Those scale errors are likely directly propagating into reconstructed 3D joint coordinates, causing consistent underestimation or overestimation of depth across the entire skeleton. Thus, any unknown focal length mismatch remains uncorrected, which may be particularly problematic when subjects perform exercises at varying distances from the cameras.

By integrating pose refinement algorithms such as SmoothNet [48], FLK [49], or HiMoReNet [50], into our proposed dual-camera pipeline, we could offer refined smoothness and accuracy of the skeleton trajectories. Such post-processing modules usually are designed as a ’plug–and–play’ as input takes a sequence of 2D/3D joint coordinates and output smoothed poses by modeling long-range temporal relations per joint. For example, SmoothNet can be applied to each joint channel individually, which is particularly appealing in rehabilitation context, although additional refinements would be needed for real-time applications. On the other hand, FLK combines an adaptive Kalman filter with a biomechanical constraint adjustment and a low-pass filter, and offers a real-time join refinement solution for a desktop PC. HiMoReNet uses a hierarchical architecture that group joints and captures long-range temporal context, and then refines each group’s motion while leveraging global body interactions. Thus, using the HiMoReNet module in our proposed pipeline could enforce biomechanically plausible trajectories, which is crucial, if the full system would be applied for montion quantification or anomaly detection.

Although this study evaluated the system using upper limb rehabilitation movements (e.g., arm adduction, flexion, and brachial motions), the framework is inherently scalable to full-body or lower-limb activities. By adjusting camera placement, calibration parameters, and pose estimation targets, it can be adapted for tracking gait, posture, or multi-joint exercises, enabling broader rehabilitation applications. To increase the clinical relevance of the system, future works should include validation against standardized rehabilitation outcome measures such as the Motor Assessment Scale (MAS) and the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire. These tools are well suited to evaluate upper limb motor function and the practical usability of motion tracking systems in clinical rehabilitation. Pilot studies involving patient cohorts could further assess the integration of this approach into therapeutic protocols and provide feedback on the effectiveness and usability of the system in real-world clinical environments.

Conclusions

Our proposed three-dimensional human skeleton tracking methodology has demonstrated the ability to refine upper limb depth coordinates for greater accuracy by fusing a side view with the frontal stream through automatic spatio-temporal alignment, Kalman smoothing and lightweight depth-error regression. By merging the two image streams, the system reduces depth-coordinate errors in wrist and elbow prediction with automatic spatial and timeframe synchronizations. The fusion of predicted human skeleton models allows building a human skeleton model even when one camera does not see all parts of the human body, and we can refine the depth coordinates using the error prediction from the linear regression model. The prediction of depth coordinates from a single image has errors that exhibit a regular deviation along the vertical axis. However, using an additional rotated video camera directed at the person, we can compensate for the inaccuracies of depth prediction and reduce errors by 0.4 m.

Although the dual-view layout relies solely on commodity RGB cameras and lightweight calibration, providing a cost-effective path to continuous DASH/MAS scoring, remote telerehabilitation, and straightforward extensibility to full-body or gait analysis via simple camera repositioning, reliable claims about its accuracy still require rigorous implementation and benchmarking against existing rehabilitation-oriented systems. In particular, these solutions must be validated in real-world clinical scenarios with established assessment instruments before they can be trusted for routine motion assessment workflows in physiotherapy and rehabilitation.

References

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3. 3. Wang Y. Monocular 2D and 3D human pose estimation review. In: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022). 2023. p. 45. https://doi.org/10.1117/12.2673629

4. 4. Xu W, Xiang D, Wang G, Liao R, Shao M, Li K. Multiview video-based 3-D pose estimation of patients in computer-assisted rehabilitation environment (CAREN). IEEE Trans Hum-Mach Syst. 2022;52(2):196–206.

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8. 8. Maskeliūnas R, Kulikajevas A, Damaševičius R, Griškevičius J, Adomavičienė A. Biomac3d: 2d-to-3d human pose analysis model for tele-rehabilitation based on pareto optimized deep-learning architecture. Applied sciences. 2023 Jan 13;13(2):1116.

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10. 10. Niu Y, She J, Xu C. A survey on IMU-and-vision-based human pose estimation for rehabilitation. In: 2022 41st Chinese Control Conference (CCC). 2022. p. 6410–5.

11. 11. Maskeliunas R, Damasevicius R, Blazauskas T, Canbulut C, AdomavicieneË(tm) A, Griskevicius J. BiomacVR: A virtual reality-based system for precise human posture and motion analysis in rehabilitation exercises using depth sensors. Electronics. 2023;12(2):339.

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24. 24. Li J, Xu C, Chen Z, Bian S, Yang L, Lu C. Hybrik: a hybrid analytical-neural inverse kinematics solution for 3D human pose and shape estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. p. 3383–93.

25. 25. Lin K, Wang L, Liu Z. Mesh graphormer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2021. p. 12939–48.

26. 26. Choi H, Moon G, Lee KM. Pose2mesh: Graph convolutional network for 3D human pose and mesh recovery from a 2D human pose. In: Computer Vision–ECCV 2020 : 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII. Springer; 2020. p. 769–87.

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32. 32. Mehraban S, Adeli V, Taati B. Motionagformer: enhancing 3D human pose estimation with a transformer-gcnformer network. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024. p. 6920–30.

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48. 48. Zeng A, Yang L, Ju X, Li J, Wang J, Xu Q. Smoothnet: A plug-and-play network for refining human poses in videos. In: European Conference on Computer Vision. 2022. p. 625–42.

49. 49. Martini E, Boldo M, Bombieri N. FLK: a filter with learned kinematics for real-time 3D human pose estimation. Signal Process. 2024;224:109598.

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Citation: Abromavičius V, Gisleris E, Daunoravičienė K, Žižienė J, Serackis A, Maskeliūnas R (2025) Robust skeletal motion tracking using temporal and spatial synchronization of two video streams. PLoS One 20(8): e0328969. https://doi.org/10.1371/journal.pone.0328969

About the Authors:

Vytautas Abromavičius

Roles: Data curation, Funding acquisition, Methodology, Resources, Software, Validation, Writing – original draft, Writing – review & editing

E-mail: [email protected]

Affiliations: Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania, Department of Electronic Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania

ORICD: https://orcid.org/0000-0003-1588-6572

Ervinas Gisleris

Roles: Methodology, Software, Visualization

Affiliation: Department of Electronic Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania

ORICD: https://orcid.org/0009-0004-6382-9016

Kristina Daunoravičienė

Roles: Conceptualization, Data curation, Resources, Supervision, Validation

Affiliation: Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania

Jurgita Žižienė

Roles: Data curation, Resources, Writing – original draft

Affiliation: Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania

Artūras Serackis

Roles: Conceptualization, Methodology, Project administration, Supervision

Affiliation: Department of Electronic Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania

Rytis Maskeliūnas

Roles: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation: Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania

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References

1. Cherry-Allen KM, French MA, Stenum J, Xu J, Roemmich RT. Opportunities for improving motor assessment and rehabilitation after stroke by leveraging video-based pose estimation. Am J Phys Med Rehabil. 2023;102(2S Suppl 1):S68–74. pmid:36634334

2. Deshmukh SL, Fernandes F, Kulkarni S, Patil A, Jabade V, Madake J. Patient monitoring system. In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon). 2022. p. 1–6. https://doi.org/10.1109/mysurucon55714.2022.9972563

3. Wang Y. Monocular 2D and 3D human pose estimation review. In: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022). 2023. p. 45. https://doi.org/10.1117/12.2673629

4. Xu W, Xiang D, Wang G, Liao R, Shao M, Li K. Multiview video-based 3-D pose estimation of patients in computer-assisted rehabilitation environment (CAREN). IEEE Trans Hum-Mach Syst. 2022;52(2):196–206.

5. Asokan R, Vijayakumar T. IoT based pose detection of patients in rehabilitation centre by PoseNet estimation control. JIIP. 2022;4(2):61–71.

6. Palermo M, Cerqueira SM, André J, Pereira A, Santos CP. From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data. Sci Data. 2022;9(1):591. pmid:36180479

7. Sheng B, Cheng J, Tao J, Wang Y. Marker-less motion capture technology based on binocular stereo vision and deep learning. In: 2022 28th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). 2022. p. 1–6.

8. Maskeliūnas R, Kulikajevas A, Damaševičius R, Griškevičius J, Adomavičienė A. Biomac3d: 2d-to-3d human pose analysis model for tele-rehabilitation based on pareto optimized deep-learning architecture. Applied sciences. 2023 Jan 13;13(2):1116.

9. Palermo M, Moccia S, Migliorelli L, Frontoni E, Santos CP. Real-time human pose estimation on a smart walker using convolutional neural networks. Expert Syst Appl. 2021;184:115498.

10. Niu Y, She J, Xu C. A survey on IMU-and-vision-based human pose estimation for rehabilitation. In: 2022 41st Chinese Control Conference (CCC). 2022. p. 6410–5.

11. Maskeliunas R, Damasevicius R, Blazauskas T, Canbulut C, AdomavicieneË(tm) A, Griskevicius J. BiomacVR: A virtual reality-based system for precise human posture and motion analysis in rehabilitation exercises using depth sensors. Electronics. 2023;12(2):339.

12. Muradli F, C¸ akar S, Cerezci F, C¸ it G. Estimating human poses using deep learning model. Sakarya Univ J Sci. 2023;27(5):1079–87.

13. Zheng C, Wu W, Chen C, Yang T, Zhu S, Shen J, et al. Deep learning-based human pose estimation: a survey. ACM Comput Surv. 2023;56(1):1–37.

14. Tharatipyakul A, Pongnumkul S. Deep learning-based pose estimation in providing feedback for physical movement: a review. Preprints. 2023.

15. Vyshnivskyi D, Liashenko O, Yeromina N. Human pose estimation system using deep learning algorithms. CNCS. 2023;2(72):75–9.

16. Martinelli G, Santoro L, Nardello M, Brunelli D, Fontanelli D, Conci N. UNPOSED: an ultra-wideband network for pose estimation with deep learning. In: 2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT). 2023. p. 299–304.

17. Shah M, Gandhi K, Pandhi BM, Padhiyar P, Degadwala S. Computer vision & deep learning-based real-time and pre-recorded human pose estimation. In: 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). 2023. p. 313–9.

18. Asperti A, Filippini D. Deep learning for head pose estimation: a survey. SN Comput Sci. 2023;4(4). 1–41.

19. XIA Y, Jiang X, Yan L. Research advanced in human pose estimation based on deep learning. In: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022). 2023. 155.https://doi.org/10.1117/12.2667904

20. Dong F, Yang M, Yang P. Human pose estimation based on HRNet and feature pyramids. HSET. 2023;39:1239–44.

21. Hellsten T, Karlsson J, Shamsuzzaman M, Pulkkis G. The potential of computer vision-based marker-less human motion analysis for rehabilitation. Rehabil Process Outcome. 2021;10:11795727211022330. pmid:34987303

22. Abromavičius V, Gisleris E, Daunoravičienė K, Å1/2ižienė J, Serackis A, Maskeliūnas R. Enhanced human skeleton tracking for improved joint position and depth accuracy in rehabilitation exercises. Appl Sci. 2025;15(2):906.

23. Zheng C, Zhu S, Mendieta M, Yang T, Chen C, Ding Z. 3D human pose estimation with spatial and temporal transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2021. p. 11656–65.

24. Li J, Xu C, Chen Z, Bian S, Yang L, Lu C. Hybrik: a hybrid analytical-neural inverse kinematics solution for 3D human pose and shape estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. p. 3383–93.

25. Lin K, Wang L, Liu Z. Mesh graphormer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2021. p. 12939–48.

26. Choi H, Moon G, Lee KM. Pose2mesh: Graph convolutional network for 3D human pose and mesh recovery from a 2D human pose. In: Computer Vision–ECCV 2020 : 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII. Springer; 2020. p. 769–87.

27. Llopart A. Liftformer: 3D human pose estimation using attention models. arXiv preprint 2020.

28. El Kaid A, Brazey D, Barra V, Baïna K. Top-down system for multi-person 3D absolute pose estimation from monocular videos. Sensors (Basel). 2022;22(11):4109. pmid:35684728

29. Sosa J, Hogg D. Self-supervised 3D human pose estimation from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 2023. p. 4788–97.

30. Zhou L, Chen Y, Wang J. SlowFastFormer for 3D human pose estimation. Comput Vis Image Understand. 2024;243:103992.

31. Li W, Liu M, Liu H, Wang P, Cai J, Sebe N. Hourglass tokenizer for efficient transformer-based 3D human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. p. 604–13.

32. Mehraban S, Adeli V, Taati B. Motionagformer: enhancing 3D human pose estimation with a transformer-gcnformer network. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024. p. 6920–30.

33. Liao Y, Vakanski A, Xian M, Paul D, Baker R. A review of computational approaches for evaluation of rehabilitation exercises. Comput Biol Med. 2020;119:103687. pmid:32339122

34. Nguyen H-C, Nguyen T-H, Scherer R, Le V-H. Deep learning for human activity recognition on 3D human skeleton: survey and comparative study. Sensors (Basel). 2023;23(11):5121. pmid:37299848

35. Sun X, Liu B, Ye X, Xu R, Li H. Self-supervised monocular depth estimation from videos via pose-adaptive reconstruction. In: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2025. p. 1–5. https://doi.org/10.1109/icassp49660.2025.10889476

36. Stenum J, Hsu MM, Pantelyat AY, Roemmich RT. Clinical gait analysis using video-based pose estimation: multiple perspectives, clinical populations, and measuring change. PLOS Digit Health. 2024;3(3):e0000467. pmid:38530801

37. Kim JW, Choi JY, Ha EJ, Choi JH. Human pose estimation using mediapipe pose and optimization method based on a humanoid model. Appl Sci. 2023;13(4):2700.

38. Mehralian MA, Soryani M. EKFPnP: extended Kalman filter for camera pose estimation in a sequence of images. IET Image Process. 2020;14(15):3774–80.

39. Saito A, Kizawa S, Kobayashi Y, Miyawaki K. Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output. Robomech J. 2020;7(1):1–11.

40. Zollhöfer M, Stotko P, Görlitz A, Theobalt C, Nießner M, Klein R, et al. State of the art on 3D reconstruction with RGB-D cameras. Comput Graph Forum. 2018;37(2):625–52.

41. Park K, Allen MS. A Gaussian process regression reduced order model for geometrically nonlinear structures. Mech Syst Signal Process. 2023;184:109720.

42. Wang W, Jing BY. Gaussian process regression: optimality, robustness, and relationship with kernel ridge regression. J Mach Learn Res. 2022;23(193):1–67.

43. Wang Z, Ruan S, Huang T, Zhou H, Zhang S, Wang Y, et al. A lightweight multi-layer perceptron for efficient multivariate time series forecasting. Knowl-Based Syst. 2024;288:111463.

44. Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell. 2002;22(11):1330–4.

45. Olson E. AprilTag: a robust and flexible visual fiducial system. In: 2011 IEEE International Conference on Robotics and Automation. 2011. p. 3400–7.

46. Tang Z, Grompone von Gioi R, Monasse P, Morel J-M. A precision analysis of camera distortion models. IEEE Trans Image Process. 2017;26(6):2694–704. pmid:28333634

47. Arampatzakis V, Pavlidis G, Mitianoudis N, Papamarkos N. Monocular depth estimation: a thorough review. IEEE Trans Pattern Anal Mach Intell. 2024;46(4):2396–414. pmid:37938941

48. Zeng A, Yang L, Ju X, Li J, Wang J, Xu Q. Smoothnet: A plug-and-play network for refining human poses in videos. In: European Conference on Computer Vision. 2022. p. 625–42.

49. Martini E, Boldo M, Bombieri N. FLK: a filter with learned kinematics for real-time 3D human pose estimation. Signal Process. 2024;224:109598.

50. Wang Z, Wang J, Ge N, Lu J. HiMoReNet: a hierarchical model for human motion refinement. IEEE Signal Process Lett. 2023;30:868–72.