Depth Camera Research Articles

Effects of dataset curation on body condition score (BCS) determination with a vision transformer (ViT) applied to RGB+depth images

Body condition score (BCS) has been a useful tool in estimating the health of cattle for many years now. This categorical metric requires experienced observers to visually inspect cows and assess body fat deposits regularly via a time consuming, subjective process. Low cost RGB+depth cameras have been used alongside machine learning algorithms in the past and have shown great promise, however, more advanced techniques are projected to yield better performance. In this work, a vision transformer (ViT) is pretrained using a recently developed self-supervised pretraining method, masked image modeling, and then fine-tuned on RGB+depth BCS data with the objective of improving performance. Model accuracy was found to be highly dependent on dataset curation, ranging from 64% to 92% accuracy. These discrepancies are attributed to non-unique data in the training and test splits and an inherently unbalanced dataset, both of which are discussed in detail. It is recommended that engineers and animal scientists collaborate more closely, as certain details related to dataset curation are critical to thoroughly assess performance and robustness of automated methods for BCS determination.

Research on Six-Degree-of-Freedom Refueling Robotic Arm Positioning and Docking Based on RGB-D Visual Guidance

The main contribution of this paper is the proposal of a six-degree-of-freedom (6-DoF) refueling robotic arm positioning and docking technology guided by RGB-D camera visual guidance, as well as conducting in-depth research and experimental validation on the technology. We have integrated the YOLOv8 algorithm with the Perspective-n-Point (PnP) algorithm to achieve precise detection and pose estimation of the target refueling interface. The focus is on resolving the recognition and positioning challenges of a specialized refueling interface by the 6-DoF robotic arm during the automated refueling process. To capture the unique characteristics of the refueling interface, we developed a dedicated dataset for the specialized refueling connectors, ensuring the YOLO algorithm’s accurate identification of the target interfaces. Subsequently, the detected interface information is converted into precise 6-DoF pose data using the PnP algorithm. These data are used to determine the desired end-effector pose of the robotic arm. The robotic arm’s movements are controlled through a trajectory planning algorithm to complete the refueling gun docking process. An experimental setup was established in the laboratory to validate the accuracy of the visual recognition and the applicability of the robotic arm’s docking posture. The experimental results demonstrate that under general lighting conditions, the recognition accuracy of this docking interface method meets the docking requirements. Compared to traditional vision-guided methods based on OpenCV, this visual guidance algorithm exhibits better adaptability and effectively provides pose information for the robotic arm.

A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8.

Yunnan Xiaomila is a pepper variety whose flowers and fruits become mature at the same time and multiple times a year. The distinction between the fruits and the background is low and the background is complex. The targets are small and difficult to identify. This paper aims at the problem of target detection of Yunnan Xiaomila under complex background environment, in order to reduce the impact caused by the small color gradient changes between xiaomila and background and the unclear feature information, an improved PAE-YOLO model is proposed, which combines the EMA attention mechanism and DCNv3 deformable convolution is integrated into the YOLOv8 model, which improves the model's feature extraction capability and inference speed for Xiaomila in complex environments, and achieves a lightweight model. First, the EMA attention mechanism is combined with the C2f module in the YOLOv8 network. The C2f module can well extract local features from the input image, and the EMA attention mechanism can control the global relationship. The two complement each other, thereby enhancing the model's expression ability; Meanwhile, in the backbone network and head network, the DCNv3 convolution module is introduced, which can adaptively adjust the sampling position according to the input feature map, contributing to stronger feature capture capabilities for targets of different scales and a lightweight network. It also uses a depth camera to estimate the posture of Xiaomila, while analyzing and optimizing different occlusion situations. The effectiveness of the proposed method was verified through ablation experiments, model comparison experiments and attitude estimation experiments. The experimental results indicated that the model obtained an average mean accuracy (mAP) of 88.8%, which was 1.3% higher than that of the original model. Its F1 score reached 83.2, and the GFLOPs and model sizes were 7.6G and 5.7MB respectively. The F1 score ranked the best among several networks, with the model weight and gigabit floating-point operations per second (GFLOPs) being the smallest, which are 6.2% and 8.1% lower than the original model. The loss value was the lowest during training, and the convergence speed was the fastest. Meanwhile, the attitude estimation results of 102 targets showed that the orientation was correctly estimated exceed 85% of the cases, and the average error angle was 15.91°. In the occlusion condition, 86.3% of the attitude estimation error angles were less than 40°, and the average error angle was 23.19°. The results show that the improved detection model can accurately identify Xiaomila targets fruits, has higher model accuracy, less computational complexity, and can better estimate the target posture.

Evaluation of a Stereo Depth Camera for Part Monitoring For CNC Machining

Evaluation of a Stereo Depth Camera for Part Monitoring For CNC Machining

Immediate SVF-Gel Injection Reduced Incision Scar Formation: A Prospective, Double-Blind, Randomized, Self-control Trial.

Skin incision scars are cosmetically displeasing; the effects of current treatments are limited, and new methods to reduce scar formation need to be found. We sought to determine whether immediate postoperative injection of stromal vascular fraction gel (SVF-gel) could reduce scar formation at skin incision sites. A prospective, randomized, double-blind, self-controlled trial was conducted in patients who underwent breast reduction. SVF-gel was intradermally injected into the surgical incision on one randomly selected side, with the other side receiving saline as a control. At the 6-month follow-up, the incision scars were evaluated using the Vancouver scar scale (VSS) and visual analog scale (VAS). Antera 3D camera was used for objective evaluation. The VSS score and VAS score were significantly different between the SVF-gel-treated side (3.80 ± 1.37, 3.37±1.25) and the control side (5.25 ± 1.18, 4.94 ± 1.28). Moreover, the SVF-gel-treated side showed statistically significant improvements in scar appearance, based on evidences from Antera 3D camera. This was a single-center, single-race, and single-gender study. Furthermore, the results were available only for the 6-month interim follow-up period. Postoperative immediate SVF-gel injection in surgical incisions can reduce scar formation, and exert a preventive effect on scars. Evidence obtained from at least one properly designed randomized controlled trial. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A Wearable Visually Impaired Assistive System Based on Semantic Vision SLAM for Grasping Operation.

Because of the absence of visual perception, visually impaired individuals encounter various difficulties in their daily lives. This paper proposes a visual aid system designed specifically for visually impaired individuals, aiming to assist and guide them in grasping target objects within a tabletop environment. The system employs a visual perception module that incorporates a semantic visual SLAM algorithm, achieved through the fusion of ORB-SLAM2 and YOLO V5s, enabling the construction of a semantic map of the environment. In the human-machine cooperation module, a depth camera is integrated into a wearable device worn on the hand, while a vibration array feedback device conveys directional information of the target to visually impaired individuals for tactile interaction. To enhance the system's versatility, a Dobot Magician manipulator is also employed to aid visually impaired individuals in grasping tasks. The performance of the semantic visual SLAM algorithm in terms of localization and semantic mapping was thoroughly tested. Additionally, several experiments were conducted to simulate visually impaired individuals' interactions in grasping target objects, effectively verifying the feasibility and effectiveness of the proposed system. Overall, this system demonstrates its capability to assist and guide visually impaired individuals in perceiving and acquiring target objects.

Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.

Monitoring oxygen saturation ( ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions. We aim to develop and validate a contactless measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative for monitoring. We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in which is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction and regression. In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with reference values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimated values was within 3% of the reference for of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% in estimations compared to gold-standard polysomnography. The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.

Optimizing Camera Setup for In-Home First-Person Rendering Mixed-Reality Gaming System

With the advent of 3D humanoid reconstruction techniques, using a realistic 3D human avatar in a serious game has become popular. This realistic representation in the virtual environment could be achieved using a single low-cost RGB-D camera such as Kinect. However, properly setting up such a camera system for high-quality rendering can be challenging due to the relatively restricted in-home environment. In this paper, we address the challenge of finding optimized camera setup guidelines for an in-home first-person perspective mixed reality (MR) gaming system. We use an MR system with personalized humanoids to simulate the texture reconstruction for a user under a specific camera configuration. Then, a derivative-free optimization is leveraged as a black box approach to search for the optimized camera setup through iterative simulation. We also introduce a novel skeleton-based calibration to address the effects of physically varying the camera's position. For evaluation, two experiments are carried out to evaluate the correctness and effectiveness of the proposed calibration. Furthermore, we conduct a case study using simulation-based optimization for reconstructing lower limb amputees. This work can potentially help locate a proper camera setup for an MR system within the constraints of an in-home environment.

Online estimation of vertical velocity for deep-sea carriers based on depth data

Seabed residential stations are essential for the future exploration of the deep-sea exploration. Deep-sea carriers accompany transport equipment for underwater resident stations. To accommodate the low-energy consumption and low-cost nature of deep-sea carriers, an online vertical velocity estimation method based on depth data is proposed. This method serves as a cost-effective alternative to the expensive acoustic and inertial velocity measurement equipment conventionally used in underwater navigation systems. To meet the demand for low-cost online velocity measurements, a dynamic model was created for buoyancy-driven vehicles such as deep-sea carriers during unpowered ascent. Two distinct methods were used to process depth sensor data: one involves central differencing followed by low-pass filtering, and the other entails the differentiation of the data after fitting it to dynamic equations. This approach yields two different types of estimated velocities. A sliding window was defined and used the variance of the velocity and fitting coefficient within the window as indicators to assess the uncertainty of the velocity data. Based on this evaluation, an adaptive weight allocation rule for the fusion process was formulated. This approach allows us to derive the weighted estimated velocity. Through lake trials, the weighted estimated velocity was validated to closely match the ideal velocity data and showed a substantial reduction in velocity variance compared to the differenced estimated velocity that was not fused. This indicates that the fluctuations in the velocity data were significantly reduced. In addition, the integrated displacement showed an average absolute error of 0.1769 m compared with the measured displacement. This indicates that the weighted estimated velocity effectively reflects the true state of motion of the deep-sea carrier. Furthermore, when unexpected motion occurs in deep-sea carriers, the weighted estimated velocity can prevent falling into the "smoothness trap.” This demonstrated the robustness of the proposed algorithm.

Dose–toxicity surface histogram-based prediction of radiation dermatitis severity and shape

Objective. This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade. Approach. Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose–toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity. Main results. The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients. Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Liver respiratory-induced motion estimation using abdominal surface displacement as a surrogate: robotic phantom and clinical validation with varied correspondence models

PurposeThis work presents the implementation of an RGB-D camera as a surrogate signal for liver respiratory-induced motion estimation. This study aims to validate the feasibility of RGB-D cameras as a surrogate in a human subject experiment and to compare the performance of different correspondence models.MethodsThe proposed approach uses an RGB-D camera to compute an abdominal surface reconstruction and estimate the liver respiratory-induced motion. Two sets of validation experiments were conducted, first, using a robotic liver phantom and, secondly, performing a clinical study with human subjects. In the clinical study, three correspondence models were created changing the conditions of the learning-based model.ResultsThe motion model for the robotic liver phantom displayed an error below 3 mm with a coefficient of determination above 90% for the different directions of motion. The clinical study presented errors of 4.5, 2.5, and 2.9 mm for the three different motion models with a coefficient of determination above 80% for all three cases.ConclusionRGB-D cameras are a promising method to accurately estimate the liver respiratory-induced motion. The internal motion can be estimated in a non-contact, noninvasive and flexible approach. Additionally, three training conditions for the correspondence model are studied to potentially mitigate intra- and inter-fraction motion.

Abiotic drivers of the space use and activity of gray reef sharks Carcharhinus amblyrhynchos in a dynamic tidal environment.

Predators display rhythms in behavior and habitat use, often with the goal of maximizing foraging success. The underlying mechanisms behind these rhythms are generally linked to abiotic conditions related to diel, lunar, or seasonal cycles. To understand their effects on the space use, activity, and swimming depth of gray reef sharks (Carcharhinus amblyrhynchos), we tagged 38 individuals with depth and accelerometer sensors in a French Polynesian atoll channel exposed to strong tidal flow, and monitored them over a year. C. amblyrhynchos used a larger space during nighttime and were more active at night and during outgoing currents. Shark activity also peaked during the full and new moons. The swimming depth of sharks was mostly influenced by diel cycles, with sharks swimming deeper during the day compared to nighttime. The dynamic energyscape may promote the emergence of discrete behavioral strategies in reef sharks that use the south channel of Fakarava for resting and foraging purposes. Turbulence imposed by outgoing tides induces additional foraging cost on sharks, shifting their hunting areas to the southern part of the channel, where turbulence is less pronounced. Understanding when and where sharks are active and foraging is important for our understanding of predator-prey dynamics and ecosystem dynamics. This study highlights how abiotic rhythms in a highly dynamic environment likely generate spatiotemporal heterogeneity in the distribution of predation pressure.

Toward Intraoperative Visual Intelligence: Real-Time Surgical Instrument Segmentation for Enhanced Surgical Monitoring.

Open surgery relies heavily on the surgeon's visual acuity and spatial awareness to track instruments within a dynamic and often cluttered surgical field. This system utilizes a head-mounted depth camera to monitor surgical scenes, providing both image data and depth information. The video captured from this camera is scaled down, compressed using MPEG, and transmitted to a high-performance workstation via the RTSP (Real-Time Streaming Protocol), a reliable protocol designed for real-time media transmission. To segment surgical instruments, we utilize the enhanced U-Net with GridMask (EUGNet) for its proven effectiveness in surgical tool segmentation. For rigorous validation, the system's performance reliability and accuracy are evaluated using prerecorded RGB-D surgical videos. This work demonstrates the potential of this system to improve situational awareness, surgical efficiency, and generate data-driven insights within the operating room. In a simulated surgical environment, the system achieves a high accuracy of 85.5% in identifying and segmenting surgical instruments. Furthermore, the wireless video transmission proves reliable with a latency of 200 ms, suitable for real-time processing. These findings represent a promising step towards the development of assistive technologies with the potential to significantly enhance surgical practice.

Multi-view 3D reconstruction of seedling using 2D image contour

3D reconstruction of seedling can provide comprehensive and quantitative spatial structure information, offering an effective digital tool for breeding research. However, accurate and efficient reconstruction of seedling is still a challenging work due to limited performance of depth sensor for seedling with small-size stem and unavoidable error for multi-view point cloud registration. Therefore, in this paper, we propose an accurate multi-view 3D reconstruction method for seedling using 2D image contour to constrain 3D point cloud. The rotation axis is calibrated and optimised by minimising point-to-contour distance between 2D image contour and projected exterior points from 3D point cloud. Then, to remove outliers and noise, we introduce the seedling mask of 2D image to constrained and delete projected outlier points of 3D model from corresponding view. Furthermore, we propose a residual-guided method to recognise missing region for 3D model and complete 3D model of small-size stem. Finally, we can obtain an accurate 3D model of seedling. The reconstruction accuracy is evaluated by average distance between projected contour of 3D model and 2D image contour of all views (0.3185 mm). Then, the phenotypic parameters were calculated from 3D model and the results are close to manual measurements (Plant height: R2 = 0.98, RMSE = 2.3 mm, rRMSE = 1.52%; Petioles inclination angle: R2 = 0.99, RMSE = 0.73°, rRMSE = 1.41%; Leaf area: R2 = 0.66, RMSE = 1.05 cm2, rRMSE = 7.63%; Leaf inclination angle: R2 = 0.99, RMSE = 1.01°, rRMSE = 1.72%; Stem diameter: R2 = 0.95, RMSE = 0.12 mm, rRMSE = 5.43%). Breeders can improve the selection of more resilient varieties and cultivars to different growing conditions starting from the dynamic analysis of their phenotype.

Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross-sectional study.

The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a deep learning (DL) framework to accurately determine malnutrition based on a 3D facial points cloud. A group of 482 patients were studied in this perspective work. The 3D facial data were obtained using a 3D camera and represented as a 3D facial points cloud. A DL model, PointNet++, was trained and evaluated using the points cloud as inputs and classified the malnutrition states. The performance was evaluated with the area under the receiver operating characteristic curve, accuracy, specificity, sensitivity, and F1 score. Among the 482 patients, 150 patients (31.1%) were diagnosed as having moderate malnutrition and 54 patients (11.2%) as having severe malnutrition. The DL model achieved the performance with an area under the receiver operating characteristic curve of 0.7240 ± 0.0416. The DL model achieved encouraging performance in accurately classifying nutrition states based on a points cloud of 3D facial information of patients with malnutrition.

Bridging the simulation-to-real gap of depth images for deep reinforcement learning

While deep reinforcement learning (DRL) models are effective at learning appropriate actions from high-dimensional data, they require large amounts of costly and time-consuming training data to be collected in real-world settings. For this reason, collecting data in simulations offers a promising alternative, but transferring policy networks from simulation to reality can be challenging due to differences in perception between the virtual and real worlds. This paper proposes a two-level method to bridge the simulation-to-reality (sim-to-real) gap for depth images, specifically for autonomous environmental navigation that uses DRL. Simulated depth images are first translated at a perception level through generative adversarial network (GAN) to make them look like real data from a depth sensor. Simulated and GAN-generated depth images are encoded into latent representations, and the encoder is trained in the latent space to make the two images paired. This encoder is trained simultaneously with a reinforcement learning network model to extract domain-invariant and task-relevant features from depth images and map the behavioral similarity of states to the latent space. Our experimental results demonstrate that our approach can effectively bridge the sim-to-real gap, enabling policies learned in simulation to maintain their control performance in the real world.

Design and evaluation of an AR-based thermal imaging system for planning reconstructive surgeries.

Thermal imaging can be used for the non-invasive detection of blood vessels of the skin. However, mapping the results to the patient currently lacks user-friendliness. Augmented reality may provide a useful tool to superimpose thermal information on the patient. A system to support planning in reconstructive surgery using a thermal camera was designed. The obtained information was superimposed on the physical object using a Microsoft HoloLens. An RGB, depth, and thermal camera were combined to capture a scene of different modalities and reconstruct a virtual scene in real time. To register the different cameras and the AR device, an active calibration target was developed and evaluated. A Vuforia marker was used to register the hologram in the virtual space. The accuracy of the projected hologram was evaluated in a laboratory setting with participants by measuring the error between the physical object and the hologram. The AR-based system was evaluated by 21 participants in a laboratory setting. The mean projection error is 10.3 ± 9.4mm. The system is able to stream a three-dimensional scene with augmented thermal information in real time at 5 frames per second. The active calibration target can be used independently of the environment. The calibration target provides an easy-to-use method for the registration of cameras capturing the visible to long-infrared spectral range. The inside-out tracking of the HoloLens in combination with a Vuforia marker is not accurate enough for the intended clinical use.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

Camera-Based Safety System for Optical Wireless Power Transmission Using Dynamic Safety-Distance

This paper introduces a new safety approach for Optical Wireless Power Transmission (OWPT), a technology that is integral to the new kinds of Wireless Power Transmission technology (WPT). It starts from the fundamental configuration of the current OWPT system, addressing the safety concerns related to lasers by involving laser irradiation hazards, laser exposure regulation and guidelines, and a comparison with other safety methods. A camera-based OWPT safety system focused on the emission control of the light source is proposed, and it utilizes a depth camera and finely tuned computer vision-based control program. Through meticulous system design and experiments, the proposed system can detect moving objects in a limited indoor environment and control the laser/LED light transmission according to the object’s velocity dynamically. Various functions and exclusive improvements towards OWPT operation are mentioned, and Dynamic Safety-Distance is proposed as the core mechanism of the safety system. Through on-site experiments, indoor safety control and system operation’s evaluation are discussed, acknowledging both the advantages and limitations of the proposed safety system. This paper concludes with suggestions for further developments in camera-based OWPT safety incorporating the concept of Automatic Emission Control.

Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises.

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for upper limb rehabilitation following a stroke, potentially impeding functional recovery. These aberrant movements are prevalent among stroke survivors and can hinder their progress in rehabilitation, making it crucial to address this issue. This study evaluated the efficacy of visual feedback, facilitated by an RGB-D camera, in reducing trunk compensation. In total, 17 able-bodied individuals and 18 stroke survivors performed reaching tasks under unrestricted trunk conditions and visual feedback conditions. In the visual feedback modalities, the target position was synchronized with trunk movement at ratios where the target moved at the same speed, double, and triple the trunk's motion speed, providing real-time feedback to the participants. Notably, trunk compensatory movements were significantly diminished when the target moved at the same speed and double the trunk's motion speed. Furthermore, these conditions exhibited an increase in the task completion time and perceived exertion among stroke survivors. This outcome suggests that visual feedback effectively heightened the task difficulty, thereby discouraging unnecessary trunk motion. The findings underscore the pivotal role of customized visual feedback in correcting aberrant upper limb movements among stroke survivors, potentially contributing to the advancement of robotic-assisted rehabilitation strategies. These insights advocate for the integration of visual feedback into rehabilitation exercises, highlighting its potential to foster more effective recovery pathways for post-stroke individuals by minimizing undesired compensatory motions.