Arm Kinematics Research Articles

ENHANCE proof-of-concept three-arm randomized trial: effects of reaching training of the hemiparetic upper limb restricted to the spasticity-free elbow range

Post-stroke motor recovery processes remain unknown. Timescales and patterns of upper-limb (UL) recovery suggest a major impact of biological factors, with modest contributions from rehabilitation. We assessed a novel impairment-based training motivated by motor control theory where reaching occurs within the spasticity-free elbow range. Patients with subacute stroke (≤ 6 month; n = 46) and elbow flexor spasticity were randomly allocated to a 10-day UL training protocol, either personalized by restricting reaching to the spasticity-free elbow range defined by the tonic stretch reflex threshold (TSRT) or non-personalized (non-restricted) and with/without anodal transcranial direct current stimulation. Outcomes assessed before, after, and 1 month post-intervention were elbow flexor TSRT angle and reach-to-grasp arm kinematics (primary) and stretch reflex velocity sensitivity, clinical impairment, and activity (secondary). Results were analyzed for 3 groups as well as those of the effects of impairment-based training. Clinical measures improved in both groups. Spasticity-free range training resulted in faster and smoother reaches, smaller (i.e., better) arm-plane path length, and closer-to-normal shoulder/elbow movement patterns. Non-personalized training improved clinical scores without improving arm kinematics, suggesting that clinical measures do not account for movement quality. Impairment-based training within a spasticity-free elbow range is promising since it may improve clinical scores together with arm movement quality.Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique Identifier: NCT02725853; Initial registration date: 01/04/2016.

Rotation sequences for the calculation of shoulder kinematics of the volleyball attack

Calculating upper extremity kinematics during overhead movements presents with problems typically not seen for the lower extremity due to the large range of motion. Due to these unique issues, different rotation sequences have been suggested to circumvent challenges due to gimbal lock (GL) and angle coherence (AC). The purpose of this study is to determine the most appropriate rotation sequence for shoulder angle calculation during a volleyball attack. METHODS: 15 healthy experienced volleyball players (women = 8) performed 5 attacks off a stationary ball. A 12-camera 3D motion capture system was utilized to record trunk and arm kinematics to compare joint angles calculated using the YXY, ZXY, XZY, YXZ, ZYX, and XYZ rotation sequences. Instances of GL and AC inconsistences were marked for each trial. The last 3 trials were used for analysis. RESULTS: The YXY and XYZ sequences presented with the least total number of errors (12 and 5, respectively). 5 instances of GL were present in the XYZ sequence while none were recorded for the YXY sequence. All other sequences returned incoherent angles that greatly exceeded known ranges of motion. CONCLUSION: When performing kinematic analyses during a volleyball attack, researchers should adhere to ISB recommendations and employ the Eulerian YXY sequence for calculations. If greater anatomical understanding is desired, the XYZ sequence may be utilized for most subjects.

Learning context shapes bimanual control strategy and generalization of novel dynamics.

Bimanual movements are fundamental components of everyday actions, yet the underlying mechanisms coordinating adaptation of the two hands remain unclear. Although previous studies highlighted the contextual effect of kinematics of both arms on internal model formation, we do not know how the sensorimotor control system associates the learned memory with the experienced states in bimanual movements. More specifically, can, and if so, how, does the sensorimotor control system combine multiple states from different effectors to create and adapt a motor memory? Here, we tested motor memory formation in two groups with a novel paradigm requiring the encoding of the kinematics of the right hand to produce the appropriate predictive force on the left hand. While one group was provided with training movements in which this association was evident, the other group was trained on conditions in which this association was ambiguous. After adaptation, we tested the encoding of the learned motor memory by measuring the generalization to new movement combinations. While both groups adapted to the novel dynamics, the evident group showed a weighted encoding of the learned motor memory based on movements of the other (right) hand, whereas the ambiguous group exhibited mainly same (left) hand encoding in bimanual trials. Despite these differences, both groups demonstrated partial generalization to unimanual movements of the left hand. Our results show that motor memories can be encoded depending on the motion of other limbs, but that the training conditions strongly shape the encoding of the motor memory formation and determine the generalization to novel contexts.

Correlation between kinetic and kinematic measures, clinical tests and subjective self-evaluation questionnaires of the affected upper limb in people after stroke.

Assessment of stroke recovery should include multiple sources of information in order to obtain a complete understanding of the individual's rehabilitation progress. Self-evaluation questionnaires' scores do not always correspond to the scores of commonly used clinical evaluation tools. The purpose of this study was to assess the relationship between self-evaluation questionnaires, clinical tests, and kinematic and kinetic analyses of the affected upper limb after stroke, and to determine the correlation between these measures and self-reported general function 2-4 years after the stroke. Twenty-six subjects recovering from stroke were included in the study. Spearman's correlation coefficient was used to measure the correlation between Stroke Impact Scale (SIS), Motor activity Log (MAL), Fugl-Meyer Assessment (FMA) and Action Reach Arm Test (ARAT) scores, and kinematic and kinetic analyses. A logistic regression was used to assess the extent to which these measures may predict the participants' functional self-reported status 2-4 years post stroke. Sections regarding hand function, hand force and general ADL of the self-evaluation questionnaires correlated with kinematic variables. However, only questionnaires that focus on hand function correlated with clinical tests. Mean and maximal hand velocity had the strongest correlations with self-evaluation questionnaires and with the clinical tests, more than other kinematic variables. Self-evaluation questionnaires and clinical tests were found to be correlated with hand kinetic metrics force-to-time ratio and number of force peaks. SIS hand force domain, mean velocity and maximal velocity predicted self-reported general function 2-4 years after the stroke. Self-evaluation questionnaires should be considered for wider use in the clinical evaluation of a patient's stroke recovery, since they add important information on the individual's functional status, which is not reflected in the clinical tests.

Immersive Virtual Reality to Assess Arm Kinematics among Older Adults with and without Major Neurocognitive Disorder – An Exploratory Cross-Sectional Study

Despite the recommendation of improving assessment objectivity and frequency, the use of immersive virtual reality to measure and quantify movement quality remains underexplored. In this study, we aimed to evaluate the reliability, validity and usability of an immersive virtual reality application, KinematicsVR, to assess upper limb kinematics among older adults with and without major neurocognitive disorder. The KinematicsVR involves the drawing of three-dimensional straight lines, circles and squares using a controller in a virtual environment. Twenty-eight older adults with or without major neurocognitive disorder were recruited. Reliability was evaluated through correlations on test–retest and validity through correlations between KinematicsVR variables and other functional tests (TEMPA, BBT-VR and Finger-Nose Test). The usability of the KinematicsVR was assessed with the System Usability Scale questionnaire. Kinematic indexes were compared between eight adults with major neurocognitive disorder and eight matched controls. Results indicated that most variables provided by the KinematicsVR had excellent reliability for tasks involving the drawing of straight lines and circles, but moderate reliability for tasks involving the drawing of squares. Secondary analyses showed that the usability of the application was excellent but few significant and strong correlations were observed between variables of the KinematicsVR and the scores of the TEMPA scale, Finger-Nose Test and BBT-VR. Adults with major neurocognitive disorder, when compared to other older adults, made larger and less linear hand movements. These findings provide perspectives for the use of immersive virtual reality to improve assessment frequency and objectivity through the autonomous measure of upper limb kinematics in older adults.

Learning inverse kinematics using neural computational primitives on neuromorphic hardware

Current low-latency neuromorphic processing systems hold great potential for developing autonomous artificial agents. However, the variable nature and low precision of the underlying hardware substrate pose severe challenges for robust and reliable performance. To address these challenges, we adopt hardware-friendly processing strategies based on brain-inspired computational primitives, such as triplet spike-timing dependent plasticity, basal ganglia-inspired disinhibition, and cooperative-competitive networks and apply them to motor control. We demonstrate this approach by presenting an example of robust online motor control using a hardware spiking neural network implemented on a mixed-signal neuromorphic processor, trained to learn the inverse kinematics of a two-joint robotic arm. The final system is able to perform low-latency control robustly and reliably using noisy silicon neurons. The spiking neural network, trained to control two joints of the iCub robot arm simulator, performs a continuous target-reaching task with 97.93% accuracy, 33.96 ms network latency, 102.1 ms system latency, and with an estimated power consumption of 26.92 μW during inference (control). This work provides insights into how specific computational primitives used by real neural systems can be applied to neuromorphic computing for solving real-world engineering tasks. It represents a milestone in the design of end-to-end spiking robotic control systems, relying on event-driven sensory encoding, neuromorphic processing, and spiking motor control.

Design and Kinematic Modeling of a Soft Continuum Telescopic Arm for the Self-Assembly Mechanism of a Modular Robot.

In recent years, the development of mining robots has grown significantly, offering improved efficiency and safety in hazardous environments. However, there is still room for improvement in adaptability, scalability, and overall performance. The ROBOMINERS project, funded by the European Union's Horizon 2020 Research and Innovation Program, aims to facilitate Europe's access to mineral resources applying disruptive robotic concepts. One such concept is resilience, which can be achieved providing modular mining robots with the ability to reconfigure during operation. To address this challenge, this article presents the development and kinematic modeling of a soft, telescopic, continuum arm integrated into a modular robot. The arm serves as a mechanical interface for coupling different robotic modules or tools following the principle of the car crane. With a fully 3D-printed design, the arm features two sections of variable length that are driven by an innovative actuation method based on soft racks. It provides a 6 degrees of freedom (DoF) motion. The arm kinematic models are obtained by backbone parameterization assuming constant curvature and independent bending between sections for forward kinematics and applying a machine learning-based approach for inverse kinematics. The models are validated through the evaluation of two trajectories, measuring the deviation in each DoF and rack extension. Furthermore, a demonstration of the arm's coupling procedure between two robotic modules and one possible configuration of the robotic system showcases its functionality.

Gaussian process regression for forward and inverse kinematics of a soft robotic arm

The use of soft robotics to perform tasks and interact with the environment requires good system identification. Data-driven methods offer a promising alternative where traditional analytical model-based techniques have proven insufficient. However, their use has been limited and under-explored in soft robotics. The novelty of this research lies in the application of Gaussian processes to soft robotics and the exploration of approximate Gaussian processes (AGP) and deep Gaussian processes (DGP) methods. It highlights the advantages of Gaussian processes in modeling uncertainty, incorporating prior knowledge, and handling complex systems. This is achieved through the identification of the forward and inverse kinematics of a two-degree-of-freedom soft robotic arm actuated by three tendons. A comparison is made between different configurations using Gaussian processes and the results are also compared with those obtained from the analytical model of the kinematics and an artificial neural network (ANN). The research contributes to the development of more efficient and accurate techniques for system identification, kinematics modeling, and control in soft robotics.

Design optimization of medical robotic systems based on task performance metrics: A feasibility study for robotic guided vascular laser treatments

AbstractWhile designing an end‐effector tool for a specific task using an already available robotic arm is a common strategy for developing robot‐based solutions, it is subject to the designer's subjective biases, especially in the medical robotics field. Consequently, this approach can result in suboptimal solutions that may limit the performance of the robotic arm and the overall system. This work introduces a novel framework for developing a virtual scenario that enables the robust optimization of design guidelines for robotics‐based medical solutions. The proposed framework aims to address the issue of subjectivity involved in the design process, improving the reliability and accuracy of the robotic system. Specifically, it aims to find the optimized 3D positioning of the Tool Center Point and the robot base that takes full advantage of the robotic arm kinematics. The feasibility of the proposed methodology is validated for designing a system for robotic‐guided vascular laser treatments. First, a simulated treatment environment is constructed to compute the optimal trajectories to treat a wide population of synthetic human models. Next, a design optimization methodology based on robot task performance metrics is developed. Overall, the proposed design guidelines allowed the performance of 94.1% of the treatment trajectories free of collisions with median precision of around 0.5 mm while guaranteeing high manipulability. Thus, proving the added value of the proposed strategy and showing the potential for promoting the development of optimized robotics‐based solutions.

Improved Rapid-Expanding-Random-Tree-Based Trajectory Planning on Drill ARM of Anchor Drilling Robots

Permanent highway support in deep coal mines now depends on the anchor drilling robot’s drill arm. The drilling arm’s trajectory planning using the conventional RRT (rapid-expanding random tree) algorithm is inefficient and has crooked, rough paths. To improve the accuracy of path planning, we propose an improved RRT algorithm. Firstly, the kinematic model of the drill arm of the drill and anchor robot was established, and the improved DH solution parameters and the positive solution of the drill arm kinematics were solved. The end effector’s attainable working space was calculated using the Monte Carlo approach. Additionally, to address the problem of the slow running speed of the RRT algorithm, an artificial potential field factor was introduced to construct virtual force fields at obstacle and target points and calculate the potential field map for the entire reachable workspace to improve the speed of the sampling points close to the target point. At the same time, the greedy approach and the three-time B-sample curve-fitting method were used simultaneously to remove unnecessary points and carry out smooth path processing in order to improve the quality of the drill arm trajectory. This was carried out in order to solve the issue of rough pathways generated by the RRT algorithm. Finally, 50 time-sampling comparison experiments were conducted on 2D and 3D maps. The experimental results showed that the improved RRT algorithm improved the average sampling speed by 20% and reduced the average path length by 14% compared with the RRT algorithm, which verified the feasibility and effectiveness of this improved RRT algorithm. The improved RRT algorithm generates more efficient and smoother paths, which can improve the intelligence of the support process by integrating and automating drilling and anchoring and providing reliable support for coal mine intelligence.

The long-term effects of aggressive spasticity reducing treatment, including selective dorsal rhizotomy, on joint kinematic outcomes of persons with cerebral palsy

BackgroundSelective dorsal rhizotomy (SDR) creates a large and permanent reduction of spasticity for children with cerebral palsy (CP). Previous SDR outcomes studies have generally lacked appropriate control groups, had limited sample sizes, or reported short-term follow-up, limiting evidence for improvement in long-term gait function. Research questionDoes aggressive spasticity management for individuals with CP improve long-term gait kinematics (discrete joint kinematics) compared to a control group of individuals with CP with minimal spasticity management? MethodsThis study was a secondary analysis – focused on joint-level kinematics – of a previous study evaluating the long-term outcomes of SDR. Two groups of participants were recruited based on a retrospectively completed baseline clinical gait study. One group received aggressive spasticity treatment including a selective dorsal rhizotomy (Yes-SDR group), while the other group had minimal spasticity management (No-SDR group). Both groups had orthopedic surgery treatment. Groups were matched on baseline spasticity. All participants prospectively returned for a follow-up gait study in young adulthood (greater than 21 years of age and at least 10 years after baseline). Change scores in discrete kinematic variables from baseline to follow-up were assessed using a linear model that included treatment arm (Yes-SDR, No-SDR), baseline age, and baseline kinematic value. For treatment arm, 5° and 5 Gait Deviation Index points were selected as thresholds to be considered a meaningful difference between treatment groups. ResultsAt follow-up, there were no meaningful differences in pelvis, hip, knee, or ankle kinematic variable changes between treatment arms. Max knee flexion – swing showed a moderate treatment effect for Yes-SDR, although it did not reach the defined threshold. SignificanceAggressive spasticity treatment does not result in meaningful differences in gait kinematics for persons with cerebral palsy in young adulthood compared to minimal spasticity management with both groups having orthopedic surgery.

Elbow-sideWINDER (Elbow-side Wearable INDustrial Ergonomic Robot): design, control, and validation of a novel elbow exoskeleton.

Musculoskeletal Disorders associated with the elbow are one of the most common forms of work-related injuries. Exoskeletons have been proposed as an approach to reduce and ideally eliminate these injuries; however, exoskeletons introduce their own problems, especially discomfort due to joint misalignment. The Elbow-sideWINDER with its associated control strategy is a novel elbow exoskeleton to assist elbow flexion/extension during occupational tasks. This study describes the exoskeleton showing how this can minimize discomfort caused by joint misalignment, maximize assistive performance, and provide increased robustness and reliability in real worksites. The proposed medium-level control strategy can provide effective assistive torque using three control units as follows: an arm kinematics estimator, a load estimator, and a friction compensator. The combined hardware/software system of the Elbow-sideWINDER is tested in load-lifting tasks (2 and 7 kg). This experiment focuses on the reduction in the activation level of the biceps brachii and triceps brachii in both arms and the change in the range of motion of the elbow during the task. It is shown that using the Elbow-sideWINDER, the biceps brachii, responsible for the elbow flexion, was significantly less activated (up to 38.8% at 2 kg and 25.7% at 7 kg, on average for both arms). For the triceps brachii, the muscle activation was reduced by up to 37.0% at 2 kg and 35.1% at 7 kg, on average for both arms. When wearing the exoskeleton, the range of motion of the elbow was reduced by up to 13.0° during the task, but it was within a safe range and could be compensated for by other joints such as the waist or knees. There are extremely encouraging results that provide good indicators and important clues for future improvement of the Elbow-sideWINDER and its control strategy.

Measuring Arm and Hand Joint Kinematics to Estimate Impairment During a Functional Reach and Grasp Task after Stroke

Background Current approaches to characterizing deficits in upper limb movements after stroke typically focus either on changes in a functional measure, for example, how well a patient can complete a task, or changes in impairment, for example, isolated measurements of joint range of motion. However, there can be notable dissociations between static measures of impairment versus those of function. Objective We develop a method to measure upper limb joint angles during performance of a functional task and use measurements to characterize joint impairment in the context of a functional task. Methods We developed a sensorized glove that can precisely measure select finger, hand, and arm joints while participants complete a functional reach-to-grasp task involving manipulation of a sensorized object. Results We first characterized the accuracy and precision of the glove’s joint angle measurements. We then measured joint angles in neurologically intact participants (n = 4 participants, 8 limbs) to define the expected distribution of joint angle variation during task execution. These distributions were used to normalize finger, hand, and arm joint angles in stroke participants (n = 6) as they performed the task. We present a participant-specific visualization of functional joint angle variance which illustrated that stroke participants with nearly identical clinical scores exhibited unique patterns of joint angle variation. Conclusions Overall, measuring individual joint angles in the context of a functional task may inform whether changes in functional scores over recovery or rehabilitation are driven by changes in impairment or the development of compensatory strategies, and provide a quantified path toward personalized rehabilitative therapy.

Analysis of Forward Kinematics of 2R Robotic Arm

In Kinematics, the analysis of acceleration and velocity is usually done whereas the forces are neglected. The machine’s smallest unit is the link, which is akin to a cell in the human body. Robotics is built on the foundation of machines. A pair is established by the combination of links which leads to the construction of linkage. A mechanical linkage is a collection of bodies that are connected to control forces and movement. The combination of linkage (also known as Mechanism) generates Machine. The robot’s kinematics equations are used in robotics, video games and animation. Inverse kinematics is the mathematical process of determining variable joint parameters necessary to place an object at the end of a kinematic chain in a specific position and orientation with respect to the start of the chain, such as robot manipulator or skeleton of an animated character. The role of Forward and Inverse Kinematics in a 2R Robotic Arm are studied with successful demonstrations. Forward Kinematics of a 2R Robotic Arm using Matlab and Python confirming similar results are exhibited (Figure 8 and Figure 9). Matlab illustrated 2D Path Tracing for 2R Robotic Arm with Inverse Kinematics. Finally, inverse kinematics of 2R Robotic Arm in Matlab using fuzzy logic is modelled successfully. Thus, it helps in the understanding of kinematics and can be used in the simulation of machines.

A methodological exploration to study 2D arm kinematics in Ophiuroidea (Echinodermata)

Brittle stars, unlike most other echinoderms, do not use their small tube feet for locomotion but instead use their flexible arms to produce a rowing or reverse rowing movement. They are among the fastest-moving echinoderms with the ability of complex locomotory behaviors. Considering the high species diversity and variability in morphotypes, a proper understanding of intra- and interspecies variation in arm flexibility and movement is lacking. This study focuses on the exploration of the methods to investigate the variability in brittle star locomotion and individual arm use. We performed a two-dimensional (2D) image processing on horizontal movement only. The result indicated that sinuosity, disc displacement and arm angle are important parameters to interpret ophiuroid locomotion. A dedicated Python script to calculate the studied movement parameters and visualize the results applicable to all 5-armed brittle stars was developed. These results can serve as the basis for further research in robotics inspired by brittle star locomotion.

The influence of gender on shoulder kinematics and head-hip technique during non-level transfers in full-time wheelchair users

BackgroundNon-level transfers are some of the most demanding tasks for manual wheelchair users. Safely performing these transfer tasks may be needed for maintaining long-term upper limb health. This cross-sectional study aimed to examine the influence of gender on the head-hip technique and lead arm kinematics during multi-height transfers in manual wheelchair users. MethodsMotion analysis was used to obtain lead-arm shoulder kinematics (flexion/extension and abduction/adduction) and trunk flexion during level, uphill, and floor-to-table transfers in full-time manual wheelchair users. FindingsTwelve male (N = 12) and fifteen female (N = 15) manual wheelchair users with a mean age of 23 ± 5 years and no signs of shoulder pain participated in the study. Lead-arm shoulder flexion and abduction increased as vertical displacement requirements increased during the various transfer tasks (p < 0.01). Women displaced greater trunk flexion during level transfers at approaching significant levels (p = 0.07). During uphill transfers, women displayed significantly greater trunk flexion than men (p < 0.05). InterpretationManual wheelchair users use unique kinematic requirements when using the head-hip during level and non-level transfers. Women may be at decreased risk of chronic shoulder pain due to a greater use of the head-hip technique during non-level transfers. Nonetheless, more research that integrates kinetics and strength assessments during non-level transfer biomechanical analyses is needed to better understand technical requirements of non-level transfers in manual wheelchair users.

A Mobile Robot with a Manipulator to Alleviate the Shortage of Health Workers in Hospitals in COVID 19

COVID-19 has brought an unprecedented burden to hospitals. Hospitals are facing many problems, such as understaffing and infected employees. But this has also promoted the development of hospital robots to a certain extent. This paper aimed to develop a robot that can move around the hospital and pick and send stuff to patients. The robot is mainly divided into 2 two parts: the moving part and the grasping part. The moving part includes map construction, localization, and navigation with gmapping, A*, and DWA algorithms that would stimulate on the ROS platform. For the grasping part, the forward and inverse kinematics of the robot arm and PD control would be analyzed using MATLAB with the Robotics System Toolbox, Simulink, and Simscape Multibody to simulate and control the robot arm. Using this approach, the robot can reach the target position and avoid dynamic obstacles. In addition, the robot arm can pick up and drop the ball with the designed trajectory.

Inverse Kinematic of 1-DOF Robot Manipulator Using Sparse Identification of Nonlinear System

Robot Manipulator is the most robot used in industry since it can act like a human arm that can move objects. Research on robot manipulator has been widely carried out in various problems such as control systems, intelligence robots, degrees of freedom, mechanics-electronics systems and various other problems. In control systems there are studies to design of robot motion through kinematics. However, modeling the kinematic motion which has nonlinear characteristics will be more difficult if the number of degrees of freedom increases. To overcome this problem, this research will proposed sparse regression to modeling the kinematics of a robotic arm with the black box principle modeling. The results obtained indicate that the method The proposed one has the ability to identify robots manipulator with a fitness score of up to 100%. This matter shows that the proposed method can modeling the kinematic inverse of the manipulator robot without through complex calculations. From this research is expected can provide other research opportunities related to identification kinematics with the identification system method

Cerebellar Transcranial Direct Current Stimulation Modulates Anticipatory Postural Adjustments in Healthy Adults.

During forward swinging of the arm, the central nervous system must anticipate the effect of upraising upon the body. Little is known about the cerebellar network that coordinates these anticipatory postural adjustments (APAs). Stimulating different cerebellar regions with transcranial direct current stimulation (tDCS) and with different polarities modulated the APAs. We used surface electromyography (sEMG) to measure muscle activities in a bilateral rapid shoulder flexion task. The onset of APAs was altered after tDCS over the vermis, while the postural stability and the kinematics of arm raising were not affected. To our knowledge, this is the first human cerebellar-tDCS (c-tDCS) study to separate cerebellar involvement in core muscle APAs in bilateral rapid shoulder flexion. These data contribute to our understanding of the cerebellar network supporting APAs in healthy adults. Modulated APAs of the erector spinae by tDCS on the vermis may be related to altered cerebellar brain inhibition (CBI), suggesting the importance of the vermal-cerebral connections in APAs regulation.

Accuracy and feasibility of a novel fine hand motor skill assessment using computer vision object tracking

We developed a computer vision-based three-dimension (3D) motion capture system employing two action cameras to examine fine hand motor skill by tracking an object manipulated by a hand. This study aimed to examine the accuracy and feasibility of this approach for detecting changes in a fine hand motor skill. We conducted three distinct experiments to assess the system's accuracy and feasibility. We employed two high-resolution, high-frame-rate action cameras. We evaluated the accuracy of our system in calculating the 3D locations of moving object in various directions. We also examined the system's feasibility in identifying improvement in fine hand motor skill after practice in eleven non-disabled young adults. We utilized color-based object detection and tracking to estimate the object's 3D location, and then we computed the object's kinematics, representing the endpoint goal-directed arm reaching movement. Compared to ground truth measurements, the findings demonstrated that our system can adequately estimate the 3D locations of a moving object. We also showed that the system can be used to measure the endpoint kinematics of goal-directed arm reaching movements to detect changes in fine hand motor skill after practice. Future research is needed to confirm the system's reliability and validity in assessing fine hand motor skills in patient populations.