Wrist Kinematics Research Articles

Accuracy of a Low-Cost 3D-Printed Wearable Goniometer for Measuring Wrist Motion.

Wrist motion provides an important metric for disease monitoring and occupational risk assessment. The collection of wrist kinematics in occupational or other real-world environments could augment traditional observational or video-analysis based assessment. We have developed a low-cost 3D printed wearable device, capable of being produced on consumer grade desktop 3D printers. Here we present a preliminary validation of the device against a gold standard optical motion capture system. Data were collected from 10 participants performing a static angle matching task while seated at a desk. The wearable device output was significantly correlated with the optical motion capture system yielding a coefficient of determination (R) of 0.991 and 0.972 for flexion/extension (FE) and radial/ulnar deviation (RUD) respectively (p < 0.0001). Error was similarly low with a root mean squared error of 4.9° (FE) and 3.9° (RUD). Agreement between the two systems was quantified using Bland–Altman analysis, with bias and 95% limits of agreement of 3.1° ± 7.4° and −0.16° ± 7.7° for FE and RUD, respectively. These results compare favourably with current methods for occupational assessment, suggesting strong potential for field implementation.

Sensing leg movement enhances wearable monitoring of energy expenditure

Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.

Ulnar Extension Coupling in Functional Wrist Kinematics During Hand Activities of Daily Living

Wrist circumduction is increasingly used as a functional motion assessment for patients. Thus, increasing our understanding of its relation to the functional motion envelope is valuable. Previous studies have shown that the wrist is preferentially extended during hand activities of daily living (ADLs), with greater ulnar than radial deviation. The purpose of this study was to characterize the functional wrist motions of 22 modern ADLs in healthy subjects. We hypothesized that the subjects would perform ADLs predominantly in ulnar extension. Ten right-handed, healthy subjects performed flexion-extension, radioulnar deviation, maximal circumduction, and 22 modern ADLs. Angular wrist positions were obtained by tracking retroreflective markers on the hand and forearm. Angular motion data were analyzed with a custom program for peak/trough angles in flexion extension and radioulnar deviation, ellipse area of circumduction data, and ellipse area of combined motion data. The required ranges of motion for ADLs were from 46.6° ± 16.5° of flexion (stirring task) to 63.8° ± 14.2° of extension (combing) in flexion-extension and from 15.6° ± 8.9° of radial deviation (opening a jar) to 32.5° ± 8.3° of ulnar deviation (picking up smartphone) in radioulnar deviation. Ellipse area of combined motion data of the 22 ADLs were, on average, 58.2% ± 14.3% of the ellipse area of maximal circumduction. A motion data quadrantal analysis revealed that 54.9% of all ADL wrist motion occurred in ulnar extension. Among the average wrist positions for 22 ADLs, 16 were located in the ulnar extension quadrant. This study revealed a functional wrist motion envelope that was less than 60% of wrist maximal motion capacity on average. Our results also showed that the majority of ADLs are performed in ulnar extension of the wrist. Baseline values for healthy subjects performing 22 wrist ADLs can informfuture studies assessing dysfunction, postsurgical changes, and rehabilitation progress.

Intramuscular Stimulation of Muscle Afferents Attains Prolonged Tremor Reduction in EssentialTremor Patients.

This study proposes and clinically tests intramuscular electrical stimulation below motor threshold to achieve prolonged reduction of wrist flexion/extension tremor in Essential Tremor (ET) patients. The developed system consisted of an intramuscular thin-film electrode structure that included both stimulation and electromyography (EMG) recording electrodes, and a control algorithm for the timing of intramuscular stimulation based on EMG (closed-loop stimulation). Data were recorded from nine ET patients with wrist flexion/extension tremor recruited from the Gregorio Marañón Hospital (Madrid, Spain). Patients participated in two experimental sessions comprising: 1) sensory stimulation of wrist flexors/extensors via thin-film multichannel intramuscular electrodes; and 2) surface stimulation of the nerves innervating the same target muscles. For each session, four of these patients underwent random 60-s trials of two stimulation strategies for each target muscle: 1) selective and adaptive timely stimulation (SATS) - based on EMG of the antagonist muscle; and 2) continuous stimulation (CON) of target muscles. Two patients underwent SATS stimulation trials alone while the other three underwent CON stimulation trials alone in each session. Kinematics of wrist, elbow, and shoulder, together with clinical scales, were used to assess tremor before, right after, and 24 h after each session. Intramuscular SATS achieved, on average, 32% acute (during stimulation) tremor reduction on each trial, while continuous stimulation augmented tremorgenic activity. Furthermore, tremor reduction was significantly higher using intramuscular than surface stimulation. Prolonged reduction of tremor amplitude (24 h after the experiment) was observed in four patients. These results showed acute and prolonged (24 h) tremor reduction using a minimally invasive neurostimulation technology based on SATS of primary sensory afferents of wrist muscles. This strategy might open the possibility of an alternative therapeutic approach for ET patients.

Wrist Angle Estimation With a Musculoskeletal Model Driven by Electrical Impedance Tomography Signals

Wrist kinematics estimation with muscle signals is a key issue in the field of wearable robots. In this study, we proposed a musculoskeletal-based-method driven by the Electrical Impedance Tomography (EIT) signals for continuously estimating wrist flexion/extension angles. The EIT-based interface can construct the conductivity distribution of the anatomical cross-sectional plane with a soft elastic sensing front-end, which is designed by our group. The estimation method took advantage of the flexor/extensor muscles' spatial information detected by the EIT-based interface to map the signals to the wrist angles. The whole model was designed with a musculoskeletal kinematic model, a muscular geometry model, and a mapping function between the EIT signals and the muscle morphological parameters. We validated the proposed method with intra-subject, inter-subject, and inter-posture cross-validations on 14 subjects in total. The results were compared with two data-driven algorithms (Lasso and kernel-based SVM). The muscle-model-based method was more robust to training data sizes than the other two methods. It achieved an average R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.97 with 1:10 intra-subject CV and 0.91 with 2:12 inter-subject CV. The model also quickly overcame the effects of posture changes with a short-time feature update. The results of our study are comparable, if not better, to that of state-of-the-art. Future endeavors are worth being paid in this direction to get more promising outcomes.

User-Defined Gestures for Mid-Air Interaction: A Comparison of Upper Limb Muscle Activity, Wrist Kinematics, and Subjective Preference

ABSTRACT Traditional gesture elicitation studies generally adopted the frequency ratio to select popular gestures among users. However, the chosen gestures were not always optimal and might pose a potential risk of musculoskeletal disorder under long-term use. The purpose of our research was to apply a novel assessment system combining the indices of electromyography (EMG), electrogoniometry, and subjective preference to the end-user elicitation experiment. In this study, we conducted a two-stage experiment to compare and analyze the results of the physiological and psychological measures for 33 candidate gestures of 16 given commands. Our results indicated that this assessment system could effectively determine the candidate gestures with lower physiological loads for all given commands except Maximize, and most of the selected gestures were in line with users’ mental models. Finally, we developed an optimal user-defined gesture set for the commands accommodated to human-computer interaction (HCI).

Optical motion capture accuracy is task-dependent in assessing wrist motion

Optical motion capture (OMC) systems are commonly used to capture in-vivo three-dimensional joint kinematics. However, the skin-based markers may not reflect the underlying bone movement, a source of error known as soft tissue artifact (STA). This study examined STA during wrist motion by evaluating the agreement between OMC and biplanar videoradiography (BVR). Nine subjects completed 7 different wrist motion tasks: doorknob rotation to capture supination and pronation, radial-ulnar deviation, flexion–extension, circumduction, hammering, and pitcher pouring. BVR and OMC captured the motion simultaneously. Wrist kinematics were quantified using helical motion parameters of rotation and translation, and Bland-Altman analysis quantified the mean difference (bias) and 95% limit of agreement (LOA). The rotational bias of doorknob pronation, a median bias of −4.9°, was significantly larger than the flexion–extension (0.7°, p < 0.05) and radial-ulnar deviation (1.8°, p < 0.01) tasks. The rotational LOA range was significantly smaller in the flexion–extension task (5.9°) compared to pitcher (11.6°, p < 0.05) and doorknob pronation (17.9°, p < 0.05) tasks. The translation bias did not differ between tasks. The translation LOA range was significantly larger in circumduction (9.8°) compared to the radial-ulnar deviation (6.3°, p < 0.05) and pitcher (3.4°, p < 0.05) tasks. While OMC technology has a wide-range of successful applications, we demonstrated it has relatively poor agreement with BVR in tracking wrist motion, and that the agreement depends on the nature and direction of wrist motion.

Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is limited in terms of task-dimensionality. Here, the potential application of higher-order tensor decomposition as a framework for proportional myoelectric control is demonstrated. A novel constrained Tucker decomposition (consTD) technique of synergy extraction is proposed for synergy-based myoelectric control model and compared with state-of-the-art matrix factorisation models. The extracted synergies were used to estimate control signals for the wrist's Degree of Freedom (DoF) through direct projection. The consTD model was able to estimate the control signals for each DoF by utilising all data in one 3rd-order tensor. This is contrast with matrix factorisation models where data are segmented for each DoF and then the synergies often have to be realigned. Moreover, the consTD method offers more information by providing additional shared synergies, unlike matrix factorisation methods. The extracted control signals were fed to a ridge regression to estimate the wrist's kinematics based on real glove data. The Coefficient of Determination (R2) for the reconstructed wrist position showed that the proposed consTD was higher than matrix factorisation methods. In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies.

Beyond Constant Curvature: A New Mechanics Model for Unidirectional Notched-Tube Continuum Wrists.

This paper presents a new mechanics model for unidirectional notched-tube continuum wrists, a class of mechanisms frequently used to implement distal steering in needle-sized surgical robotic instruments. Existing kinematic models available for these devices are based on the simplifying assumption that, during actuation, all the notches undergo the same amount of deflection, so that the shape of a wrist can be approximated by an arc of constant curvature. This approach is analytically attractive, but, as we show in this paper, it can sometimes fail to provide good tracking accuracy. In this article, we provide a new model that relaxes the assumption above, and we report experimental evidence showing its superior accuracy. We model wrist deflection using Castigliano's second theorem, with the addition of a capstan friction term that accounts for frictional losses on the actuation tendon. Because notched-tube wrists are typically made of Nickel-Titanium (Nitinol), which has nonlinear stress-strain characteristics, we use a technique to obtain a local linearized approximation of the material modulus, suitable for use in the deflection model. The result of our modeling is a system of nonlinear equations that can be solved numerically to predict the wrist configuration based on the applied actuation force. Experimental results on physical specimens show that this improved model provides a more accurate estimate of wrist kinematics than prior models assuming constant curvature bending.

The effect of four-corner fusion and proximal row carpectomy on uniplanar and multiplanar wrist motion: A biomechanical study

PurposeTo compare changes in wrist kinematics after scaphoidectomy and four-corner fusion (4CF), and proximal row carpectomy (PRC). MethodsSix cadaveric specimens underwent flexion-extension, radial-ulnar deviation and circumduction in an active motion wrist simulator. Native state, “anatomic 4CF”, “radial 4CF”, and PRC were compared. ResultsRadial 4CF reduced wrist extension, while PRC reduced radial deviation. Fusion groups had similar motion profiles. 44%, 41%, and 32% of native circumduction was maintained in PRC, anatomic, and radial 4CF. ConclusionsBoth fusion positions resulted in comparable motion outcomes. Anatomic 4CF was restricted in wrist extension compared to PRC but provided favourable radial deviation.

Elbow, wrist kinematics and shock transmission of backhand stroke in wheelchair tennis players

ABSTRACT The aim was to compare the differences in kinematics of elbow and wrist and shock transmission of the upper extremity in wheelchair tennis players and able-bodied players (in standing and sitting position) during backhand strokes. Fifteen wheelchair tennis and 15 able-bodied tennis players enrolled. Electromagnetic system and trial-axial accelerometers were used to measure the difference in the kinematic parameters of the upper extremity and the impact vibration transferred across the wrist joint. The results indicated that wheelchair players demonstrated unique elbow and wrist kinematics, especially shorter total swing time, greater elbow flexion at preparation, lower wrist extension acceleration before impact, and smaller racket vibration at impact. Comparing to able-bodied players in standing, wheelchair players and players in sitting demonstrated significantly greater elbow joint flexion/extension angle, angular velocity, angular acceleration during extension, and wrist joint flexion angle. Wheelchair players also differ significantly with the players in sitting regarding elbow joint angular velocity and acceleration, and wrist joint flexion velocity. These adaptations and adjustments can be attributed to the missing lower extremity function and deficient trunk kinetic chain. The differences between wheelchair tennis players and able-bodied players in sitting could represent the progress as the wheelchair players moving from novices to experts.

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

BackgroundProcessing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration.MethodsForearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session.ResultsMetric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (p<0.05) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen’s d) separating MRL from LDA ranging from left|dright|=0.62 to left|dright|=1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission.ConclusionsThe results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.

Driver Distraction Recognition Using Wearable IMU Sensor Data

Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.

Manipulation Planning with Soft Constraints by Randomized Exploration of the Composite Configuration Space

In this paper, an efficient and probabilistic complete planning algorithm called Composite-space RRT is presented to address motion planning with soft constraints for spherical wrist manipulators. Firstly, we propose a novel configuration space termed Composite Configuration Space (“Composite Space” for short), which is composed of the joint space and the task space. Then, collision-free paths are generated in the composite space by the Rapidly-exploring Random Trees (RRT) algorithm. Finally, the planned paths in the composite space are mapped into the corresponding joint-space paths by a local planner. As the analytical inverse kinematics (IK) of the spherical wrist is used in the local planner, the proposed Composite-space RRT algorithm is characterized by high efficiency and no numerical iteration. Moreover, this approach can effectively improve the smoothness of the end-effector orientation path. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage’s PR2 simulation platform with two typical orientation-constrained cases.

Inter-Subject Domain Adaptation for CNN-Based Wrist Kinematics Estimation Using sEMG.

Recently, convolutional neural network (CNN) has been widely investigated to decode human intentions using surface Electromyography (sEMG) signals. However, a pre-trained CNN model usually suffers from severe degradation when testing on a new individual, and this is mainly due to domain shift where characteristics of training and testing sEMG data differ substantially. To enhance inter-subject performances of CNN in the wrist kinematics estimation, we propose a novel regression scheme for supervised domain adaptation (SDA), based on which domain shift effects can be effectively reduced. Specifically, a two-stream CNN with shared weights is established to exploit source and target sEMG data simultaneously, such that domain-invariant features can be extracted. To tune CNN weights, both regression losses and a domain discrepancy loss are employed, where the former enable supervised learning and the latter minimizes distribution divergences between two domains. In this study, eight healthy subjects were recruited to perform wrist flexion-extension movements. Experiment results illustrated that the proposed regression SDA outperformed fine-tuning, a state-of-the-art transfer learning method, in both single-single and multiple-single scenarios of kinematics estimation. Unlike fine-tuning which suffers from catastrophic forgetting, regression SDA can maintain much better performances in original domains, which boosts the model reusability among multiple subjects.

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative, and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies, but conventional CNN can only exploit spatial correlations. Considering that the long short-term memory (LSTM) neural network is able to capture long-term and nonlinear dynamics of time-series data, in this article, we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. First, CNN is utilized to extract deep features from the sEMG spectrum, and then, these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intrasession and intersession evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM, and several representative machine learning approaches, particularly when complex wrist movements are activated.

The Effect of Forearm Position on Wrist Joint Biomechanics

All active motion wrist joint simulators have been designed to simulate physiologic wrist motion; however, a main difference among them is the orientation of the forearm (horizontal or vertical with respect to gravity). Moreover, the effect of forearm orientation on experimental results has yet to be quantified, but it may be an important variable. Thus, the purpose of this study was to determine the effect of forearm orientation on wrist kinematics and contact mechanics. Eight cadaveric upper limbs were cycled through a flexion-extension motion using an active motion wrist simulator. Motion trials were performed in 3 forearm orientations (gravity-neutral, gravity-flexion, and gravity-extension). A computed tomography-based joint congruency technique was used to examine radiocarpal joint contact and joint contact centroid translation in the 3 tested orientations. At full wrist extension and wrist flexion, radioscaphoid contact area was greatest in the gravity-extension orientation. Radiolunate contact area was similar among all 3 forearm orientations. The radioscaphoid contact centroid was consistent among the 3 tested positions with the wrist in neutral wrist position. In contrast, the radioscaphoid contact centroid translated radially in the gravity-neutral position relative to the gravity-flexion position in extreme extension. There were no differences in radiolunate centroid contact position in the 3 forearm orientations. This study demonstrates that forearm orientation affects contact mechanics and end-range carpal kinematics. Future biomechanical studies should report forearm orientation and discuss the implication of the forearm orientation used on the experimental results. This study provides evidence that the wrist joint is sensitive to forearm positions consistent with activities of daily living and rehabilitation protocols.

A Biomechanical Evaluation of the ECRL Tenodesis for Reconstruction of the Scapholunate Ligament

Reconstruction of the scapholunate ligament (SLL) in the setting of dynamic instability remains a surgical challenge, with lack of consensus on the best reconstructive procedure. Reconstruction of only the dorsal component may lead to volar gapping and abnormal wrist kinematics. This cadaveric active motion simulation study determined whether scapholunate (SL) motion, angulation, and contact are restored following open reconstruction using the extensor carpi radialis longus (ECRL) tenodesis, which reconstructs both the volar and the dorsal SLL components. Seven fresh-frozen cadaveric upper limbs (mean age, 68 ± 10.1 years) underwent a 4-stage protocol of cyclic dart-throw motion and flexion-extension motion (utilizing an active wrist motion simulator that used tendon load/motion-controlled actuation. Scaphoid and lunate motion, relative scaphoid translation, SL angle, and dorsal-volar SL diastasis were measured with (1) wrist ligaments intact, (2) following complete sectioning of the SLL, and (3) following SL reconstruction using the ECRL tenodesis technique. Complete SLL sectioning resulted in a typical pattern of SL instability. Following the ECRL tenodesis, lunate extension was not corrected. Scaphoid flexion, however, was not significantly different from the native state in FEM but remained significantly flexed during dart-throw motion. Differential dorsal and volar gapping did not significantly improve following ECRL tenodesis (dorsal, 1.2-2.3 mm; volar, 1.1-1.7 mm). This biomechanical study demonstrates that the ECRL tenodesis did not fully restore native carpal kinematics, despite dorsal and volar SLL, and scaphotrapeziotrapezoid reconstruction. Therapeutic IV.

EMG-based Estimation of Wrist Motion Using Polynomial Models.

Myoelectric control is a method of decoding the motor intent from the electromyogram (EMG) data and using the estimated intent to control prostheses and robots. This work investigates estimation of the wrist kinematics from EMG signals using polynomial models. Due to their low complexity, polynomial models are potentially the perfect choice for EMG-kinematics modeling. Ten ablebodied individuals participated in this study, where the EMG signals from the forearm and the wrist kinematics from the contralateral wrist were measured during mirrored contractions. Two sets of EMG features were employed including the time domain (TD) set, and TD features along with autoregressive coefficients (TDAR). Polynomial models of order 1 to 4 were applied to map the EMG signals to the wrist motions. The performance was directly compared to that of a multilayer perceptron (MLP) neural network. The estimation accuracy of the wrist kinematics improved with increasing the order of the model, but saturated at the 4th order. When using the TD set, the MLP significantly outperformed all polynomial models. However, when using the TDAR set, the polynomial models' performance improved so that the 4th order model performance was not significantly different than that of the MLP in two DoFs, although it was lower than MLP in one DoF. These results indicate that polynomial models are not as effective as more complex models such as neural networks, in learning the highly nonlinear mapping between the EMG data and motion intent. However, using a sufficiently high number of various EMG features, would reduce the mapping nonlinearities, and thereby may increase the polynomial models' performance to levels similar to those of complex black box models.

How Much Scaphoid Can be Safely Resected? A Biomechanical Analysis of the Effects of Distal Scaphoid Resection.

Resection of the distal pole of the scaphoid has been advocated as a simple alternative to other wrist salvage procedures for scaphoid nonunion advanced collapse and scaphotrapezio-trapezoid arthritis. However, the extent of scaphoid that may be resected without adversely affecting carpal kinematics has never been clearly defined. Seven cadaveric upper extremities were tested in a custom motion wrist simulator. A 3-stage sequential sectioning of the distal scaphoid protocol was performed in 25% increments then cyclic active wrist flexion-extension and dart thrower's motion trials were recorded. The extent of distal scaphoid resection had no effect on overall wrist range of motion. The lunate assumed a more extended position following resection of the distal scaphoid compared to intact. At 25%, 50%, and 75% of distal scaphoid resection, the lunate extended to 13.32° ± 9.4°, 23.43° ± 7.5°, and 15.81° ± 16.9°, respectively. The capitate migrated proximally with 25% and 50% distal scaphoidectomy, and proximally and radially with 75% of the scaphoid resected. Resection of 75% of the scaphoid resulted in unstable wrist kinematics. Resection of up to 25% of the distal scaphoid did not significantly influence carpal kinematics and induced mild lunate extension deformity. Resection of 50% of the scaphoid induced further and potentially clinically significant lunate extension and dorsal intercalated segment instability. Further removal of 75% of the distal scaphoid induced capitate migration radially and unpredictable wrist kinematics. Consequently, removal of over 25% of the scaphoid should be avoided or supplemented with partial wrist fusion.