Clinical validation of automated depth camera-based measurement of the Fugl-Meyer assessment for upper extremity.

OBJECTIVE: Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. DESIGN: Validation and feasibility study of a measurement instrument based on single cross-sectional data. SETTING: Rehabilitation unit in a general hospital. PARTICIPANTS: Ninety-five patients with hemiparesis admitted for inpatient rehabilitation unit (2021-2023). MAIN MEASURES: Scores for each item, excluding those related to reflexes, were computed utilizing machine learning models trained on participant videos and readouts from force test devices, while the remaining reflex scores were derived through regression algorithms. Concurrent criterion validity was evaluated using sensitivity, specificity, percent agreement and Cohen's Kappa coefficient for ordinal scores of individual items, as well as correlations and intraclass correlation coefficients for total scores. Video-based manual assessment was also conducted and compared to the automated tools. RESULT: The majority of patients completed the assessment without therapist intervention. The automated scoring models demonstrated superior validity compared to video-based manual assessment across most items. The total scores derived from the automated assessment exhibited a high coefficient of 0.960. However, the validity of force test items utilizing force sensing resistors was relatively low. CONCLUSION: The integration of depth camera technology and machine learning models for automated Fugl-Meyer Assessment demonstrated acceptable validity and feasibility, suggesting its potential as a valuable tool in rehabilitation assessment.

Anatomical variations of the brachial plexus in adult cadavers: A descriptive study and clinical significance.

BACKGROUND: Brachial plexus injury is recognized as one of the most severe clinical challenges due to the complex anatomical configuration of the brachial plexus and its propensity for variation, which complicates safe clinical interventions. This study aimed to ascertain the prevalence and characterize the types of brachial plexus variations, and to elucidate their clinical implications. MATERIALS AND METHODS: We conducted meticulous dissections of 60 formalin-fixed cadavers' upper arm, axilla and lower neck to reveal and assess the roots, trunks, divisions, cords, and branches of the brachial plexus. The pattern of branching was noted by groups of dissecting medical students and confirmed by the senior anatomists. The variations discovered were record and photographed using a digital camera for further analysis. RESULTS: Variations in the brachial plexus were identified in 40 of the 60 cadavers, yielding a prevalence rate of 66.7%. These variations were classified into root anomalies (2.1%), trunk anomalies (8.5%), division anomalies (2.1%), and cord anomalies (4.3%). Notably, anomalies in communicating branches were observed in 39 cadavers (83.0%): 14 with bilateral anomalies, 14 with anomalies on the left side, and 11 on the right side. These communicating branches formed connections between the roots and other segments, including trunks, cords, and terminal nerves, and involved the median, musculocutaneous, and ulnar nerves. CONCLUSION: The frequency and diversity of brachial plexus variations, particularly in communicating branches, are significant in cadavers. It is imperative that these variations are carefully considered during the diagnostic process, treatment planning, and prior to procedures such as supraclavicular brachial plexus blocks and nerve transfers, to mitigate the risk of iatrogenic complications.

Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data.

Immunotherapy has improved the prognosis of patients with advanced non-small cell lung cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC. We retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, a combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic curve. Survival analysis was performed to determine the difference in progression-free survival (PFS) between the two groups with the prediction label generated by the combined model. The radiomic model based on the combination of precontrast and postcontrast CT radiomic features and the clinical model produced an AUC of 0.92±0.04 and 0.89±0.03, respectively. By integrating radiomic and clinical features together, the combined model had the best performance with an AUC of 0.94±0.02. The survival analysis showed that the two groups had significantly different PFS times (p<0.0001). The baseline multidimensional data including CT radiomic and multiple clinical features were valuable in predicting the efficacy of ICIs monotherapy in patients with advanced NSCLC.

Reliability of a human pose tracking algorithm for measuring upper limb joints: comparison with photography-based goniometry.

BACKGROUND: Range of motion (ROM) measurements are essential for diagnosing and evaluating upper extremity conditions. Clinical goniometry is the most commonly used methods but it is time-consuming and skill-demanding. Recent advances in human tracking algorithm suggest potential for automatic angle measuring from RGB images. It provides an attractive alternative for at-distance measuring. However, the reliability of this method has not been fully established. The purpose of this study is to evaluate if the results of algorithm are as reliable as human raters in upper limb movements. METHODS: Thirty healthy young adults (20 males, 10 females) participated in this study. Participants were asked to performed a 6-motion task including movement of shoulder, elbow and wrist. Images of movements were captured by commercial digital cameras. Each movement was measured by a pose tracking algorithm (OpenPose) and compared with the surgeon-measurement results. The mean differences between the two measurements were compared. Pearson correlation coefficients were used to determine the relationship. Reliability was investigated by the intra-class correlation coefficients. RESULTS: Comparing this algorithm-based method with manual measurement, the mean differences were less than 3 degrees in 5 motions (shoulder abduction: 0.51; shoulder elevation: 2.87; elbow flexion:0.38; elbow extension:0.65; wrist extension: 0.78) except wrist flexion. All the intra-class correlation coefficients were larger than 0.60. The Pearson coefficients also showed high correlations between the two measurements (p < 0.001). CONCLUSIONS: Our results indicated that pose estimation is a reliable method to measure the shoulder and elbow angles, supporting RGB images for measuring joint ROM. Our results presented the possibility that patients can assess their ROM by photos taken by a digital camera. TRIAL REGISTRATION: This study was registered in the Clinical Trials Center of The First Affiliated Hospital, Sun Yat-sen University (2021-387).

Finite-Time Interactive Control of Robots with Multiple Interaction Modes.

This paper proposes a finite-time multi-modal robotic control strategy for physical human-robot interaction. The proposed multi-modal controller consists of a modified super-twisting-based finite-time control term that is designed in each interaction mode and a continuity-guaranteed control term. The finite-time control term guarantees finite-time achievement of the desired impedance dynamics in active interaction mode (AIM), makes the tracking error of the reference trajectory converge to zero in finite time in passive interaction mode (PIM), and also guarantees robotic motion stop in finite time in safety-stop mode (SSM). Meanwhile, the continuity-guaranteed control term guarantees control input continuity and steady interaction modes transition. The finite-time closed-loop control stability and the control effectiveness is validated by Lyapunov-based theoretical analysis and simulations on a robot manipulator.

Adaptive Interaction Control of Compliant Robots Using Impedance Learning.

This paper presents an impedance learning-based adaptive control strategy for series elastic actuator (SEA)-driven compliant robots without the measurement of the robot-environment interaction force. The adaptive controller is designed based on the command filter-based adaptive backstepping approach, where a command filter is used to decrease computational complexity and avoid the requirement of high derivatives of the robot position. In the controller, environmental impedance profiles and robotic parameter uncertainties are estimated using adaptive learning laws. Through a Lyapunov-based theoretical analysis, the tracking error and estimation errors are proven to be semiglobally uniformly ultimately bounded. The control effectiveness is illustrated through simulations on a compliant robot arm.

Functional outcome of contralateral C7 nerve transfer combined with free functional gracilis transplantation to repair total brachial plexus avulsion: a report of thirty-nine cases.

PURPOSE: Treatment of total brachial plexus avulsion (TBPA) is a challenge in the clinic, especially the restoration of hand function. The current main surgical order is from proximal to distal joints. The purpose of this study was to demonstrate the outcomes of "distal to proximal" surgical method. METHODS: Thirty-nine patients underwent contralateral C7 (CC7) nerve transfer to directly repair the lower trunk (CC7-LT) and phrenic nerve transfer to the suprascapular nerve (PN-SSN) during the first stage, followed by free functional gracilis transplantation (FFGT) for elbow flexion and finger extension. Muscle strength of upper limb, degree of shoulder abduction and elbow flexion, and Semmes-Weinstein monofilament test and static two-point discrimination of the hand were examined according to the modified British Medical Research Council (mBMRC) scoring system. RESULTS: The results showed that motor recovery reached a level of M3 + or greater in 66.7% of patients for shoulder abduction, 87.2% of patients for elbow flexion, 48.7% of patients for finger extension, and 25.6% of patients for finger flexion. The mean shoulder abduction angle was 45.5° (range 0-90°), and the average elbow flexion angle was 107.2° (range 0-142°), with 2.5 kg average flexion strength (range 0.5-5 kg). In addition, protective sensibility (≥ S2) was found to be achieved in 71.8% of patients. CONCLUSION: In reconstruction of TBPA, CC7 transfer combined with free functional gracilis transplantation is an available treatment method. It could help patients regain shoulder joint stability and the function of elbow flexion and finger extension and, more importantly, provide finger sensation and partial finger flexion function. However, the pick-up function was unsatisfied, which needed additional surgery.

Local Riluzole Release from a Thermosensitive Hydrogel Rescues Injured Motoneurons through Nerve Root Stumps in a Brachial Plexus Injury Rat Model.

The C5-C6 nerve roots are usually spared from avulsion after brachial plexus injury (BPI) and can thus be used as donors for nerve repair. A BPI rat model with C5-C6 nerve root stumps has been established in our previous work. The aim of this study was to test whether riluzole loaded into a thermosensitive hydrogel could applied locally in the nerve root stumps of this BPI rat model, thus increasing the reparative effect of the nerve root stumps. Nile red (a hydrophobic dye) was used as a substitute for riluzole since riluzole itself does not emit light. Nile red, loaded into a thermosensitive hydrogel, was added to the nerve root stumps of the BPI rat model. Additionally, eighteen rats, with operation on right brachial plexus, were evenly divided into three groups: control (Con), thermosensitive hydrogel (Gel) and thermosensitive hydrogel loaded with riluzole (Gel + Ri) groups. Direct nerve repair was performed after local riluzole release for two weeks. Functional and electrophysiological evaluations and histological assessments were used to evaluate the reparative effect 8 weeks after nerve repair. Nile red was slowly released from the thermosensitive hydrogel and retrograde transport through the nerve root stumps to the motoneurons, according to immunofluorescence. Discernible functional recovery began earlier in the Gel + Ri group. The compound muscle action potential, ChAT-expressing motoneurons, positivity for neurofilaments and S100, diameter of regenerating axons, myelin sheath thickness and density of myelinated fibers were markedly increased in the Gel + Ri group compared with the Con and Gel groups. Our results indicate that the local administration of riluzole could undergo retrograde transportation through C5-C6 nerve root stumps, thereby promoting neuroprotection and increasing nerve regeneration.

Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton.

Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r2 values were higher than those of GP.

Construction of tissue-engineered lymphatic vessel using human adipose derived stem cells differentiated lymphatic endothelial like cells and decellularized arterial scaffold: A preliminary study.

We have previously demonstrated that human adipose-derived stem cells (hADSCs) can be differentiated into lymphatic endothelial like cells. The purpose of this study was to investigate the feasibility of utilizing the induced lymphatic endothelial like cells and decellularized arterial scaffold to construct the tissue-engineered lymphatic vessel. The hADSCs were isolated from adipose tissue in healthy adults and were characterized the multilineage differentiation potential. Decellularized arterial scaffold was prepared using the Triton x-100 method. ADSCs were differentiated into lymphatic-like endothelial cells, and the induced cells were then seeded onto the decellularized arterial scaffold to engineer the lymphatic vessel. The histological analyses were performed to examine the endothelialized construct. The decellularized arterial scaffold was successfully obtained and was able to maintain its vessel morphology. The isolated ADSCs can be differentiated into osteocytes and adipocytes. After seeding onto the scaffold, the seeded cells attached and grew well on the decellularized arterial scaffold. Our preliminary results demonstrated that the induced lymphatic endothelial like cells combined with decellularized arterial scaffold could be utilized to successfully engineer the lymphatic vessel. Our findings may be helpful for the development of tissue-engineering of the lymphatic graft.

Microanatomy of the Separable Length of the C7.

OBJECTIVE: The objective of this study was to provide anatomical data on modified contralateral C7 (cC7) nerve root transfers by dissecting and measuring the separable lengths of the C7 root, trunk, and divisions. MATERIALS AND METHODS: Fifteen adult cervicothoracic specimens were dissected and measured using Vernier calipers after exposing the brachial plexus. Measurements included the length of the C7 from the root to the trunk, the lengths of the C7 root-trunk-anterior division (and posterior division). The epineuria at the C7 root-division-cord junctions were opened until the internal nerve bundles fused together and could not be separated by microdissection. The lengths of the C7 root-trunk-anterior (and posterior) division were measured again after microdissection. The lengths of cC7 nerve of 20 patients with bracial plexus avulsion were measured using the former technique. RESULTS: The length of the C7 root-trunk was 45.87 SD 10.43 mm. Before separation, the lengths of the C7 root-trunk-anterior division and the root-trunk-posterior division were 61.14 SD 13.44 and 54.63 SD 11.35 mm, respectively; after separation, the lengths were 74.67 SD 12.86 and 68.73 SD 11.86 mm, respectively. The prolonged lengths were 13.15 SD 4.26 and 14.21 SD 6.98 mm, respectively. The prolonged lengths were significantly greater (p < 0.05). The prolonged length of C7 nerve clinically was anterior division, 15.30 SD 3.76 mm and posterior division, 11.10 SD 3.01 mm. CONCLUSION: The C7 division lengths can be prolonged by dissecting the epineuria at the division-cord junction of the C7 nerve root.

Diagnostic accuracy of three sensory tests for diagnosis of sensory disturbances.

BACKGROUND: Clinical diagnosis of sensory disturbances is extremely challenging, partly because the utility of sensory tests is questionable. Transection of C7 nerve root provides an objective assessment model to determine the diagnostic accuracy of sensory tests. The purpose of this study was to investigate the diagnostic accuracy of Semmes-Weinstein Monofilaments (SWM; Sammons Preston, Bolingbrook, IL), static two-point discrimination (s2PD), and current perception threshold (CPT) tests in patients with contralateral C7 nerve root transfer. METHODS: The contralateral index finger of patients was tested preoperatively and 6 weeks postoperatively, including the Revised Short-Form McGill Pain Questionnaire (SF-MPQ-2), SWM, s2PD, and CPT tests. The SF-MPQ-2 was used as a gold standard for sensory disturbances. RESULTS: A total of 38 patients were included in the study. The likelihood probability ratio of SWM and CPT tests generated small shifts in probability, whereas the s2PD test did not show any capacity to detect sensory disturbances. The areas under the curves (AUC) for SWM and CPT tests were 0.724 and 0.697, respectively. Based on different positive test standards, the AUCs for s2PD (≥ 6 mm) and s2PD (≥ 7 mm) tests were 0.632 and 0.658, respectively. When CPT and SWM tests were combined as a measure, the AUC increased to 0.763. CONCLUSION: The SWM and CPT tests show a small capacity to detect sensory disturbances, whereas the s2PD test does not show any diagnostic capacity. To better understand sensory disturbances, a comprehensive testing protocol including valid measures of physical impairment and symptom-specific measurement tools should be adopted.

Modified pathological classification of brachial plexus root injury and its MR imaging characteristics.

The authors described a modified pathological classification (PC) of brachial plexus injury (BPI) and its magnetic resonance (MR) imaging characteristics. The reliability and diagnostic accuracy of MR imaging for detecting nerve injury was discussed. Between 2006 and 2010, 86 patients with BPI were managed surgically in our department. Their preoperative MR images and surgical findings were analyzed retrospectively. The PC of BPI was classified into five types: (I) nerve root injury in continuity (including Sunderland grade I-IV injury); (II) postganglionic spinal nerve rupture with or without proximal stump; (III) preganglionic root injury (visible); (IV) preganglionic nerve root injury and postganglionic spinal nerves injury; (V) preganglionic root injury (invisible). The main MR imaging characteristics of BPI included traumatic meningocele, displacement of spinal cord, the absence of nerve root, "Black line" sign, nerve root/trunk injury in continuity, and thickening and edema of nerve root. The accuracy of MR imaging for detecting C5, C6, C7, C8, and T1 nerve roots injury were 93.3, 95.2, 92.3, 84, and 74.4%, respectively. The modified PC provides a detailed description of nerve root injury in BPI, and MR imaging technique is a reliable method for detecting nerve root injury.

Microbiota-accessible fiber activates short-chain fatty acid and bile acid metabolism to improve intestinal mucus barrier in broiler chickens.

IMPORTANCE: The intestinal mucus barrier, located at the interface of the intestinal epithelium and the microbiota, is the first line of defense against pathogenic microorganisms and environmental antigens. Dietary polysaccharides, which act as microbiota-accessible fiber, play a key role in the regulation of intestinal microbial communities. However, the mechanism via which dietary fiber affects the intestinal mucus barrier through targeted regulation of the gut microbiota is not clear. This study provides fundamental evidence for the benefits of dietary fiber supplementation in broiler chickens through improvement in the intestinal mucus barrier by targeted regulation of the gut ecosystem. Our findings suggest that the microbiota-accessible fiber-gut microbiota-short-chain fatty acid/bile acid axis plays a key role in regulating intestinal function.

Deciphering the nutritional strategies for polysaccharides effects on intestinal barrier in broilers: Selectively promote microbial ecosystems.

The gut microbiota, a complex and dynamic microbial ecosystem, plays a crucial role in regulating the intestinal barrier. Polysaccharide foraging is specifically dedicated to establishing and maintaining microbial communities, contributing to the shaping of the intestinal ecosystem and ultimately enhancing the integrity of the intestinal barrier. The utilization and regulation of individual polysaccharides often rely on distinct gut-colonizing bacteria. The products of their metabolism not only benefit the formation of the ecosystem but also facilitate cross-feeding partnerships. In this review, we elucidate the mechanisms by which specific bacteria degrade polysaccharides, and how polysaccharide metabolism shapes the microbial ecosystem through cross-feeding. Furthermore, we explore how selectively promoting microbial ecosystems and their metabolites contributes to improvements in the integrity of the intestinal barrier.

Spatial hybrid adaptive impedance learning control for robots in repetitive interactive tasks.

The existing model-based impedance learning control methods can provide variable impedance regulation for physical human-robot interaction (PHRI) in repetitive tasks without interactive force sensing, however, these methods require the completion of the repetitive tasks with constant time, which restricts their applications. For PHRI in repetitive tasks with different completion time, this paper proposes a spatial hybrid adaptive impedance learning control (SHAILC) strategy by using the spatial periodic characteristics of the tasks. In the spatial hybrid adaptation, spatial periodic adaptation is used for estimating time-varying human impedance and differential adaptation is designed for estimating robotic constant unknown parameters. The use of deadzone modifications in hybrid adaptation maintains the accuracy of the parameter estimation when the tracking error is small relative to the modeling error. The control stability is analyzed by a Lyapunov-based analysis in the spatial domain, and the control effectiveness and superiority is illustrated on a parallel robot in repetitive tasks with different task completion time.

Joint torques estimation in human gait based on Gaussian process.

BACKGROUND: Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. OBJECTIVE: The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. METHOD: The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs. RESULTS: The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s). CONCLUSION: The results show that it is possible to estimate the joint torques at different scenarios.

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration-dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand.

BACKGROUND: Telemedicine support virtual consultations and evaluations in hand surgery for patients in remote areas during the COVID-19 era. However, traditional physical examination is challenging in telemedicine and it is inconvenient to manually measure the hand range of motion (ROM) from images or videos. Here, we propose an automatic method using the hand pose estimation technique, aiming to measure the hand ROM from smartphone images. METHODS: Twenty-eight healthy volunteers participated in the study. An eight-hand gestures measurement protocol and the Google MediaPipe Hands were used to analyze images and calculate the ROM automatically. Manual goniometry was also performed according to the guideline of the American Medical Association. The correlation between the automatic and manual methods was analyzed by the intraclass correlation coefficient and Pearson correlation coefficient. The clinical acceptance was testified using Bland-Altman plots. RESULTS: A total of 32 parameters of each hand were measured by both methods, and 1792 measurement results were compared. The mean difference between automatic and manual methods is -2.21 ± 9.29° in the angle measurement and 0.48 ± 0.48 cm in the distance measurement. The intraclass correlation coefficient of 75% of parameters was higher than 0.75, the Pearson correlation coefficient of 84% of parameters was over 0.6, and 40.6% of parameters reached well-accepted clinical agreements. CONCLUSIONS: The proposed method provides a helpful protocol for automatic hand ROM measurement based on smartphone images and the MediaPipe Hands pose estimation technique. The automatic measurement is acceptable and comparable with existing methods, showing a possible application in the telemedicine examination of hand surgery.

Precision and accuracy of measuring finger motion with a depth camera: a cross-sectional study of healthy participants.

The purpose of this cross-sectional study was to determine the precision and accuracy of the measurement of finger motion with a depth camera. Fifty-five healthy adult hands were included. Measurements were done with a depth camera and compared with traditional manual goniometer measurements. Repeated measuring showed that the overall repeatability and reproducibility of extension measured with the depth camera were within 3° and 4° and that of flexion were within 13° and 14°. Compared with traditional manual goniometry, biases of extension of all finger joints and flexion of metacarpophalangeal joints were less than 5°, and the average bias of flexion of proximal and distal interphalangeal joints was 29°. We conclude that the measurement of finger extension and flexion of the metacarpophalangeal joints with a depth camera was reliable, but improvement is required in the precision and accuracy of interphalangeal joint flexion.