Evaluation of hand motion capture protocol using static computed tomography images: application to an instrumented glove

A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks

OBJECTIVE

The purpose of this work was to develop an open-source musculoskeletal model of the hand and wrist and to evaluate its performance during simulations of functional tasks.

METHODS

The current model was developed by adapting and expanding upon existing models. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization was used to simulate maximum grip and pinch force. Active and passive hand opening were simulated to evaluate coordinated kinematic hand movements.

RESULTS

The model's maximum grip force production matched experimental measures of grip force, force distribution amongst the digits, and displayed sensitivity to wrist flexion. Simulated lateral pinch strength replicated in vivo palmar pinch strength data. Additionally, predicted activations for 7 of 8 muscles fell within variability of EMG data during palmar pinch. The active and passive hand opening simulations predicted reasonable activations and demonstrated passive motion mimicking tenodesis, respectively.

CONCLUSION

This work advances simulation capabilities of hand and wrist models and provides a foundation for future work to build upon.

SIGNIFICANCE

This is the first open-source musculoskeletal model of the hand and wrist to be implemented during both functional kinetic and kinematic tasks. We provide a novel simulation framework to predict maximal grip and pinch force which can be used to evaluate how potential surgical and rehabilitation interventions influence these functional outcomes while requiring minimal experimental data.

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.

Validity and Reliability of a Depth Camera-Based Quantitative Measurement for Joint Motion of the Hand

Purpose

Quantitative measurement of hand motion is essential in evaluating hand function. This study aimed to investigate the validity and reliability of a novel depth camera-based contactless automatic measurement system to assess hand range of motion and its potential benefits in clinical applications.

Methods

Five hand gestures were designed to evaluate the hand range of motion using a depth camera-based measurement system. Seventy-one volunteers were enrolled in performing the designed hand gestures. Then, the hand range of motion was measured with the depth camera and manual procedures. System validity was evaluated based on 3 dimensions: repeatability, within-laboratory precision, and reproducibility. For system reliability, linear evaluation, the intraclass correlation coefficient, paired t -test and bias were employed to test the consistency and difference between the depth camera and manual procedures.

Results

When measuring phalangeal length, repeatability, within-laboratory precision, and reproducibility were 2.63%, 12.87%, and 27.15%, respectively. When measuring angles of hand motion, the mean repeatability and within-laboratory precision were 1.2° and 3.3° for extension of 5 digits, 2.7° and 10.2° for flexion of 4 fingers, and 3.1° and 5.3° for abduction of 4 metacarpophalangeal joints, respectively. For system reliability, the results showed excellent consistency (intraclass correlation coefficient = 0.823; P < .05) and good linearity with the manual procedures (r = 0.909-0.982, approximately; P < .001). Besides, 78.3% of the measurements were clinically acceptable.

Conclusions

Our depth camera-based evaluation system provides acceptable validity and reliability in measuring hand range of motion and offers potential benefits for clinical care and research in hand surgery. However, further studies are required before clinical application.

Clinical relevance

This study suggests a depth camera-based contactless automatic measurement system holds promise for assessing hand range of motion in hand function evaluation, diagnosis, and rehabilitation for medical staff. However, it is currently not adequate for all clinical applications.

Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

This study assessed the accuracy of marker-based kinematic analysis of the fingers, considering soft tissue artefacts (STA) and marker imaging uncertainty. We collected CT images of the hand from healthy volunteers with fingers in full extension, mid- and full-flexion, including motion capture markers. Bones and markers were segmented and meshed. The bone meshes for each volunteer's scans were aligned using the proximal phalanx to study the proximal interphalangeal joint (PIP), and using the middle phalanx to study the distal interphalangeal joint (DIP). The angle changes between positions were extracted. The HAWK protocol was used to calculate PIP and DIP joint flexion angles in each position based on the marker centroids. Finally the marker locations were 'corrected' relative to the underlying bones, and the flexion angles recalculated. Static and dynamic marker imaging uncertainty was evaluated using a wand. A strong positive correlation was observed between marker- and CT-based joint angle changes with 0.980 and 0.892 regression slopes for PIP and DIP, respectively, and Root Mean Squared Errors below 4°. Notably for the PIP joint, correlation was worsened by STA correction. The 95% imaging uncertainty interval was < ± 1° for joints, and < ± 0.25 mm for segment lengths. In summary, the HAWK marker set's accuracy was characterised for finger joint flexion angle changes in a small group of healthy individuals and static poses, and was found to benefit from skin movements during flexion.

A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living

Linking hand kinematics and forearm muscle activity is a challenging and crucial problem for several domains, such as prosthetics, 3D modelling or rehabilitation. To advance in this relationship between hand kinematics and muscle activity, synchronised and well-defined data are needed. However, currently available datasets are scarce, and the presented tasks and data are often limited. This paper presents the KIN-MUS UJI Dataset that contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.

Assessment of hand function during activities of daily living using motion tracking cameras: A systematic review

The human hand is the most frequently used body part in activities of daily living. With its complex anatomical structure and the small size compared to the body, assessing the functional capability is highly challenging. The aim of this review was to provide a systematic overview on currently available 3D motion analysis based on skin markers for the assessment of hand function during activities of daily living. It is focused on methodology rather than results. A systematic review according to the PRISMA guidelines was performed. The systematic search yielded 1349 discrete articles. Of 147 articles included on basis of title, 123 were excluded after abstract review, and 24 were included in the full-text analysis with 13 key articles. There is still limited knowledge about hand and finger kinematics during activities of daily living. A standardization of the task is required in order to overcome the nonrepetitive nature and high variability of upper limb motion and ensure repeatability of task performance. To yield a progress in the analysis of human hand movements, an assessment of human kinematics including fingers, wrist, and thumb and an identification of relevant parameters that characterize a healthy motion pattern during functional tasks are needed.

Index finger and thumb kinematics and performance measurements for common touchscreen gestures

This study aimed to quantify differences in 7 touchscreen gestures. Eighteen participants performed index finger tapping, sliding in 4 orthogonal directions, and index finger and thumb pinch and stretch gestures on a touchscreen tablet computer. We hypothesized that two finger gestures would require longer task completion time and greater finger joint excursions than sliding gestures using only the index finger. We measured task completion times and finger joint kinematics. Tapping showed the fastest average (±SD) task completion time, 567(190) ms, of all gestures (p < 0.001). Pinch had faster task completion time, 765(277) ms, than all single-finger sliding gestures (p < 0.001). Stretch was faster to complete at 843(317) ms (p < 0.001) than all sliding gestures except slide right. Stretch demonstrated greater mean index finger metacarpophalangeal flexion/extension joint excursions, 63(16)°, compared to sliding gestures, 34(10)°, and tapping, 27(13)° (p < 0.01). Overall, two-finger gestures were faster to complete and showed greater joint excursions than single-finger sliding gestures.

Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes

Motion capture of all degrees of freedom of the hand collected during performance of daily living activities remains challenging. Instrumented gloves are an attractive option because of their higher ease of use. However, subject-specific calibration of gloves is lengthy and has limitations for individuals with disabilities. Here, a calibration procedure is presented, consisting in the recording of just a simple hand position so as to allow capture of the kinematics of 16 hand joints during daily life activities even in case of severe injured hands. 'across-subject gains' were obtained by averaging the gains obtained from a detailed subject-specific calibration involving 44 registrations that was repeated three times on multiple days to 6 subjects. In additional 4 subjects, joint angles that resulted from applying the 'across-subject calibration' or the subject-specific calibration were compared. Global errors associated with the 'across-subject calibration' relative to the detailed, subject-specific protocol were small (bias: 0.49°; precision: 4.45°) and comparable to those that resulted from repeating the detailed protocol with the same subject on multiple days (0.36°; 3.50°). Furthermore, in one subject, performance of the 'across-subject calibration' was directly compared to another fast calibration method, expressed relative to a videogrammetric protocol as a gold-standard, yielding better results.

Real-time simulation of hand motion for prosthesis control

Individuals with hand amputation suffer substantial loss of independence. Performance of sophisticated prostheses is limited by the ability to control them. To achieve natural and simultaneous control of all wrist and hand motions, we propose to use real-time biomechanical simulation to map between residual EMG and motions of the intact hand. Here we describe a musculoskeletal model of the hand using only extrinsic muscles to determine whether real-time performance is possible. Simulation is 1.3 times faster than real time, but the model is locally unstable. Methods are discussed to increase stability and make this approach suitable for prosthesis control.