IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues.

Wearable sensing using inertial measurement units (IMUs) is enabling portable and customized gait retraining for knee osteoarthritis. However, the vibrotactile feedback that users receive directly depends on the accuracy of IMU-based kinematics. This study investigated how kinematic errors impact an individual's ability to learn a therapeutic gait using vibrotactile cues. Sensor accuracy was computed by comparing the IMU-based foot progression angle to marker-based motion capture, which was used as ground truth. Thirty subjects were randomized into three groups to learn a toe-in gait: one group received vibrotactile feedback during gait retraining in the laboratory, another received feedback outdoors, and the control group received only verbal instruction and proceeded directly to the evaluation condition. All subjects were evaluated on their ability to maintain the learned gait in a new outdoor environment. We found that subjects with high tracking errors exhibited more incorrect responses to vibrotactile cues and slower learning rates than subjects with low tracking errors. Subjects with low tracking errors outperformed the control group in the evaluation condition, whereas those with higher error did not. Errors were correlated with foot size and angle magnitude, which may indicate a non-random bias. The accuracy of IMU-based kinematics has a cascading effect on feedback; ignoring this effect could lead researchers or clinicians to erroneously classify a patient as a non-responder if they did not improve after retraining. To use patient and clinician time effectively, future implementation of portable gait retraining will require assessment across a diverse range of patients.

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables.

OBJECTIVE: Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. METHODS: We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. RESULTS: Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. CONCLUSIONS: Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

Fusion of video and inertial sensing data via dynamic optimization of a biomechanical model.

Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model significantly improved estimation of lower-extremity kinematics over the video-only approach and estimation of joint centers over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved estimation of joint kinematics and joint centers over unconstrained fusion, while unconstrained fusion was advantageous in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.

Markerless Motion Tracking With Noisy Video and IMU Data.

OBJECTIVE: Marker-based motion capture, considered the gold standard in human motion analysis, is expensive and requires trained personnel. Advances in inertial sensing and computer vision offer new opportunities to obtain research-grade assessments in clinics and natural environments. A challenge that discourages clinical adoption, however, is the need for careful sensor-to-body alignment, which slows the data collection process in clinics and is prone to errors when patients take the sensors home. METHODS: We propose deep learning models to estimate human movement with noisy data from videos (VideoNet), inertial sensors (IMUNet), and a combination of the two (FusionNet), obviating the need for careful calibration. The video and inertial sensing data used to train the models were generated synthetically from a marker-based motion capture dataset of a broad range of activities and augmented to account for sensor-misplacement and camera-occlusion errors. The models were tested using real data that included walking, jogging, squatting, sit-to-stand, and other activities. RESULTS: On calibrated data, IMUNet was as accurate as state-of-the-art models, while VideoNet and FusionNet reduced mean ± std root-mean-squared errors by 7.6 ± 5.4 ° and 5.9 ± 3.3 °, respectively. Importantly, all the newly proposed models were less sensitive to noise than existing approaches, reducing errors by up to 14.0 ± 5.3 ° for sensor-misplacement errors of up to 30.0 ± 13.7 ° and by up to 7.4 ± 5.5 ° for joint-center-estimation errors of up to 101.1 ± 11.2 mm, across joints. CONCLUSION: These tools offer clinicians and patients the opportunity to estimate movement with research-grade accuracy, without the need for time-consuming calibration steps or the high costs associated with commercial products such as Theia3D or Xsens, helping democratize the diagnosis, prognosis, and treatment of neuromusculoskeletal conditions.

Predicting knee adduction moment response to gait retraining with minimal clinical data.

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a regression model that uses minimal clinical data-a set of six features easily obtained in the clinic-to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.

Sensor-Based Evaluation of Physical Therapy Exercises.

Physical therapy is important for the treatment and prevention of musculoskeletal injuries, as well as recovery from surgery. In this paper, we explore techniques for automatically determining whether an exercise was performed correctly or not, based on camera images and wearable sensors. Classifiers were tested on data collected from 30 patients during normally-scheduled physical therapy appointments. We considered two lower limb exercises, and asked how well classifiers could generalize to the assessment of individuals for whom no prior data were available. We found that our classifiers performed well relative to several metrics (mean accuracy: 0.76, specificity: 0.90), but often returned low sensitivity (mean: 0.34). For one of the two exercises considered, these classifiers compared favorably with human performance.

American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis.

The field of biomechanics is at a turning point, with marker-based motion capture set to be replaced by portable and inexpensive hardware, rapidly improving markerless tracking algorithms, and open datasets that will turn these new technologies into field-wide team projects. Despite progress, several challenges inhibit both inertial and vision-based motion tracking from reaching the high accuracies that many biomechanics applications require. Their complementary strengths, however, could be harnessed toward better solutions than those offered by either modality alone. The drift from inertial measurement units (IMUs) could be corrected by video data, while occlusions in videos could be corrected by inertial data. To expedite progress in this direction, we have collected the CMU Panoptic Dataset 2.0, which contains 86 subjects captured with 140 VGA cameras, 31 HD cameras, and 15 IMUs, performing on average 6.5 min of activities, including range of motion activities and tasks of daily living. To estimate ground-truth kinematics, we imposed simultaneous consistency with the video and IMU data. Three-dimensional joint centers were first computed by geometrically triangulating proposals from a convolutional neural network applied to each video independently. A statistical meshed model parametrized in terms of body shape and pose was then fit through a top-down optimization approach that enforced consistency with both the video-based joint centers and IMU data. As proof of concept, we used this dataset to benchmark pose estimation from a sparse set of sensors, showing that incorporation of complementary modalities is a promising frontier that can be further strengthened through physics-informed frameworks.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

OBJECTIVE: We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. DESIGN: We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. RESULTS: Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. CONCLUSIONS: Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries.

Importance: Implant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry. Objectives: To quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States. Design, Setting, and Participants: Retrospective cohort study of a large EHR of veterans who had 45 351 total hip arthroplasty procedures in Veterans Health Administration hospitals from 2000 to 2017. Data analysis was performed from January 1, 2000, to December 31, 2017. Exposures: Total hip arthroplasty. Main Outcomes and Measures: Number of total hip arthroplasty procedures extracted from the EHR, trends in implant use, and relative survivorship of implants. Results: A total of 45 351 total hip arthroplasty procedures were identified from 2000 to 2017 with 192 805 implant parts. Data completeness improved over the time. After 2014, 85% of prosthetic heads, 91% of shells, 81% of stems, and 85% of liners used in the Veterans Health Administration health care system were identified by part number. Revision burden and trends in metal vs ceramic prosthetic femoral head use were found to reflect data from the American Joint Replacement Registry. Recalled implants were obvious negative outliers in implant survivorship using Kaplan-Meier curves. Conclusions and Relevance: Although loss to follow-up remains a challenge that requires additional attention to improve the quantitative nature of calculated implant survivorship, we conclude that data collected during routine clinical care and stored in the EHR of a large health system over 18 years were sufficient to provide clinically meaningful data on trends in implant use and to identify poor implants that were subsequently recalled. This automated approach was low cost and had no reporting burden. This low-cost, low-overhead method to assess implant use and performance within a large health care setting may be useful to internal quality assurance programs and, on a larger scale, to postmarket surveillance of implant performance.

Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework.

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27° (±0.38°) in flexion/extension, less than 2.52° (±0.98°) in ad/abduction, and less than 3.34° (±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer new perspective into disease progression, patient recovery, and sports biomechanics.

Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks.

PURPOSE: To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists. MATERIALS AND METHODS: Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades. RESULTS: With committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions. CONCLUSION: An end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. Supplemental material is available for this article. © RSNA, 2020.

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.

Changes in Local Bone Density in Early Thumb Carpometacarpal Joint Osteoarthritis.

PURPOSE: Thumb carpometacarpal (CMC) osteoarthritis (OA) represents a major source of functional morbidity. The effects of early CMC OA on loading and use patterns potentially lead to changes in local bone density and microarchitecture. Hounsfield units (HU), a quantitative attenuation coefficient obtained from computed tomography (CT) scans, have been shown to be a reliable marker of bone density. We hypothesized that early CMC OA is associated with lower local bone density about the CMC joint as assessed by HU. METHODS: We examined HU units from CT scans in 23 asymptomatic subjects and 91 patients with early CMC OA. The HU measurements were obtained within cancellous portions of the trapezium, capitate, first and third metacarpal bases, and distal radius. Linear regression models, with age and sex included as covariates, were used to assess the relationship between CMC OA and HU values at each anatomical site. RESULTS: Early OA patients had significantly lower HU than asymptomatic subjects within the trapezium (mean, 377 HU vs 436 HU) and first metacarpal bases (265 HU vs 324 HU). No significant group differences were noted at the capitate, third metacarpal, or distal radius. Male sex and younger age were associated with significantly higher HU at all the anatomical sites, except the first metacarpal base, where age had no significant effect. CONCLUSIONS: Subjects presenting with early CMC OA had significantly lower bone density as assessed with HU at the thumb CMC joint (trapezium and first metacarpal base). Early thumb CMC OA and discomfort may lead to diminished loading across the basal joint, producing focal disuse osteopenia. These findings in symptomatic early arthritis suggest a relationship between symptoms, functional use of the CMC joint, and local bone density. TYPE OF STUDY/LEVEL OF EVIDENCE: Diagnostic II.

ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information.

In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data. However, current methods do not jointly optimize over structured covariates and time series in the feature extraction process. We present ShortFuse, a method that boosts the accuracy of deep learning models for time series by explicitly modeling temporal interactions and dependencies with structured covariates. ShortFuse introduces hybrid convolutional and LSTM cells that incorporate the covariates via weights that are shared across the temporal domain. ShortFuse outperforms competing models by 3% on two biomedical applications, forecasting osteoarthritis-related cartilage degeneration and predicting surgical outcomes for cerebral palsy patients, matching or exceeding the accuracy of models that use features engineered by domain experts.

Older asymptomatic women exhibit patterns of thumb carpometacarpal joint space narrowing that precede changes associated with early osteoarthritis.

In small joints, where cartilage is difficult to image and quantify directly, three-dimensional joint space measures can be used to gain insight into potential joint pathomechanics. Since the female sex and older age are risk factors for carpometacarpal (CMC) joint osteoarthritis (OA), the purpose of this in vivo computed tomography (CT) study was to determine if there are any differences with sex, age, and early OA in the CMC joint space. The thumbs of 66 healthy subjects and 81 patients with early stage CMC OA were scanned in four range-of-motion, three functional-task, and one neutral positions. Subchondral bone-to-bone distances across the trapezial and metacarpal articular surfaces were computed for all the positions. The joint space area, defined as the articular surface that is less than 1.5mm from the mating bone, was used to assess joint space. A larger joint space area typically corresponds to closer articular surfaces, and therefore a narrower joint space. We found that the joint space areas are not significantly different between healthy young men and women. Trends indicated that patients with early stage OA have larger CMC joint space areas than healthy subjects of the same age group and that older healthy women have larger joint space areas than younger healthy women. This study suggests that aging in women may lead to joint space narrowing patterns that precede early OA, which is a compelling new insight into the pathological processes that make CMC OA endemic to women.

The Envelope of Physiological Motion of the First Carpometacarpal Joint.

Much of the hand's functional capacity is due to the versatility of the motions at the thumb carpometacarpal (CMC) joint, which are presently incompletely defined. The aim of this study was to develop a mathematical model to completely describe the envelope of physiological motion of the thumb CMC joint and then to examine if there were differences in the kinematic envelope between women and men. In vivo kinematics of the first metacarpal with respect to the trapezium were computed from computed tomography (CT) volume images of 44 subjects (20M, 24F, 40.3 ± 17.7 yr) with no signs of CMC joint pathology. Kinematics of the first metacarpal were described with respect to the trapezium using helical axis of motion (HAM) variables and then modeled with discrete Fourier analysis. Each HAM variable was fit in a cyclic domain as a function of screw axis orientation in the trapezial articular plane; the RMSE of the fits was 14.5 deg, 1.4 mm, and 0.8 mm for the elevation, location, and translation, respectively. After normalizing for the larger bone size in men, no differences in the kinematic variables between sexes could be identified. Analysis of the kinematic data also revealed notable coupling of the primary rotations of the thumb with translation and internal and external rotations. This study advances our basic understanding of thumb CMC joint function and provides a complete description of the CMC joint for incorporation into future models of hand function. From a clinical perspective, our findings provide a basis for evaluating CMC pathology, especially the mechanically mediated aspects of osteoarthritis (OA), and should be used to inform artificial joint design, where accurate replication of kinematics is essential for long-term success.

In vivo recruitment patterns in the anterior oblique and dorsoradial ligaments of the first carpometacarpal joint.

The anterior oblique ligament (AOL) and the dorsoradial ligament (DRL) are both regarded as mechanical stabilizers of the thumb carpometacarpal (CMC) joint, which in older women is often affected by osteoarthritis. Inferences on the potential relationship of these ligaments to joint pathomechanics are based on clinical experience and studies of cadaveric tissue, but their functions has been studied sparsely in vivo. The purpose of this study was to gain insight into the functions of the AOL and DRL using in vivo joint kinematics data. The thumbs of 44 healthy subjects were imaged with a clinical computed tomography scanner in functional-task and thumb range-of-motion positions. The origins and insertion sites of the AOL and the DRL were identified on the three-dimensional bone models and each ligament was modeled as a set of three fibers whose lengths were the minimum distances between insertion sites. Ligament recruitment, which represented ligament length as a percentage of the maximum length across the scanned positions, was computed for each position and related to joint posture. Mean AOL recruitment was lower than 91% across the CMC range of motion, whereas mean DRL recruitment was generally higher than 91% in abduction and flexion. Under the assumption that ligaments do not strain by more than 10% physiologically, our findings of mean ligament recruitments across the CMC range of motion indicate that the AOL is likely slack during most physiological positions, whereas the DRL may be taut and therefore support the joint in positions of CMC joint abduction and flexion.

Early osteoarthritis of the trapeziometacarpal joint is not associated with joint instability during typical isometric loading.

The saddle-shaped trapeziometacarpal (TMC) joint contributes importantly to the function of the human thumb. A balance between mobility and stability is essential in this joint, which experiences high loads and is prone to osteoarthritis (OA). Since instability is considered a risk factor for TMC OA, we assessed TMC joint instability during the execution of three isometric functional tasks (key pinch, jar grasp, and jar twist) in 76 patients with early TMC OA and 44 asymptomatic controls. Computed tomography images were acquired while subjects held their hands relaxed and while they applied 80% of their maximum effort for each task. Six degree-of-freedom rigid body kinematics of the metacarpal with respect to the trapezium from the unloaded to the loaded task positions were computed in terms of a TMC joint coordinate system. Joint instability was expressed as a function of the metacarpal translation and the applied force. We found that the TMC joint was more unstable during a key pinch task than during a jar grasp or a jar twist task. Sex, age, and early OA did not have an effect on TMC joint instability, suggesting that instability during these three tasks is not a predisposing factor in TMC OA.

In Vivo kinematics of the trapeziometacarpal joint during thumb extension-flexion and abduction-adduction.

PURPOSE: The primary aim of this study was to determine whether the in vivo kinematics of the trapeziometacarpal (TMC) joint differ as a function of age and sex during thumb extension-flexion (Ex-Fl) and abduction-adduction (Ab-Ad) motions. METHODS: The hands and wrists of 44 subjects (10 men and 11 women with ages 18-35 y and 10 men and 13 women with ages 40-75 y) with no symptoms or signs of TMC joint pathology were imaged with computed tomography during thumb extension, flexion, abduction, and adduction. The kinematics of the TMC joint were computed and compared across direction, age, and sex. RESULTS: We found no significant effects of age or sex, after normalizing for size, in any of the kinematic parameters. The Ex-Fl and Ab-Ad rotation axes did not intersect, and both were oriented obliquely to the saddle-shaped anatomy of the TMC articulation. The Ex-Fl axis was located in the trapezium and the Ab-Ad axis was located in the metacarpal. Metacarpal translation and internal rotation occurred primarily during Ex-Fl. CONCLUSIONS: Our findings indicate that normal TMC joint kinematics are similar in males and females, regardless of age, and that the primary rotation axes are nonorthogonal and nonintersecting. In contrast to previous studies, we found Ex-Fl and Ab-Ad to be coupled with internal-external rotation and translation. Specifically, internal rotation and ulnar translation were coupled with flexion, indicating a potential stabilizing screw-home mechanism. CLINICAL RELEVANCE: The treatment of TMC pathology and arthroplasty design require a detailed and accurate understanding of TMC function. This study confirms the complexity of TMC kinematics and describes metacarpal translation coupled with internal rotation during Ex-Fl, which may explain some of the limitations of current treatment strategies and should help improve implant designs.

Is early osteoarthritis associated with differences in joint congruence?

Previous studies suggest that osteoarthritis (OA) is related to abnormal or excessive articular contact stress. The peak pressure resulting from an applied load is determined by many factors, among which is shape and relative position and orientation of the articulating surfaces or, referring to a more common nomenclature, joint congruence. It has been hypothesized that anatomical differences may be among the causes of OA. Individuals with less congruent joints would likely develop higher peak pressure and thus would be more exposed to the risk of OA onset. The aim of this work was to determine if the congruence of the first carpometacarpal (CMC) joint differs with the early onset of OA or with sex, as the female population has a higher incidence of OA. 59 without and 38 with early OA were CT-scanned with their dominant or arthritic hand in a neutral configuration. The proposed measure of joint congruence is both shape and size dependent. The correlation of joint congruence with pathology and sex was analyzed both before and after normalization for joint size. We found a significant correlation between joint congruence and sex due to the sex-related differences in size. The observed correlation disappeared after normalization. Although joint congruence increased with size, it did not correlate significantly with the onset of early OA. Differences in joint congruence in this population may not be a primary cause of OA onset or predisposition, at least for the CMC joint.