Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems

STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.

Real-Time Identification of Wrist Kinematics via Sparsity-Promoting Extended Kalman Filter Based on Ellipsoidal Joint Formulation

This paper proposes a novel method for real-time wrist kinematics identification. Method: We design the wrist kinematics regression model following a novel ellipsoidal joint formulation, which features a quaternion-based rotation constraint and 2-dimensional Fourier linear combiners (FLC) to approximate the coupled rotations and translational displacements of the wrist. Extended Kalman Filter (EKF) is then implemented to update the model in real-time. However, unlike previous studies, here we introduce a sparsity-promoting feature in the model regression through the optimality of EKF by designing a smooth 1-minimization observation function. This is done to ensure the best identification of key parameters, and to improve the robustness of regression under noisy conditions. Results: Simulations employ multiple reference models to evaluate the performance of the proposed approach. Experiments are later carried out on motion data collected by a lab-developed wrist kinematics measurement tool. Both simulation and experiment show that the proposed approach can robustly identify the wrist kinematics in real-time. Conclusion: The findings confirm that the proposed regression model combined with the sparsity-promoting EKF is reliable in the real-time modeling of wrist kinematics. Significance: The proposed method can be applied to generic wrist kinematics modeling problems, and utilized in the control system of wearable wrist exoskeletons. The framework of the proposed method may also be applied to real-time identification of other joints for exoskeleton control.

Simplified digital balance assessment in typically developing school children

BACKGROUND

Postural balance can be considered a conjoined parameter of gross motor performance. It is acquired in early childhood and honed until adolescence, but may also be influenced by various conditions. A simplified clinical assessment of balance and posture could be helpful in monitoring motor development or therapy particularly in pediatric patients. While analogue scales are considered unprecise and lab-based force-plate posturography lacks accessibility, we propose a novel kinematic balance assessment based on markerless 3D sensor technology.

RESEARCH QUESTION

Can balance and posture be assessed by tracking kinematic data using a single 3D motion tracking camera and are the results representative of normal motor development in a healthy pediatric cohort?

METHODS

A proprietary algorithm was developed and tested that uses skeletal data from the Microsoft Kinect™ V2 3D motion capture camera to calculate and track the center of mass in real time during a set of balance tasks. The algorithm tracks the distance of the COM traveled over time to calculate a balance score (COM speed). For this study, 432 school children aged 4-18 years performed 5 balance tasks and the resulting balance scores were analyzed and correlated with demographic data.

RESULTS

Preliminary experiments demonstrated that the system was able to reliably detect differences in COM speed during different balance tasks. The method showed moderate correlation with age and sex. Athletic activity positively correlated with balance skill in the age group < 8 years, but not in older children. Body mass appeared not to be correlated with balance ability.

SIGNIFICANCE

This study demonstrates that markerless 3D motion analysis can be used for the clinical assessment of coordination and balance and could potentially be used to monitor gross motor performance at the point-of-care.

Craniocervical Posture Assessed With Photogrammetry and the Accuracy of Palpation Methods for Locating the Seventh Cervical Spinous Process: A Cross-sectional Study

OBJECTIVE

The purpose of this study was to compare craniocervical posture assessed by photogrammetry using 2 distinct palpation methods for locating the spinous process of the seventh cervical vertebra (C7SP).

METHODS

This cross-sectional study was conducted in 2 phases. In phase I (n = 42), the assessor's accuracy in locating the C7SP using the flexion-extension and the modified thorax-rib static methods was compared to radiography. In phase II (n = 68), the craniocervical posture was analyzed with photogrammetry after palpation using the 2 methods. Neck pain intensity and disability were also determined.

RESULTS

The accuracy in locating the C7SP was higher using the modified thorax-rib static method (67%, 95% confidence interval [CI], 55-79) compared to the flexion-extension method (38%, 95% CI, 26-50, P = .016). Lower values of the craniocervical angle were obtained with the flexion-extension method than the modified thorax-rib static method (mean difference = -1.1°, 95% CI, -1.6 to -0.6, P < .001). However, both palpation methods resulted in similar classifications of participants as with or without forward head posture (P = .096). Weak correlations were observed between the craniocervical angle and neck pain intensity (ρ = -0.088 and -0.099, respectively) and disability (ρ = -0.231 and -0.249, respectively).

CONCLUSION

Craniocervical angles obtained using palpation methods with different accuracies were different, although the magnitude of the difference was insufficient to lead to different classifications of a forward head posture in adults with mild neck pain and disability. Craniocervical posture was weakly correlated with neck-pain intensity and disability.

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application

Optoelectronic devices are the gold standard for 3D evaluation in clinics, but due to the complexity of this kind of hardware and the lack of access for patients, affordable, transportable, and easy-to-use systems must be developed to be largely used in daily clinics. The Kinect^TM sensor has various advantages compared to optoelectronic devices, such as its price and transportability. However, it also has some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points, which makes 3D evaluation impossible. To overcome these limitations, a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises, allowing functional evaluation while performing physical rehabilitation. To validate this new approach, a two-step method was used. The first step was a laboratory validation where the results obtained with the Kinect^TM were compared with the results obtained with an optoelectronic device; 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of this kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns could be observed. The new methodology and the different steps of the validations are presented in this paper.

Is the instrumented-pointer method of calibrating anatomical landmarks in 3D motion analysis reliable?

Instrumented-pointers are often used to calibrate anatomical landmarks in biomechanical analyses. However, little is known about the effect of altering the orientation of the pointer during calibration on the co-ordinates recorded. Incorrect positioning of a landmark influences the axes created, and thus the kinematic data recorded. This study aimed to investigate the reliability of the pointer method for anatomical calibration. Two points were drawn onto a fixed box to resemble knee joint epicondyles, then a custom-made pointer was used to define the positions of these landmarks in three-dimensions. Twenty different pointer-orientations were chosen, and the position of the pointer in each of these orientations was recorded 8 times. Euclidean distances between single points were calculated for both landmarks and compared statistically (α = 0.05). Average Euclidean distances between all reconstructed points were 3.2±1.4mm (range: 0.3-7.1mm) for one landmark and 3.3±1.5mm (range: 0.3-7.9mm) for the other. The x- and y-co-ordinates recorded differed statistically when the pointer was moved about the X and Y axes (anterior/posterior and superior/inferior to landmark) (p < 0.05). No statistical differences were found between co-ordinates recorded when the pointer was moved around the Z axes (p > 0.05). ICC values for all co-ordinates were excellent, highlighting the reliability of the method (ICC > 0.90). These results support this method of anatomical calibration; however, we recommend that pointers be consistently held in a neutral oriented position (where the handle is not anterior, posterior, superior or inferior to the landmark) during calibration, to reduce the likelihood of calibration errors.

Physiologically corrected coupled motion during gait analysis using a model-based approach

Gait analysis is used in daily clinics for patients' evaluation and follow-up. Stereophotogrammetric devices are the most used tool to perform these analyses. Although these devices are accurate results must be analyzed carefully due to relatively poor reproducibility. One of the major issues is related to skin displacement artifacts. Motion representation is recognized reliable for the main plane of motion displacement, but secondary motions, or combined, are less reliable because of the above artifacts. Model-based approach (MBA) combining accurate joint kinematics and motion data was previously developed based on a double-step registration method. This study presents an extensive validation of this MBA method by comparing results with a conventional motion representation model. Thirty five healthy subjects participated to this study. Gait motion data were obtained from a stereophotogrammetric system. Plug-in Gait model (PiG) and MBA were applied to raw data, results were then compared. Range-of-motion, were computed for pelvis, hip, knee and ankle joints. Differences between PiG and MBA were then computed. Paired-sample t-tests were used to compare both methods. Normalized root-mean square errors were also computed. Shapes of the curves were compared using coefficient of multiple correlations. The MBA and PiG approaches shows similar results for the main plane of motion displacement but statistically significative discrepancies appear for the combined motions. MBA appear to be usable in applications (such as musculoskeletal modeling) requesting better approximations of the joints-of-interest thanks to the integration of validated joint mechanisms.