Subaxial lateral mass prosthesis for posterior reconstruction of cervical spine

Purpose: To obtain the relevant morphometry of the lateral mass of the subaxial cervical spine (C3-C7) and to design a series of lateral mass prostheses for the posterior reconstruction of the stability of cervical spine. Methods: The computed tomography (CT) scans of healthy volunteers were obtained. RadiAnt DICOM Viewer software (Version 2020.1, Medixant, Poland) was used to measure the parameters of lateral mass, such as height, anteroposterior dimension (APD), mediolateral dimension (MLD) and facet joint angle. According to the parameters, a series of cervical lateral mass prostheses were designed. Cadaver experiment was conducted to demonstrate its feasibility. Results: 23 volunteers with an average age of 30.1 ± 7.1 years were enrolled in this study. The height of lateral mass is 14.1 mm averagely. Facet joint angle, APD and MLD of lateral mass averaged 40.1 degrees, 11.2 mm and 12.18 mm, respectively. With these key data, a lateral mass prosthesis consists of a bone grafting column and a posterior fixation plate was designed. The column has a 4.0 mm radius, 41 degrees surface angle and adjustable height of 13, 15, or 17 mm. In the cadaver experiment, the grafting column could function as a supporting structure between adjacent facets, and it would not violate exiting nerve root (NR) or vertebral artery (VA). Conclusion: This study provided detailed morphology of the lateral mass of subaxial cervical spine. A series of subaxial cervical lateral mass prostheses were designed awaiting further clinical application.

Proprioceptive sensitivity to imposed finger deflections

Hand proprioception - the sense of the posture and movements of the wrist and digits - is critical to dexterous manual behavior and to stereognosis, the ability to sense the three-dimensional structure of objects held in the hand. To better understand this sensory modality and its role in hand function, we sought to characterize the acuity with which the postures and movements of finger joints are sensed. To this end, we measured the ability of human subjects to discriminate changes in posture and speed around the three joints of the index finger. In these experiments, we isolated the sensory component by imposing the postures on an otherwise still hand, to complement other studies, in which subjects made judgments on actively achieved postures. We found that subjects could reliably sense 12-16% changes in joint angle and 18-32% changes in joint speed. Furthermore, the acuity for posture and speed was comparable across the three joints of the finger. Finally, task performance was unaffected by the presence of a vibratory stimulus, calling into question the role of cutaneous cues in hand proprioception.

Multivariate analysis of pedicle screw invasion of the proximal facet joint after lumbar surgery

Background To analyze the risk factors for pedicle screw invasion of the proximal facet joint after lumbar surgery. Methods From January 2019 to January 2021, 1794 patients with lumbar degenerative disease, such as lumbar disc herniation, lumbar spinal stenosis and lumbar spondylolisthesis, were treated at our hospital. In all, 1221 cases were included. General data (sex, age, BMI), bone mineral density, proximal facet joint angle, degenerative lumbar spondylolisthesis, isthmic lumbar spondylolisthesis and fixed segment in the two groups were recorded. After the operation, vertebral CT of the corresponding surgical segments was performed for three-dimensional reconstruction and evaluation of whether the vertebral arch root screw interfered with the proximal facet joint. The included cases were divided into an invasion group and a noninvasion group. Univariate analysis was used to screen the risk factors for pedicle screw invasion of the proximal facet joint after lumbar surgery, and the selected risk factors were included in the logistic model for multivariate analysis. Results The single-factor analysis showed a significant difference in age, BMI, proximal facet joint angle, degenerative lumbar spondylolisthesis, and fixed segment (P < 0.1). Multifactor analysis of the logistic model showed a significant difference for age ≥ 50 years (P < 0.001, OR = 2.291), BMI > 28 kg/m2 (P < 0.001, OR = 2.548), degenerative lumbar spondylolisthesis (P < 0.001, OR = 2.187), gorge cleft lumbar relaxation (P < 0.001, OR = 2.410), proximal facet joint angle (35 ~ 45°: P < 0.001, OR = 3.151; > 45°: P < 0.001, OR = 3.578), and fixed segment (lower lumbar spine: P < 0.001, OR = 2.912). Conclusion Age (≥ 50 years old), BMI (> 28 kg/m2), proximal facet joint angle (35 ~ 45°, > 45°), degenerative lumbar spondylolisthesis, isthmic lumbar spondylolisthesis and fixed segment (lower lumbar spine) are independent risk factors for pedicle screw invasion of the proximal facet joint after lumbar surgery. Compared with degenerative lumbar spondylolisthesis, facet joint intrusion is more likely in isthmic lumbar spondylolisthesis.

A numerically stable algorithm for analytic inverse kinematics of 7-DoF S-R-S manipulators with joint limit avoidance

This paper presents a numerically stable algorithm for analytic inverse kinematics of 7-DoF S-R-S manipulators with joint limit avoidance. The arm angle is used to represent the self-motion manifold within a global arm configuration. The joint limits are analytically mapped to the arm angle space for joint limit avoidance. To profile the relation between the joint angle and arm angle, it is critical to characterize the singular arm angle for each joint. In the-state-of-the art methods, the existence of the singular arm angle is triggered by comparing a discriminant with zero given a threshold. We will show this leads to numerical issues since the threshold is inconsistent among different target poses, leading to incorrect range of the arm angle. These issues are overcome by associating indeterminate joint angles of tangent joints with angles of 0 or pi of cosine joints, rather than using an independent threshold for each joint. The closed-form algorithm in C++ code to perform numerically stable inverse kinematics of 7-DoF S-R-S manipulators with global arm configuration control and joint limit avoidance is also given.

In vivo assessment of the passive stretching response of the bi-compartmental human semitendinosus muscle using shear wave elastography

The semitendinosus muscle contains distinct proximal and distal compartments arranged anatomically in-series but separated by a tendinous inscription, with each compartment innervated by separate nerve branches. Although extensively investigated in other mammals, compartment-specific mechanical properties within the human semitendinosus have scarcely been assessed in vivo. Experimental data obtained during muscle-tendon unit stretching (e.g., slack angle) can also be used to validate and/or improve musculoskeletal model estimates of semitendinosus muscle force. The purpose of this study was to investigate the passive stretching response of proximal and distal humans semitendinosus compartments to distal joint extension. Using two-dimensional shear wave elastography, we bilaterally obtained shear moduli of both semitendinosus compartments from 14 prone-positioned individuals at ten knee flexion angles (from 90° to 0° [full extension] at 10° intervals). Passive muscle mechanical characteristics (slack angle, slack shear modulus, and the slope of the increase in shear modulus) were determined for each semitendinosus compartment by fitting a piecewise exponential model to the shear modulus-joint angle curves. We found no differences between compartments or legs for slack angle, slack shear modulus, or the slope of the increase in shear modulus. We also found the experimentally determined slack angle occurred at ~15-80° higher knee flexion angles compared to estimates from two commonly used musculoskeletal models, depending on participant and model used. Overall, these findings demonstrate that passive shear modulus-joint angle curves do not differ between proximal and distal human semitendinosus compartments, and provide experimental data to improve semitendinosus force estimates derived from musculoskeletal models.

InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

A-GAS: a Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children

<pre><p>Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably. </p> </pre>

Postural Variabilities Associated with the Most Comfortable Sitting Postures: A Preliminary Study

This study examined postural variabilities based on the self-perceived most comfortable postures of 12 participants (six men and six women) when sitting on three commonly used types of chairs (a stool, computer chair, and gaming chair). Participants’ global joint angles were recorded and analyzed. Of the chairs studied, the stool was not adjustable, but the computer and gaming chairs were moderately and highly adjustable, respectively. During the test, participants were encouraged to adjust the chairs until they perceived that the most comfortable posture had been reached. The results demonstrated that in a sitting position perceived to be comfortable, the participants’ postural variabilities with respect to global joint angle, calculated from five repetitions, were unexpectedly high for all three chair types, at approximately 9.4, 10.2, and 11.1° for head inclination, trunk angle, and knee angle, respectively. The average differences in range for each joint angle among the three chair types were relatively low, with all values within 3°. The result also showed that gender (p < 0.01) and chair type (p < 0.001) significantly affected trunk angle, whereas these variables did not affect head inclination or knee angle (p > 0.05). The preliminary results observed unexpectedly high variabilities in sitting posture when the participants sat at a posture that they perceived to be the most comfortable. The findings also indicated an inherent difference in comfortable sitting posture between genders; women tend to extend their trunk backward more than men. For permanent use with only an initial adjustment and memory-aided seat design, designers should minimize the loads that are borne by body parts over a prolonged period due to an unchanging sitting posture.