Kinematic Angles Research Articles

Comparative analysis of biomechanical characteristics of knee flexion and extension muscles in volleyball physical training

This study aimed to analyze the biomechanical characteristics of knee flexion and extension muscles in volleyball physical training from the cellular and molecular biomechanics aspect. Multiple training modes like strength, elasticity, and comprehensive training were chosen to systematically evaluate relevant characteristics of these muscles among volleyball players. Advanced devices such as 3D motion capture systems, ground reaction force platforms, and electromyography equipment were used to gather precise biomechanical data. At the cellular and molecular level, different trainings impact muscle cells differently. For example, strength training might enhance the synthesis of contractile proteins within cells, while elasticity training could influence the elasticity-related molecular structures. With multidimensional data analysis, the effects of various training modes were compared. The comprehensive training group had a kinematic flexion extension angle of 528.27º ± 11.49º, an angular velocity of 135.52º ± 5.97º, and an angular acceleration of 3177.02º ± 116.88º, performing best. This could be due to its comprehensive influence on cellular and molecular processes in muscles, promoting better coordination and force generation. This article offers a theoretical basis for volleyball players to create scientific training plans and gives practical tips for coaches and athletes to optimize programs and prevent injuries. By focusing on cellular and molecular biomechanics, it fills research gaps and helps boost the development of biomechanics in volleyball physical training.

Steps to Facilitate the Use of Clinical Gait Analysis in Stroke Patients: The Validation of a Single 2D RGB Smartphone Video-Based System for Gait Analysis.

Clinical gait analysis plays a central role in the rehabilitation of stroke patients. However, practical and technical challenges limit their use in clinical settings. This study aimed to validate SMARTGAIT, a deep learning-based gait analysis system that addresses these limitations. Eight stroke patients took part in the study at the Human Performance Research Centre of the University of Konstanz. Gait measurements were taken using both the marker-based Vicon motion capture system and the single-smartphone-based SMARTGAIT system. We evaluated the agreement for knee, hip, and ankle joint angle kinematics in the frontal and sagittal plane and spatiotemporal gait parameters between the two systems. The results mostly demonstrated high levels of agreement between the two systems, with Pearson correlations of ≥0.79 for all lower body angle kinematics in the sagittal plane and correlations of ≥0.71 in the frontal plane. RMSE values were ≤4.6°. The intraclass correlation coefficients for all derived gait parameters showed good to excellent levels of agreement. SMARTGAIT is a promising tool for gait analysis in stroke, particularly for quantifying gait characteristics in the sagittal plane, which is very relevant for clinical gait analysis. However, further analyses are required to validate the use of SMARTGAIT in larger samples and its transferability to different types of pathological gait. In conclusion, a single smartphone recording (monocular 2D RGB camera) could make gait analysis more accessible in clinical settings, potentially simplifying the process and making it more feasible for therapists and doctors to use in their day-to-day practice.

A DEVICE-BASED (GAITON) SAGITTAL PLANE KINEMATICS ANALYSIS OF LOWER EXTREMITY JOINTS AMONG PATIENTS WITH KNEE OSTEOARTHRITIS – A METHODOLOGICAL APPROACH.

Background: Gait assessment is an effective method for identifying functional limitations associated with knee Osteoarthritis and understanding the relationship between disease symptoms and gait quality. Objective: To evaluate the mean difference in the kinematics of lower extremity joints among patients with knee osteoarthritis. Methods: A total of 20 people living with Knee OA were recruited as of 25th September 2024 in this prospective cross-sectional design study. The eligible participants were conveniently recruited from the SRM physiotherapy OPD based on voluntary participation after signing the informed consent. Adult participants aged 18 years and above, both sexes and able to walk independently without mobility aids were recruited and the joint kinematics were measured using the GaitOn Software, 2-dimensional analysis, and sagittal kinematic parameters of stance phase. Results: The mean age of the patients was 53.6 ± 7.3, and the mean BMI was 29.1±4.6. The one sample t-test demonstrated a statistically significant difference between kinematic angles of the lower extremity joint in sagittal plane of population mean (reference value) and sample mean (measured by GaitOn); Initial contact phase (hip: mean diff -4.06, p<0.01, ankle: mean diff 34.1, p < 0.0001), Loading response (hip: mean diff -5.8. p <0.001, knee: mean diff 9.6, p < 0.001, ankle: mean diff 32.4, p < 0.001). Midstance (hip: mean diff 11.06, p <0.001, ankle: 31.4, p< 0.001). Terminal stance (hip: mean diff 28.3, p< 0.004, ankle: mean diff 31.4, p< 0.001) and Pre-swing (hip: mean diff 16.5, p< 0.001, ankle: mean diff 31.4, p< 0.001). Conclusions: The hip and ankle joints equally share knee kinematic compensations. By acknowledging the complex interactions between the hip, ankle, and knee joints, healthcare providers can develop more comprehensive and effective treatment plans for individuals with knee OA.

Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury – a comparison of an image extraction and classification system to expert scoring

BackgroundWalking impairment is a common disability post acquired brain injury (ABI), with visually evident arm movement abnormality identified as negatively impacting a multitude of psychological factors. The International Classification of Functioning, Disability and Health (ICF) qualifiers scale has been used to subjectively assess arm movement abnormality, showing strong intra-rater and test-retest reliability, however, only moderate inter-rater reliability. This impacts clinical utility, limiting its use as a measurement tool. To both automate the analysis and overcome these errors, the primary aim of this study was to evaluate the ability of a novel two-level machine learning model to assess arm movement abnormality during walking in people with ABI.MethodsFrontal plane gait videos were used to train four networks with 50%, 75%, 90%, and 100% of participants (ABI: n = 42, healthy controls: n = 34) to automatically identify anatomical landmarks using DeepLabCut™ and calculate two-dimensional kinematic joint angles. Assessment scores from three experienced neurorehabilitation clinicians were used with these joint angles to train random forest networks with nested cross-validation to predict assessor scores for all videos. Agreement between unseen participant (i.e. test group participants that were not used to train the model) predictions and each individual assessor’s scores were compared using quadratic weighted kappa. One sample t-tests (to determine over/underprediction against clinician ratings) and one-way ANOVA (to determine differences between networks) were applied to the four networks.ResultsThe machine learning predictions have similar agreement to experienced human assessors, with no statistically significant (p < 0.05) difference for any match contingency. There was no statistically significant difference between the predictions from the four networks (F = 0.119; p = 0.949). The four networks did however under-predict scores with small effect sizes (p range = 0.007 to 0.040; Cohen’s d range = 0.156 to 0.217).ConclusionsThis study demonstrated that machine learning can perform similarly to experienced clinicians when subjectively assessing arm movement abnormality in people with ABI. The relatively small sample size may have resulted in under-prediction of some scores, albeit with small effect sizes. Studies with larger sample sizes that objectively and automatically assess dynamic movement in both local and telerehabilitation assessments, for example using smartphones and edge-based machine learning, to reduce measurement error and healthcare access inequality are needed.

An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning

Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME’s kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu’s method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME’s motion in time-series images and finally determine the CME’s kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs’ geoeffectiveness.

Properties of a fading AGN from SDSS-IV MaNGA

ABSTRACT We identify a fading AGN SDSS J220141.64+115124.3 from the internal Product Launch-11 (MPL-11) in Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The central region with a projected radius of $\sim$2.4 kpc is characterized as LINER-like line ratios while the outskirts extended to $\sim$15 kpc show Seyfert-like line ratios. The ${\rm{[O {\small III}]}}$$\lambda$5007 luminosity of the Seyfert regions is a factor of 37 (2) higher than the LINER regions without (with) dust attenuation correction, suggesting that the AGN activity decreases at least $\sim$8 $\times$ 10$^3$ yr ($\sim$2.4 kpc/light-speed) ago. We model the emission line spectra in the central region with double Gaussian components (a narrow core and a broad wing) and analyse the properties of each component. The narrow core component mostly co-rotates with the stellar disc, whereas the broad wing component with a median of the velocity dispersion $\sim$300 km s$^{-1}$ is related to a wind outflow. The kinematic position angle (PA) of the ionized gas shows an $\sim 20^{\circ }$ twist from the galaxy centre to 1.5 effective radius. The median of the PA difference between the gas and stellar components is as large as $\sim 50^{\circ }$ within 0.4 effective radius. The tidal feature in DESI image and star–gas misalignment suggest this galaxy is a merger remnant. Combining all these observational results as well as public available X-ray and MIR luminosities, we confirm this is a fading AGN, the merger process kick-started the central engine to quasar phase which ionized gas composed of tidal debris, and now the activity of the central black hole decreases. The discontinuity in ${\rm{[O {\small III}]}}$$\lambda$5007 flux and EQW maps is due to multiple AGN outbursts triggered by merger remnant gas inflows.

Where to mount the IMU? Validation of joint angle kinematics and sensor selection for activities of daily living

We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average &amp;lt; 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.

Revealing the kinematic puzzle of the AGN host NGC 3884: optical integral field spectroscopy unravels stellar and gas motions

ABSTRACT We used optical integral field spectroscopy to analyse the stellar and gas properties of the inner 1.4 kpc radius of NGC 3884, a low-luminosity active galactic nucleus (AGN) host. The observations were performed with Gemini Multi-Object Spectrograph (GMOS)-Integral Field Unit at a seeing of ∼0.85 arcsec (475 pc at the galaxy) that allowed us to map the stellar and gas emission structure and kinematics, for the first time in this galaxy. The stellar motions are consistent with rotation in a disc, with the kinematic position angle (PA) ranging from approximately 0° within 500 pc to 20° beyond 1 kpc, consistent with the photometric PA. We detected extended ionized and neutral gas emission throughout most of the GMOS field of view, with three kinematic components: (i) a disc component with a kinematic PA similar to that of the stars beyond ∼670 pc from the nucleus; (ii) a twist in the PA of up to 60° at a smaller radii that we attribute to gas inflow towards the nucleus; and (iii) an outflow detected as broad components to the emission lines (σ ∼ 250–400 km s−1), with a maximum mass outflow rate of 0.25 ± 0.15 M⊙ yr−1 and a kinetic power corresponding to 0.06 per cent of the AGN bolometric luminosity, possibly being powerful enough to suppress star formation in the galaxy. The observed gas kinematics thus reveals both inflows and outflows in ionized gas.

Perancangan dan Analisis Konstruksi Mekanik pada Robot Pemadam Api Hexapod Menggunakan Metode Finite Element Analysis dan Inverse Kinematics

Perancangan dan Analisis Konstruksi Mekanik pada Robot Pemadam Api Hexapod Menggunakan Metode Finite Element Analysis dan Inverse Kinematics

Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements

Motion analysis is essential for assessing in-vivo human biomechanics. Marker-based motion capture is the standard to analyze human motion, but the inherent inaccuracy and practical challenges limit its utility in large-scale and real-world applications. Markerless motion capture has shown promise to overcome these practical barriers. However, its fidelity in quantifying joint kinematics and kinetics has not been verified across multiple common human movements. In this study, we concurrently captured marker-based and markerless motion data on 10 healthy study participants performing 8 daily living and exercise movements. We calculated the correlation (Rxy) and root-mean-square difference (RMSD) between markerless and marker-based estimates of ankle dorsi-plantarflexion, knee flexion, and three-dimensional hip kinematics (angles) and kinetics (moments) during each movement. Estimates from markerless motion capture matched closely with marker-based in ankle and knee joint angles (Rxy ≥ 0.877, RMSD ≤ 5.9°) and moments (Rxy ≥ 0.934, RMSD ≤ 2.66 % height × weight). High outcome comparability means the practical benefits of markerless motion capture can simplify experiments and facilitate large-scale analyses. Hip angles and moments demonstrated more differences between the two systems (RMSD: 6.7–15.9° and up to 7.15 % height × weight), especially during rapid movements such as running. Markerless motion capture appears to improve the accuracy of hip-related measures, yet more research is needed for validation. We encourage the biomechanics community to continue verifying, validating, and establishing best practices for markerless motion capture, which holds exciting potential to advance collaborative biomechanical research and expand real-world assessments needed for clinical translation.

MATHEMATICAL MODELS OF CUTTING FORCES IN TURNING OPERATIONS

The paper considers a variant of the predictive determination of the cutting force components for non-free cutting when turning. It is established that both the process of plastic deformation and the process of friction on back surfaces adjacent to main and auxiliary cutting edges of the tool influence the magnitude and direction of a cutting force component. The resulting force is defined as the vector sum of the forces that occur on the main and auxiliary cutting edges. We propose to determine the cutting force direction in non-free cutting as a vector in the direction of chip flow relative to the main cutting edge. The cutting process photography fixes the inclination angle of the chip flow. It is confirmed that in free cutting, the direction of chip flow is perpendicular to the main cutting edge. Contour turning on CNC lathes is accompanied by a change in the direction of feed motion resulting in a change in the main and auxiliary kinematic angles of the cutting tool. An increase in the active length of the main cutting edge in this situation leads to an increase in the contact area of the back surface of the main cutting edge with the workpiece surface, and, consequently, to an increase in the friction component of force, acting on the main cutting edge. Experimental measurement of the cutting force components makes it possible to determine the degree of influence of the force components associated with both the process of plastic deformation of the work material and the process of friction on the back surfaces adjacent to the main and auxiliary cutting edges of the tool. The obtained analytical dependences express the functional relationship between the elements of the cutting modes, the geometric parameters of the cutters, the degree of wear, the shape of the workpiece surface, and physical and mechanical properties of the work material. A large set of parameters included in the formulas for determining the components of the cutting force allows adequate monitoring the nature of the force interaction of the elements in the technological system when processing parts.

Analyzing the impact of activation functions on the performance of the data-driven gait model

Long-term prediction of joint kinematics is an important factor for the development of advanced technology in the area of the prosthetic leg, biped robot, automotive industry, and human-robot collaboration. It is well-defined that the long-term prediction of joint kinematic angle for an uneven surface is a challenging task. In previous work, authors have employed deep learning techniques for kinematic modelling using the collected walking dataset on an uneven surface with multiple speeds. However, it is a challenging task to find the appropriate activation function for deep learning techniques. Therefore, this research work focuses on the suitability of the activation function for a data-driven gait model for multiple slopes and inclines as ground conditions. Twenty-five activation function with their time complexity is extensively studied. In addition, the fusion of standard error from the subject mean trajectory with conventional loss function is also presented. This fusion helps the training algorithm to train the data-driven model to follow the low variance point in the gait cycle more precisely. The result shows that the Sigmoid-weighted Linear Units (SiLU) activation function-based gait model outperforms the others based on the maximum error summary statistic. It is also established that the SiLU function-based gait model trained by fused objective function significantly improved the long-term prediction for two cases: (a) step change slope and (b) continuously varying slope.

Evaluating the effect of glidants on tablet sticking propensity of ketoprofen using powder rheology

Punch sticking has been a leading drawback that has challenged successful tablet manufacturing since its initial conception. Due to the capricious nature of the complication, this can arise during any phase of the development process. Even now, identifying such a problem is a prerequisite during the initial stage of development. The present study evaluated the role of Aerosil®200, talc, and Syloid®244 as glidants in varying amounts ranging from 0.0 percent to 2.0 percent w/w on tablets sticking relatively to five different metal surfaces, with ketoprofen as the model drug. Powder rheology is a predictable technique used to calculate the sticking index. The sticking index of each formulation in comparison to each metal coupon was identified by calculating the kinematic angle of internal friction and the angle of wall friction using the shear cell test and wall friction test, respectively. Interestingly, glidants were found to reduce the sticking propensity of the powder blend in a concentration-dependent manner. In addition, the compression study validated the expected sticking tendency ranking order. According to the research data, the sticking index could effectively be utilized to envisage the possibility of tablet sticking, i.e., by selecting the formulation’s excipient and their percentages or selecting appropriate punched metal surfaces in the tableting process.

Evaluating the effects of giraffe skin disease and wire snare wounds on the gaits of free-ranging Nubian giraffe

Giraffe skin disease (GSD), a condition that results in superficial lesions in certain giraffe (Giraffa spp.) populations, has emerged as a potential conservation threat. Preliminary findings suggested that individuals with GSD lesions move with greater difficulty which may in turn reduce their foraging efficiency or make them more vulnerable to predation. A current known threat to some giraffe populations is their mortality associated with entrapment in wire snares, and the morbidity and potential locomotor deficiencies associated with wounds acquired from snares. The goal of our study was to quantify the locomotor kinematics of free-ranging Nubian giraffe (G. camelopardalis camelopardalis) in Murchison Falls National Park (MFNP), Uganda, and compare spatiotemporal limb and neck angle kinematics of healthy giraffe to those of giraffe with GSD lesions, snare wounds, and both GSD lesions and snare wounds. The presence of GSD lesions did not significantly affect spatiotemporal limb kinematic parameters. This finding is potentially because lesions were located primarily on the necks of Nubian giraffe in MFNP. The kinematic parameters of individuals with snare wounds differed from those of healthy individuals, resulting in significantly shorter stride lengths, reduced speed, lower limb phase values, and increased gait asymmetry. Neck angle kinematic parameters did not differ among giraffe categories, which suggests that GSD neck lesions do not impair normal neck movements and range of motion during walking. Overall, MFNP giraffe locomotor patterns are largely conservative between healthy individuals and those with GSD, while individuals with snare wounds showed more discernible kinematic adjustments consistent with unilateral limb injuries. Additional studies are recommended to assess spatiotemporal limb kinematics of giraffe at sites where lesions are found predominantly on the limbs to better assess the potential significance of GSD on their locomotion.

Comparison of Canine Forelimb Kinematic Joint Angles Collected with 2D and 3D Models.

The aim of this study was to compare a Joint Coordinate System (JCS) three-dimensional (3D) kinematic model of the canine forelimb with more widely used linear (LIN) and segmental (SEG) 2D models. It was an in vivo biomechanical study. Normal adult mixed breed dogs were used in this study (n = 6). Nineteen retroreflective markers were applied to the skin of dogs' right forelimbs. Dogs were trotted and walked through the calibrated testing space. The first five good trials were used to generate sagittal plane (flexion and extension angle) waveforms from 3 different models (JCS, LIN and SEG) for the shoulder, elbow and carpal joints. The JCS model also generated transverse and frontal plane joint angular data (internal/external and abduction/adduction angles) for all three joints. Minimum, maximum and total angular displacement was calculated for each joint. Comparison of sagittal plane waveforms was performed before and after waveform alignment using statistical parametric mapping. Each model produced similar sagittal plane waveforms, though the LIN model had a greater vertical shift along the y-axis for the shoulder and elbow. Before waveform alignment, differences were revealed between the LIN model when compared to JCS or SEG model at a trot. No differences were revealed at a walk. After waveform alignment, no differences were revealed between models at a walk or trot. There were no differences in angular displacement measurements between models before or after waveform alignment at a walk or trot. The 3D JCS model reported in this study produced sagittal plane waveforms comparable to conventional 2D models while also providing joint specific information from other planes of motion.

An increase in black hole activity in galaxies with kinematically misaligned gas

External accretion events such as a galaxy merger or the accretion of gas from the immediate environment of a galaxy can create a large misalignment between the gas and the stellar kinematics. Numerical simulations have suggested that misaligned structures may promote the inflow of gas to the nucleus of the galaxy and the accretion of gas by the central supermassive black hole. We show for the first time that galaxies with a strong misalignment between the ionized gas and stellar kinematic angles have a higher observed fraction of active black holes than galaxies with aligned rotation of gas and stars. The increase in black hole activity suggests that the process of formation and/or the presence of misaligned structures are connected with the fuelling of active supermassive black holes.

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

Application of a Joint Coordinate System Kinematic Model to the Canine Thoracic Limb.

The aim of this study was to apply a three-dimensional kinematic model to the canine thoracic limb using a joint coordinate system. Six clinically normal adult mixed-breed dogs. Dogs had 19 retroreflective markers affixed to the skin of the right forelimb. Twelve infrared cameras were arranged in a circle around the testing space, recording the locations of the markers as dogs walked and trotted through the testing space. Five trials were used of both walks and trots at velocities 0.9 to 1.2 m/s and 1.7 to 2.1 m/s respectively. Raw marker location data were used to generate a joint coordinate system, and a six degrees of freedom model of the canine forelimb was created. Three-dimensional kinematic angles were collected for the shoulder, elbow and carpal joints. Sagittal, transverse and frontal plane kinematics joint angles were generated by use of a joint coordinate system. Range of motion was calculated for each joint in all three planes. This minimally invasive joint coordinate system model can be used in both clinical and research settings to determine changes in range of motion of the shoulder, elbow or carpus in the canine forelimb in three dimensions.

Differences in motor response to stability perturbations limit fall-resisting skill transfer

This study investigated transfer of improvements in stability recovery performance to novel perturbations. Thirty adults (20-53 yr) were assigned equally to three treadmill walking groups: groups exposed to eight trip perturbations of either low or high magnitude and a third control group that walked unperturbed. Following treadmill walking, participants were exposed to stability loss from a forward-inclined position (lean-and-release) and an overground trip. Lower limb joint kinematics for the swing phase of recovery steps was compared for the three tasks using statistical parametric mapping and recovery performance was analysed by margin of stability and base of support. The perturbation groups improved stability (greater margin of stability) over the eight gait perturbations. There was no group effect for stability recovery in lean-and-release. For the overground trip, both perturbation groups showed similar enhanced stability recovery (margin of stability and base of support) compared to controls. Differences in joint angle kinematics between treadmill-perturbation and lean-and-release were more prolonged and greater than between the two gait perturbation tasks. This study indicates that: (i) practising stability control enhances human resilience to novel perturbations; (ii) enhancement is not necessarily dependent on perturbation magnitude; (iii) differences in motor response patterns between tasks may limit transfer.

The Bricard Linkage Network Constructed by Bennett Linkage: Design Method and Kinematic Analysis

The paper focuses on a Bricard Linkage network constructed by Bennett Linkage and discusses the possibility of connecting Bricard linkage using Bennett linkage as the connection. To simplify the problem, accessible geometric parameters are set without much loss of generality. This new design can also evolve into some new mechanisms by connecting the linkages in different ways. Based on the designed connection idea, the corresponding three-dimensional model is created by Onshape. Using Onshape, the movement and deployment of each mechanism could be observed. Then the kinematic analysis is carried out with Adams software, to find out how the kinematic angles vary as time increases. A physical model is made to observe the deployment process more clearly, indicating a potential application of a retractable table. Since the problem has been simplified by setting accessible parameters, the mechanism may show some further performance worthy of being explored if the parameters are more complicated.