[Design and support performance evaluation of medical multi-position auxiliary support exoskeleton mechanism]

Aiming at the status of muscle and joint damage caused on surgeons keeping surgical posture for a long time, this paper designs a medical multi-position auxiliary support exoskeleton with multi-joint mechanism by analyzing the surgical postures and conducting conformational studies on different joints respectively. Then by establishing a human-machine static model, this study obtains the joint torque and joint force before and after the human body wears the exoskeleton, and calibrates the strength of the exoskeleton with finite element analysis software. The results show that the maximum stress of the exoskeleton is less than the material strength requirements, the overall deformation is small, and the structural strength of the exoskeleton meets the use requirements. Finally, in this study, subjects were selected to participate in the plantar pressure test and biomechanical simulation with the man-machine static model, and the results were analyzed in terms of plantar pressure, joint torque and joint force, muscle force and overall muscle metabolism to assess the exoskeleton support performance. The results show that the exoskeleton has better support for the whole body and can reduce the musculoskeletal burden. The exoskeleton mechanism in this study better matches the actual working needs of surgeons and provides a new paradigm for the design of medical support exoskeleton mechanism.

Computational modelling of articular joints with biphasic cartilage: recent advances, challenges and opportunities

Biphasic models have been widely used to simulate the time-dependent biomechanical response of soft tissues. Modelling techniques of joints with biphasic weight-bearing soft tissues have been markedly improved over the last decade, enhancing our understanding of the function, degenerative mechanism and outcomes of interventions of joints. This paper reviews the recent advances, challenges and opportunities in computational models of joints with biphasic weight-bearing soft tissues. The review begins with an introduction of the function and degeneration of joints from a biomechanical aspect. Different constitutive models of articular cartilage, in particular biphasic materials, are illustrated in the context of the study of contact mechanics in joints. Approaches, advances and major findings of biphasic models of the hip and knee are presented, followed by a discussion of the challenges awaiting to be addressed, including the convergence issue, high computational cost and inadequate validation. Finally, opportunities and clinical insights in the areas of subject-specific modeling and tissue engineering are provided and discussed.

Determining the effects of AR/VR HMD design parameters (mass and inertia) on cervical spine joint torques

This study aimed to determine gravitational and dynamic torques and muscle activity of the neck across a series of design parameters of head mounted displays (mass, center of mass, and counterweights) associated with virtual and augmented reality (VR/AR). Twenty young adult participants completed five movement types (Slow and Fast Flexion/Extension and Rotation, and Search) while wearing a custom-designed prototype headset that varied the three design parameters: display mass (0, 200, 500, and 750 g), distance of the display's center of mass in front of the eyes (approximately 1, 3, and 5 cm anteriorly), and counterweights of 0, 166, 332, and 500 g to balance the display mass of 500 g at 7 cm. Inverse dynamics of a link segment model of the head and headset provided estimates of the torques about the joint between the skull and the occiput-first cervical vertebrae (OC1) and joint between the C7 and T1 vertebrae (C7). Surface electromyography (EMG) measured bilateral muscle activity of the splenius and upper trapezius muscles. Adding 750 g of display mass nearly doubled root mean square joint torques across all movement types. Increasing the distance of the display mass in front of the eyes by 4 cm increased torques about OC1 for the Slow and Fast Rotation and Search movements by approximately 20%. Adding a counterweight decreased torques about OC1 during the rotation and search tasks but did not decrease the torques experienced in the lower cervical spine (C7). For the flexion/extension axis, the magnitude of the dynamic torque component was 20% or less of the total torque experienced whereas for the rotation axis the magnitude of the dynamic torque component was greater than 50% of the total torque. Surface EMG root mean square values significantly varied across movement types with the fast rotation having the largest values; however, they did not vary significantly across the headset configurations.

Prepared for landing: A simple activation strategy scales muscle force to landing height

Before landing from a jump or fall, animals preactivate muscles to stiffen their limb joints but it is unclear how muscles tune limb stiffness and how collision forcefulness is anticipated. We measured electromyography and force from the lateral gastrocnemius muscle during landings in turkeys, an animal model that allows for direct measurements of muscle force. Many studies of landings in humans and other animals have found the duration of muscle preactivation to be constant, starting approximately 100 ms before impact, irrespective of fall duration. Therefore, we hypothesized a lack of relationship between fall duration (as dictated by drop height), muscle activity onset-time, and force at toe-down. Contrary to our expectations, both muscle activity and force rose from briefly after fall initiation until toe-down. Preactivation duration was proportional to fall height, while the rate of force rise was consistent across drop heights, resulting in force at landing and leg stiffness being proportional to fall height. Onset of muscle activity lagged 22 ± 7 ms (mean ± S.E.M.) from fall initiation, consistent with a reflex response initiation of the force ramp-up. Together, our results suggest that a constant (clock-like) rate of motor unit recruitment, initiated at fall initiation provides a preactivation that is proportional to drop height. The result is a tuning of pre-landing muscle force, providing a limb stiffening that is proportional to impact intensity, possibly without using information about fall distance.

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.

Measuring the global mechanical properties of the human thorax: Costo-vertebral articulation

Biomechanical simulation of the human thorax, e.g. for 3D-printed rib implant optimisation, requires an accurate knowledge of the associated articulation and tissue stiffness. The present study is focusing on determining the stiffness of the costo-vertebral articulations. Specimens of rib segments including the adjacent thoracic vertebrae and ligaments were obtained from two human post-mortem bodies at four different rib levels. The rib samples were loaded with a tensile force in the local longitudinal, sagittal and transverse direction and the resulting displacement was continuously measured. The moment-angle response of the rib articulations was also determined by applying a load at the rib end in the cranial - caudal direction and measuring the resulting displacement. The torsional load response of the costo-vertebral articulations at an applied moment between -0.1 Nm and 0.1 Nm corresponded to a median range of motion of 13.2° (6.4° to 20.9°). An almost uniform stiffness was measured in all tensile loading directions. The median displacement at the defined force of 28 N was 1.41 mm in the longitudinal, 1.55 mm in the sagittal, and 1.08 mm in the transverse direction. The measured moment-angle response of the costo-vertebral articulation is in line with the data from literature. On the contrary, larger displacements in longitudinal, sagittal and transverse directions were measured compared to the values found in literature.

Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression

The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below 15%, most Pearson correlation coefficient (R) values exceed 0.85, and most decisive factor (R2) values surpass 0.75. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs.

Kinematic characteristics of canine hindlimb movement during sit-to-stand and stand-to-sit motions

Sit-to-stand and stand-to-sit motions are basic motions for daily animal life, and these motions are used as therapeutic exercises for dogs with functional impairments. The sit-to-stand motion is divided into several phases for kinesiological assessment in human rehabilitation and physical therapy. However, these motions in dogs have not been characterized in detail. We examined canine hindlimb kinematic characteristics during sit-to-stand/stand-to-sit motions and compared the characteristics with those during walking. In addition, we tried to classify phases of the movements based on kinematic characteristics of the transition of the range of motion of the hindlimb. We used a three-dimensional motion analysis system to evaluate the motions of eight clinically healthy beagles. During the sit-to-stand motion, the total range of motion (ROM) in the hip joint flexion/extension was half of that of during walking, but the total ROM of the hindlimb external/internal rotation relative to the pelvis and flexion/extension of the stifle and the tarsal joints were significantly larger than those of walking, suggesting that sit-to-stand exercise causes movements of hindlimb joints without marked changes in hip joint flexion/extension movement. Both sit-to-stand and stand-to-sit motions could not be divided into multiple phases only by the transition of the range of motion of the hindlimb.

Biomechanical Load of Neck and Lumbar Joints in Open-Surgery Training

The prevalence of musculoskeletal symptoms (MSS) like neck and back pain is high among open-surgery surgeons. Prolonged working in the same posture and unfavourable postures are biomechanical risk factors for developing MSS. Ergonomic devices such as exoskeletons are possible solutions that can reduce muscle and joint load. To design effective exoskeletons for surgeons, one needs to quantify which neck and trunk postures are seen and how much support during actual surgery is required. Hence, this study aimed to establish the biomechanical profile of neck and trunk postures and neck and lumbar joint loads during open surgery (training). Eight surgical trainees volunteered to participate in this research. Neck and trunk segment orientations were recorded using an inertial measurement unit (IMU) system during open surgery (training). Neck and lumbar joint kinematics, joint moments and compression forces were computed using OpenSim modelling software and a musculoskeletal model. Histograms were used to illustrate the joint angle and load distribution of the neck and lumbar joints over time. During open surgery, the neck flexion angle was 71.6% of the total duration in the range of 10~40 degrees, and lumbar flexion was 68.9% of the duration in the range of 10~30 degrees. The normalized neck and lumbar flexion moments were 53.8% and 35.5% of the time in the range of 0.04~0.06 Nm/kg and 0.4~0.6 Nm/kg, respectively. Furthermore, the neck and lumbar compression forces were 32.9% and 38.2% of the time in the range of 2.0~2.5 N/kg and 15~20 N/kg, respectively. In contrast to exoskeletons used for heavy lifting tasks, exoskeletons designed for surgeons exhibit lower support torque requirements while additional degrees of freedom (DOF) are needed to accommodate combinations of neck and trunk postures.

Kinematic Motor Synergy Analysis to Understand Lock Dance Choreographies

Lock dance, or locking, is one of the popular old-school street dance styles featuring sharp, sudden, and isolated body movements through intricate control and coordination of joints and muscles. This work aims to understand the complex lock dance motions based on kinematic motor synergy analysis. Lock dance motions performed by three experienced dancers were measured with a markerless human motion capture technique. The motor synergies were identified and summarized using principle component analysis (PCA). The motion complexity, joint contributions, and motor coordination of ten basic lock dance choreographies were analyzed based on the synergy patterns and their activations. The results enhance our understanding of complex dance motions and serve as a step toward future applications to, e.g. dance skill or injury risk assessments.

Dose-response effect of incremental lateral-wedge hardness on the lower limb Biomechanics during typical badminton footwork

Badminton footwork has been characterised with jump-landing, cross step, side side and lunges, which requires movement agility to facilitate on-court performance. A novel badminton shoe design with systematic increase of lateral wedge hardness (Asker C value of 55, 60, 65, and 70) was developed and investigated in this study, aiming to analyse the dose-response effect of incremental wedge hardness on typical badminton footwork. Stance time and joint stiffness were employed to investigate the footwork performance, and the factorial Statistical non-Parametric Mapping and Principal Component Analysis (PCA) were used to quantify the biomechanical responses over the stance. As reported, shorter contact times (decreased by 8.9%-13.5%) and increased joint stiffness (in side step) of foot-ankle complex were found, suggesting improved footwork stability and agility from increased hardness. Time-varying differences were noted during the initial landing and driving-off phase of cross and side steps and drive-off returning of lunges, suggesting facilitated footwork performance. The reconstructed modes of variations from PCA further deciphered the biomechanical response to the wedge dosage, especially during drive-off, to understand the improved footwork agility and stability.

SE-TCN network for continuous estimation of upper limb joint angles

The maturity of human-computer interaction technology has made it possible to use surface electromyographic signals (sEMG) to control exoskeleton robots and intelligent prostheses. However, the available upper limb rehabilitation robots controlled by sEMG have the shortcoming of inflexible joints. This paper proposes a method based on a temporal convolutional network (TCN) to predict upper limb joint angles by sEMG. The raw TCN depth was expanded to extract the temporal features and save the original information. The timing sequence characteristics of the muscle blocks that dominate the upper limb movement are not apparent, leading to low accuracy of the joint angle estimation. Therefore, this study squeeze-and-excitation networks (SE-Net) to improve the network model of the TCN. Finally, seven movements of the human upper limb were selected for ten human subjects, recording elbow angle (EA), shoulder vertical angle (SVA), and shoulder horizontal angle (SHA) values during their movements. The designed experiment compared the proposed SE-TCN model with the backpropagation (BP) and long short-term memory (LSTM) networks. The proposed SE-TCN systematically outperformed the BP network and LSTM model by the mean RMSE values: by 25.0 and 36.8% for EA, by 38.6 and 43.6% for SHA, and by 45.6 and 49.5% for SVA, respectively. Consequently, its R² values exceeded those of BP and LSTM by 13.6 and 39.20% for EA, 19.01 and 31.72% for SHA, and 29.22 and 31.89% for SVA, respectively. This indicates that the proposed SE-TCN model has good accuracy and can be used to estimate the angles of upper limb rehabilitation robots in the future.

A test of the effort equalization hypothesis in children with cerebral palsy who have an asymmetric gait

Healthy people can walk nearly effortlessly thanks to their instinctively adaptive gait patterns that tend to minimize metabolic energy consumption. However, the economy of gait is severely impaired in many neurological disorders such as stroke or cerebral palsy (CP). Moreover, self-selected asymmetry of impaired gait does not seem to unequivocally coincide with the minimal energy cost, suggesting the presence of other adaptive origins. Here, we used hemiparetic CP gait as a model to test the hypothesis that pathological asymmetric gait patterns are chosen to equalize the relative muscle efforts between the affected and unaffected limbs. We determined the relative muscle efforts for the ankle and knee extensors by relating extensor joint moments during gait to maximum moments obtained from all-out hopping reference test. During asymmetric CP gait, the unaffected limb generated greater ankle (1.36±0.15 vs 1.17±0.16 Nm/kg, p = 0.002) and knee (0.74±0.33 vs 0.44±0.19 Nm/kg, p = 0.007) extensor moments compared with the affected limb. Similarly, the maximum moment generation capacity was greater in the unaffected limb versus the affected limb (ankle extensors: 1.81±0.39 Nm/kg vs 1.51±0.34 Nm/kg, p = 0.033; knee extensors: 1.83±0.37 Nm/kg vs 1.34±0.38 Nm/kg, p = 0.021) in our force reference test. As a consequence, no differences were found in the relative efforts between unaffected and affected limb ankle extensors (77±12% vs 80±16%, p = 0.69) and knee extensors (41±17% vs 38±23%, p = 0.54). In conclusion, asymmetric CP gait resulted in similar relative muscle efforts between affected and unaffected limbs. The tendency for effort equalization may thus be an important driver of self-selected gait asymmetry patterns, and consequently advantageous for preventing fatigue of the weaker affected side musculature.

Advances in Engineered Three-Dimensional (3D) Body Articulation Unit Models

Indeed, the body articulation units, commonly referred to as body joints, play significant roles in the musculoskeletal system, enabling body flexibility. Nevertheless, these articulation units suffer from several pathological conditions, such as osteoarthritis (OA), rheumatoid arthritis (RA), ankylosing spondylitis, gout, and psoriatic arthritis. There exist several treatment modalities based on the utilization of anti-inflammatory and analgesic drugs, which can reduce or control the pathophysiological symptoms. Despite the success, these treatment modalities suffer from major shortcomings of enormous cost and poor recovery, limiting their applicability and requiring promising strategies. To address these limitations, several engineering strategies have been emerged as promising solutions in fabricating the body articulation as unit models towards local articulation repair for tissue regeneration and high-throughput screening for drug development. In this article, we present challenges related to the selection of biomaterials (natural and synthetic sources), construction of 3D articulation models (scaffold-free, scaffold-based, and organ-on-a-chip), architectural designs (microfluidics, bioprinting, electrospinning, and biomineralization), and the type of culture conditions (growth factors and active peptides). Then, we emphasize the applicability of these articulation units for emerging biomedical applications of drug screening and tissue repair/regeneration. In conclusion, we put forward the challenges and difficulties for the further clinical application of the in vitro 3D articulation unit models in terms of the long-term high activity of the models.

Joint Simon effect in movement trajectories

In joint action literature it is often assumed that acting together is driven by pervasive and automatic process of co-representation, that is, representing the co-actor's part of the task in addition to one's own. Much of this research employs joint stimulus-response compatibility tasks varying the stimuli employed or the physical and social relations between participants. In this study we test the robustness of co-representation effects by focusing instead on variation in response modality. Specifically, we implement a mouse-tracking version of a Joint Simon Task in which participants respond by producing continuous movements with a computer mouse rather than pushing discrete buttons. We have three key findings. First, in a replication of an earlier study we show that in a classical individual Simon Task movement trajectories show greater curvature on incongruent trials, paralleling longer response times. Second, this effect largely disappears in a Go-NoGo Simon Task, in which participants respond to only one of the cues and refrain from responding to the other. Third, contrary to previous studies that use button pressing responses, we observe no overall effect in the joint variants of the task. However, we also detect a notable diversity in movement strategies adopted by the participants, with some participants showing the effect on the individual level. Our study casts doubt on the pervasiveness of co-representation, highlights the usefulness of mouse-tracking methodology and emphasizes the need for looking at individual variation in task performance.

Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction with an OpenSim Musculoskeletal Model

OBJECTIVE

In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities.

METHODS

First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out.

RESULTS

Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model.

CONCLUSION

The proposed hybrid model combines the skeletal models rapid motion prediction with OpenSims complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.

Kinematics and joints moments profile during straight arm press to handstand in male gymnasts

Biomechanical features of the handstand, one of the most fundamental skills required for artistic gymnastics events, have not been well documented. The purpose of this study was to clarify the kinematics and joint moment profiles during straight arm press to handstand in different highly skilled male gymnasts. Fifty-nine male gymnasts performed a straight arm press to handstand on a force platform and were judged on their performance by experienced certified judges. Subjects were divided into two groups (highly-skilled and less-skilled). Kinematic data were obtained using a video camera synchronized with force platform. Joint moments (wrist, shoulder, hip) during each straight arm press to handstand were calculated using the inverse dynamics solution. Larger shoulder flexion moments were observed in less-skilled compared with highly- skilled performers (at 3-59%, p < 0.001) while larger hip flexion moments were observed in highly- skilled performers at 52% (p = 0.045) and 56% (p = 0.048) and normalized time of straight arm press to handstand. Major differences between highly-skilled and less-skilled performers were observed in hip joint moment production as it shifted from extension to flexion from the leg horizontal position to the handstand position in highly-skilled gymnasts. Successful straight arm press to handstand techniques observed in highly-skilled performers were characterized as a more acute pike position at toe-off as well as hip flexor moments at latter phase of the straight arm press to handstand.

A biomarker perspective on the acute effect of exercise with and without impact on joint tissue turnover: an exploratory randomized cross-over study

PURPOSE

To investigate acute changes in biochemical markers of bone and cartilage turnover in response to moderate intensity exercise with and without joint impact in healthy human subjects.

METHODS

A randomized, cross-over, exploratory, clinical study was conducted. Twenty healthy subjects with no history of joint trauma completed 30 min interventions of standardized moderate intensity cycling and running as well as a resting intervention 1 week apart. Blood samples were taken immediately before, four times after exercise and again the next day. Urine was sampled, before, after and the next day. On the day of rest, samples were taken at timepoints similar to the days of exercise. Markers of type I (CTX-I), II (C2M, CTX-II) and VI (C6M) collagen degradation, cartilage oligomeric matrix protein (COMP) and procollagen C-2 (PRO-C2) was measured.

TRIAL REGISTRATION NUMBER

NCT04542655, 02 September 2020, retrospectively registered.

RESULTS

CTX-I was different from cycling (4.2%, 95%CI: 0.4-8.0%, p = 0.03) and resting (6.8%, 95%CI: 2.9-10.7%, p = 0.001) after running and the mean change in COMP was different from cycling (10.3%, 95%CI: 1.1-19.5%, p = 0.03), but not from resting (8.6%, 95%CI: - 0.7-17.8%, p = 0.07) after running. Overall, changes in other biomarkers were not different between interventions.

CONCLUSION

In this exploratory study, running, but not cycling, at a moderate intensity and duration induced acute changes in biomarkers of bone and cartilage extra-cellular matrix turnover.

2-D forward dynamics simulation of gait adaptation to muscle weakness in elderly gait

BACKGROUND

Age-related changes of human gait characteristics associated with muscle weakness have been reported in previous studies. Human gait is considered as a cyclic motion adapted to individual body-characteristics and the surrounding-environment based on motion criteria. Based on this hypothesis, elderly gait characteristics may be caused by an adaptation to muscle weakness.

RESEARCH QUESTION

What role does gait adaptation to muscle weakness play in the development of elderly gait, and what criteria are used in elderly gait adaptation?

METHODS

We examined the effects of gait adaptation to muscle weakness on steady gait characteristics using computational forward dynamics simulation with a two-dimensional neuro-musculo-skeletal model. For gait adaptation, we tested two motion criteria: (i) energy cost minimization, which is a widely used criterion for healthy adults; and (ii) energy rate minimization, based on existing measurements of elderly gait characteristics.

RESULTS

Progression of muscle weakness enhanced the reduction of joint angle motion and minimum toe clearance, and finally resulted in falling. Gait adaptation to muscle weakness successfully formed stable walking patterns regardless of motion criteria, even at muscle weakness of 30 %, which represents a moderate degree of elderly muscle weakness. When criterion (i) was used, the time courses of joint motion were similar to those of healthy adults and a relatively high level of muscle activation was found in the whole gait cycle to compensate for muscle weakness. When criterion (ii) was used, the muscle activity level was lower than that of criterion (i) to minimize the energy rate, and the constructed gait successfully captured the characteristics of elderly gait reported in previous studies.

SIGNIFICANCE

These findings suggest that gait adaptation to muscle weakness plays an essential role in the development of stable gait characteristics, whereas elderly people might use a different motion criterion compared with healthy adults in gait adaptation.

A new role for joint mobility in reconstructing vertebrate locomotor evolution

Reconstructions of movement in extinct animals are critical to our understanding of major transformations in vertebrate locomotor evolution. Estimates of joint range of motion (ROM) have long been used to exclude anatomically impossible joint poses from hypothesized gait cycles. Here we demonstrate how comparative ROM data can be harnessed in a different way to better constrain locomotor reconstructions. As a case study, we measured nearly 600,000 poses from the hindlimb joints of the Helmeted Guineafowl and American alligator, which represent an extant phylogenetic bracket for the archosaurian ancestor and its pseudosuchian (crocodilian line) and ornithodiran (bird line) descendants. We then used joint mobility mapping to search for a consistent relationship between full potential joint mobility and the subset of joint poses used during locomotion. We found that walking and running poses are predictably located within full mobility, revealing additional constraints for reconstructions of extinct archosaurs. The inferential framework that we develop here can be expanded to identify ROM-based constraints for other animals and, in turn, will help to unravel the history of vertebrate locomotor evolution.

Comparison of multidirectional jump performance and lower limb passive range of motion profile between soccer and basketball young players

This study was performed aimed at comparing multidirectional bilateral and unilateral jump performance and passive range of motion (ROM) of lower limbs between soccer and basketball young players and evaluating associations between inter-limb ROM asymmetry and bilateral jump performance. A total of 67 young male athletes participated in this study, who were classified as soccer (n = 40; 15.55 ± 1.5 y; 1.76 ± 0.12 m; 58.15 ± 10.82 kg; 19.84 ± 2.98 kg·m2) and basketball (n = 27; 15.7 ± 1.66 y; 1.76 ± 0.12 m; 62.33 ± 16.57 kg; 19.84 ± 2.98 kg·m2) players. Participants were asked to perform bilateral and unilateral multidirectional jumps, and passive ROM of hip (flexion, extension and abduction), knee (flexion) and ankle (dorsiflexion) joints was also assessed. Significant between-group differences were observed for hip extension with flexed knee ROM in dominant (soccer: 142.43 ± 7.74°; basketball: 148.63 ± 8.10°) and non-dominant (soccer: 144.38 ± 8.36°; basketball: 148.63 ± 6.45°) legs; hip flexion with flexed knee ROM in dominant (soccer: 13.26 ± 4.71°; basketball: 9.96 ± 3.42°) and non-dominant (soccer: 12.86 ± 4.55°; basketball: 9.70 ± 3.62°) legs; and for the ratio of hip abduction (soccer: 1.02 ± 0.08; basketball: 0.97 ± 0.11). However, no significant between-group differences were observed for bilateral and unilateral jump capacity, or for inter-limb asymmetries (dominant vs. non-dominant leg). Finally, no associations were observed between ROM ratio (dominant vs. non-dominant leg) and bilateral jump performance. These findings lead to the suggestion that differences on passive ROM values in young male athletes may be sport-specific. Additionally, there seems to be need for the implementation of training strategies specifically aimed at improving bilateral or unilateral jump ability, or at diminishing inter limb passive ROM differences in order to improve multidirectional jump performance for neither soccer nor basketball youth male players.

Isokinetic Testing: Sensitivity of the Force-Velocity Relationship Assessed through the Two-Point Method to Discriminate between Muscle Groups and Participants' Physical Activity Levels

BACKGROUND

Isokinetic testing has been routinely used to assess the capacities of individual muscle groups. In this study we aimed to evaluate the sensitivity of the force-point (F-v) relationship assessed through the two-point method to discriminate between antagonist muscle groups and males with different physical activity levels.

METHODS

The concentric force output of the knee, hip, elbow, and shoulder flexors and extensors of 27 active and 13 non-active men was recorded at 60 and 180°/s to determine the F-v relationship parameters (maximum force [F₀], maximum velocity [v₀], and maximum power [P_max]).

RESULTS

F₀ and P_max were higher for knee extensors (effect size [ES] = 1.97 and 0.57, respectively), hip extensors (ES = 2.52 and 0.77, respectively), and shoulder flexors (ES = 1.67 and 0.83, respectively) compared to their antagonist muscles, while v₀ was higher for knee flexors compared to knee extensors (ES = 0.59). Active males revealed higher F₀ for knee extensors (ES = 0.72) and knee flexors (ES = 0.83) and higher P_max for knee flexors (ES = 0.70), elbow extensors (ES = 0.83) and shoulder extensors (ES = 0.36).

CONCLUSIONS

The sensitivity of the two-point method for testing the maximal mechanical capacities was high for the knee, moderate for the hip and shoulder, and low for the elbow joint.

Adjustments in end-effector trajectory and underlying joint angle synergies after a target switch: Order of adjustment is flexible

Goal-directed reaching adapts to meet changing task requirements after unexpected perturbations such as a sudden switch of target location. Literature on adaptive behavior using a target switch has primarily focused on adjustments of the end-effector trajectory, addressing proposed feedback and feedforward processes in planning adjusted actions. Starting from a dynamical systems approach to motor coordination, the current paper focusses on coordination of joint angles after a target switch, which has received little attention in the literature. We argue that joint angles are coordinated in synergies, temporary task-specific units emerging from interactions amongst task, organism, and environmental constraints. We asked whether after a target switch: i) joint angles were coordinated in synergies, ii) joint angles were coordinated in a different synergy than the synergy used when moving to the original target, and iii) synergies or end-effector trajectory was adjusted first. Participants (N = 12) performed manual reaching movements toward a target on a table (stationary target trials), where in some trials the target could unexpectedly switch to a new location (switch trials). Results showed that the end-effector curved to the switched target. Joint angles were synergistically organized as shown by the large extent of co-variation based on Uncontrolled Manifold analyses. At the end of the target switch movement, joint angle configurations differed from the joint angle configurations used to move to the original stationary target. Hence, we argue, a new synergy emerged after the target switch. The order of adjustment in the synergies and in the end-effector was flexible within participants, though most often synergies were adjusted first. These findings support the two-step framework of Kay (1988) to understand the coordination of abundant degrees of freedom and to explain adaptive actions. The flexibility in the order of adjustments of synergies suggests that the coordination of DOF emerges from self-organization.

Modeling the neuro-mechanics of human balance when recovering from a fall: a continuous-time approach

BACKGROUND

Balance control deteriorates with age and nearly 30% of the elderly population in the United States reports stability problems. Postural stability is an integral task to daily living reliant upon the control of the ankle and hip. To this end, the estimation of joint parameters can be a useful tool when analyzing compensatory actions aimed at maintaining postural stability.

METHODS

Using an analytical approach, this study expands on previous work and analyzes a two degrees of freedom human model. The first two modes of vibration of the system are represented by the neuro-mechanical parameters of a second-order, time-varying Kelvin-Voigt model actuated at the ankle and hip. The model is tested using a custom double inverted pendulum and healthy volunteers who were subjected to a positional step-like perturbation during quiet standing. An in silico sensitivity analysis of the influence of inertial parameters was also performed.

RESULTS

The proposed method is able to correctly identify the time-varying visco-elastic parameters of of a double inverted pendulum. We show that that the parameter estimation method can be applied to standing humans. These results appear to identify a subject-independent strategy to control quiet standing that combines both the modulation of stiffness, and the use of an intermittent control.

CONCLUSIONS

This paper presents the analysis of the non-linear system of differential equations representing the control of lumped muscle-tendon units. It utilizes motion capture measurements to obtain the estimates of the system's control parameters by constructing a simple time-dependent regressor for estimating the time-varying parameters of the control with a single perturbation. This work is a step forward into the understanding of the neuro-mechanical control parameters of human recovering from a fall. In previous literature, the analysis is either restricted to the first vibrational mode of an inverted-pendulum model or assumed to be time-invariant. The proposed method allows for the analysis of hip related movement for stability control and highlights the importance of core training.

Whole body kinematic sex differences persist across non-dimensional gait speeds

Sex differences in human locomotion are of interest in a broad variety of interdisciplinary applications. Although kinematic sex differences have been studied for many years, the underlying reasons behind several noted differences, such as pelvis and torso range of motion, are still not well understood. Walking speed and body size in particular represent confounding influences that hinder our ability to determine causal factors. The purpose of this study was to investigate sex differences in whole body gait kinematics across a range of controlled, non-dimensional walking and running speeds. We hypothesized that as task demand (i.e. gait speed) increased, the influences of modifiable factors would decrease, leading to a kinematic motion pattern convergence between sexes. Motion capture data from forty-eight healthy young adults (24 M, 24 F) wearing controlled footwear was captured at three walking and three running Froude speeds. Spatiotemporal metrics, center of mass displacement, and joint/segment ranges of motion were compared between sexes using 2x6 mixed-model ANOVAs. Three dimensional time-series waveforms were also used to describe the time-varying behavior of select joint angles. When controlling for size, sex differences in spatiotemporal metrics and center of mass displacement disappeared. However, contrary to our hypothesis, sagittal plane ankle, frontal plane pelvis, and transverse plane pelvis and torso range of motion all displayed sex differences that persisted or increased with gait speed. Overall, most spatiotemporal sex differences appear to be related to size and self-selection of gait speeds, while in contrast, sex differences in joint motion may be more inherent and ubiquitous than previously thought. Discussion on potential causal factors is presented.

Validation of Marker-Less System for the Assessment of Upper Joints Reaction Forces in Exoskeleton Users

This paper presents the validation of a marker-less motion capture system used to evaluate the upper limb stress of subjects using exoskeletons for locomotion. The system fuses the human skeletonization provided by commercial 3D cameras with forces exchanged by the user to the ground through upper limbs utilizing instrumented crutches. The aim is to provide a low cost, accurate, and reliable technology useful to provide the trainer a quantitative evaluation of the impact of assisted gait on the subject without the need to use an instrumented gait lab. The reaction forces at the upper limbs' joints are measured to provide a validation focused on clinically relevant quantities for this application. The system was used simultaneously with a reference motion capture system inside a clinical gait analysis lab. An expert user performed 20 walking tests using instrumented crutches and force platforms inside the observed volume. The mechanical model was applied to data from the system and the reference motion capture, and numerical simulations were performed to assess the internal joint reaction of the subject's upper limbs. A comparison between the two results shows a root mean square error of less than 2% of the subject's body weight.

Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton

Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r² values were higher than those of GP.

GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connections between joints, which contain discriminative information for different actions. In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action. In addition, to filter out the useless and redundant information in the temporal dimension, we propose a simple yet effective operation named gated temporal convolution. These two major novelties ensure the superiority of our proposed method, as demonstrated on three large-scale public datasets: NTU-RGB + D, Kinetics, and NTU-RGB + D 120, and also shown in the detailed ablation studies.

Real-Time Human Action Recognition with a Low-Cost RGB Camera and Mobile Robot Platform

Human action recognition is an important research area in the field of computer vision that can be applied in surveillance, assisted living, and robotic systems interacting with people. Although various approaches have been widely used, recent studies have mainly focused on deep-learning networks using Kinect camera that can easily generate data on skeleton joints using depth data, and have achieved satisfactory performances. However, their models are deep and complex to achieve a higher recognition score; therefore, they cannot be applied to a mobile robot platform using a Kinect camera. To overcome these limitations, we suggest a method to classify human actions in real-time using a single RGB camera, which can be applied to the mobile robot platform as well. We integrated two open-source libraries, i.e., OpenPose and 3D-baseline, to extract skeleton joints on RGB images, and classified the actions using convolutional neural networks. Finally, we set up the mobile robot platform including an NVIDIA JETSON XAVIER embedded board and tracking algorithm to monitor a person continuously. We achieved an accuracy of 70% on the NTU-RGBD training dataset, and the whole process was performed on an average of 15 frames per second (FPS) on an embedded board system.

A practical 3D-printed soft robotic prosthetic hand with multi-articulating capabilities

Soft robotic hands with monolithic structure have shown great potential to be used as prostheses due to their advantages to yield light weight and compact designs as well as its ease of manufacture. However, existing soft prosthetic hands design were often not geared towards addressing some of the practical requirements highlighted in prosthetics research. The gap between the existing designs and the practical requirements significantly hampers the potential to transfer these designs to real-world applications. This work addressed these requirements with the consideration of the trade-off between practicality and performance. These requirements were achieved through exploiting the monolithic 3D printing of soft materials which incorporates membrane enclosed flexure joints in the finger designs, synergy-based thumb motion and cable-driven actuation system in the proposed hand prosthesis. Our systematic design (tentatively named X-Limb) achieves a weight of 253gr, three grasps types (with capability of individual finger movement), power-grip force of 21.5N, finger flexion speed of 1.3sec, a minimum grasping cycles of 45,000 (while maintaining its original functionality) and a bill of material cost of 200 USD (excluding quick disconnect wrist but without factoring in the cost reduction through mass production). A standard Activities Measure for Upper-Limb Amputees benchmark test was carried out to evaluate the capability of X-Limb in performing grasping task required for activities of daily living. The results show that all the practical design requirements are satisfied, and the proposed soft prosthetic hand is able to perform all the real-world grasping tasks of the benchmark tests, showing great potential in improving life quality of individuals with upper limb loss.

Weakly Supervised Adversarial Learning for 3D Human Pose Estimation from Point Clouds

Point clouds-based 3D human pose estimation that aims to recover the 3D locations of human skeleton joints plays an important role in many AR/VR applications. The success of existing methods is generally built upon large scale data annotated with 3D human joints. However, it is a labor-intensive and error-prone process to annotate 3D human joints from input depth images or point clouds, due to the self-occlusion between body parts as well as the tedious annotation process on 3D point clouds. Meanwhile, it is easier to construct human pose datasets with 2D human joint annotations on depth images. To address this problem, we present a weakly supervised adversarial learning framework for 3D human pose estimation from point clouds. Compared to existing 3D human pose estimation methods from depth images or point clouds, we exploit both the weakly supervised data with only annotations of 2D human joints and fully supervised data with annotations of 3D human joints. In order to relieve the human pose ambiguity due to weak supervision, we adopt adversarial learning to ensure the recovered human pose is valid. Instead of using either 2D or 3D representations of depth images in previous methods, we exploit both point clouds and the input depth image. We adopt 2D CNN to extract 2D human joints from the input depth image, 2D human joints aid us in obtaining the initial 3D human joints and selecting effective sampling points that could reduce the computation cost of 3D human pose regression using point clouds network. The used point clouds network can narrow down the domain gap between the network input i.e. point clouds and 3D joints. Thanks to weakly supervised adversarial learning framework, our method can achieve accurate 3D human pose from point clouds. Experiments on the ITOP dataset and EVAL dataset demonstrate that our method can achieve state-of-the-art performance efficiently.

A Game-Based Rehabilitation System for Upper-Limb Cerebral Palsy: A Feasibility Study

Game-based rehabilitation systems provide an effective tool to engage cerebral palsy patients in physical exercises within an exciting and entertaining environment. A crucial factor to ensure the effectiveness of game-based rehabilitation systems is to assess the correctness of the movements performed by the patient during the game-playing sessions. In this study, we propose a game-based rehabilitation system for upper-limb cerebral palsy that includes three game-based exercises and a computerized assessment method. The game-based exercises aim to engage the participant in shoulder flexion, shoulder horizontal abduction/adduction, and shoulder adduction physical exercises that target the right arm. Human interaction with the game-based rehabilitation system is achieved using a Kinect sensor that tracks the skeleton joints of the participant. The computerized assessment method aims to assess the correctness of the right arm movements during each game-playing session by analyzing the tracking data acquired by the Kinect sensor. To evaluate the performance of the computerized assessment method, two groups of participants volunteered to participate in the game-based exercises. The first group included six cerebral palsy children and the second group included twenty typically developing subjects. For every participant, the computerized assessment method was employed to assess the correctness of the right arm movements in each game-playing session and these computer-based assessments were compared with matching gold standard evaluations provided by an experienced physiotherapist. The results reported in this study suggest the feasibility of employing the computerized assessment method to evaluate the correctness of the right arm movements during the game-playing sessions.

Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.

Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion

Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist’s motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist’s attitude information after sensor calibration, and then the motions of canoeist’s actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learning algorithm was used as the classification method to divide the stroke cycle into different phases, including propulsion-phase and recovery-phase, a quantitative kinematic analysis was carried out. Experiments conducted in this paper demonstrated that our method possesses the capacity to reveal the similarities and differences between novice and coach, the whole process of canoeist’s motions can be analyzed with satisfactory accuracy validated by videography method. It can provide quantitative data for coaches or athletes, which can be used to improve the skills of rowers.

Multi-Person Pose Estimation Using an Orientation and Occlusion Aware Deep Learning Network

Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based on Mask Region-based Convolutional Neural Networks (R-CNN) and extended to integrate the joint feature, body boundary, body orientation and occlusion condition together. In order to further improve the performance of the multi-person pose estimation, this paper proposes to organize the different information in serial multi-task models instead of the widely used parallel multi-task network. The proposed models are trained on the public dataset Common Objects in Context (COCO), which is further augmented by ground truths of body orientation and mutual-occlusion mask. Experiments demonstrate the performance of the proposed method for multi-person pose estimation and body orientation estimation. The proposed method can detect 84.6% of the Percentage of Correct Keypoints (PCK) and has an 83.7% Correct Detection Rate (CDR). Comparisons further illustrate the proposed model can reduce the over-detection compared with other methods.

Is There an Optimal Recovery Step Landing Zone Against Slip-Induced Backward Falls During Walking?

Recovery stepping in response to forward slips has the potential to not only rebuild the base of support to prevent backward falling, but also provide extra limb support to prevent downward falling. Hence, recovery stepping is often necessary for fall prevention following an unexpected slip. However, less is known about whether recovery foot placement could affect the likelihood of recovery following a slip. The purpose of this study was to determine whether there is an optimal recovery landing zone within which older adults have a higher likelihood of recovery. 195 participants experienced a novel, unannounced forward slip while walking on a 7-m walkway. The center of mass (COM) stability (computed from its position and velocity), vertical limb support (computed from change in hip kinematics), and recovery limb joint moments (computed from joint kinematics and ground reaction force) in the sagittal plane were analyzed. The results showed that a longer distance between recovery foot landing position and the projected COM position at recovery foot touchdown (relative recovery step placement) was conducive to stability improvement but adverse to limb support enhancement, and vice versa for a shorter distance. Relative recovery step placement could predict the recovery likelihood with an accuracy of 67.3%, and the recovery rate was greater than 50% when the distance between recovery foot and COM is less than 0.3 × foot length. This study also found more posterior stepping could be attributed to insufficient ankle plantar flexor and hip flexor moments in the pre-swing phase, while more anterior stepping was induced by insufficient hip and knee extensor moments in the following swing phase.

Kinematic Parameters of Topspin Forehand in Table Tennis and Their Inter- and Intra-Individual Variability

The aims of the research were to (1) determine the values of kinematic parameters in two modifications of the topspin forehand stroke as well as the differences between them and (2) assess the inter-individual and intra-individual variability of the values. Two modifications of a topspin forehand were evaluated: topspin after a topspin ball (TF1) and topspin after a backspin ball (TF2). The MyoMotion Noraxon analysis system was used to record the kinematic data. A piezo-electric sensor was used to identify the moment when the ball made contact with the racket. The coefficient of variation determined the variability of the kinematic parameters. Most of the joint angles in four identified events reflected how the individual segments of a player's body should move. The difference in acceleration at the moment of contact between the two types of the topspin forehand was significant, but the variability of the acceleration values was small. Large variability in the angular parameters was found, and this result was considered a manifestation of different coordination patterns in the stroke movements. It is possible that even though the players used different methods of performing the movement, they obtained similar values for some parameters (e.g., acceleration), which should be taken into account by coaches. There were small differences in many parameters within individual players, which can indicate that a player performs tasks in a similar way each time. However, there was high variability in some angular parameters, indicating that the repetitions of particular strokes were not performed in an identical way. The reasons for this phenomenon include movement functionality and functional variability.

Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

A stable posture requires the coordination of multiple joints of the body. This coordination of the multiple joints of the human body to maintain a stable posture is a subject of research. The number of degrees of freedom (DOFs) of the human motor system is considerably larger than the DOFs required for posture balance. The manner of managing this redundancy by the central nervous system remains unclear. To understand this phenomenon, in this study, three local inter-joint coordination pattern (IJCP) features were introduced to characterize the strength, changing velocity, and complexity of the inter-joint couplings by computing the correlation coefficients between joint velocity signal pairs. In addition, for quantifying the complexity of IJCPs from a global perspective, another set of IJCP features was introduced by performing principal component analysis on all joint velocity signals. A Microsoft Kinect depth sensor was used to acquire the motion of 15 joints of the body. The efficacy of the proposed features was tested using the captured motions of two age groups (18-24 and 65-73 years) when standing still. With regard to the redundant DOFs of the joints of the body, the experimental results suggested that an inter-joint coordination strategy intermediate to that of the two extreme coordination modes of total joint dependence and independence is used by the body. In addition, comparative statistical results of the proposed features proved that aging increases the coupling strength, decreases the changing velocity, and reduces the complexity of the IJCPs. These results also suggested that with aging, the balance strategy tends to be more joint dependent. Because of the simplicity of the proposed features and the affordability of the easy-to-use Kinect depth sensor, such an assembly can be used to collect large amounts of data to explore the potential of the proposed features in assessing the performance of the human balance control system.

Static loading of the knee joint results in modified single leg landing biomechanics

Background

External loading of the ligamentous tissues induces mechanical creep, which modifies neuromuscular response to perturbations. It is not well understood how ligamentous creep affects athletic performance and contributes to modifications of knee biomechanics during functional tasks.

Hypothesis/purpose

The purpose of this study was to examine the mechanical and neuromuscular responses to single leg drop landing perturbations before and after passive loading of the knee joint.

Methods

Descriptive laboratory study. Male (n = 7) and female (n = 14) participants’ (21.3 ± 2.1 yrs., 1.69 ± 0.09 m, 69.3 ± 13.0 kg) right hip, knee, and ankle kinematics were assessed during drop landings performed from a 30 cm height onto a force platform before and after a 10 min creep protocol. Electromyography (EMG) signals were recorded from rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), semimembranosus (SM), and biceps femoris (BF) muscles. The creep protocol involved fixing the knee joint at 35° during static loading with perpendicular loads of either 200 N (males) or 150 N (females). Maximum, minimum, range of motion (ROM), and angular velocities were assessed for the hip, knee, and ankle joints, while normalized EMG (NEMG), vertical ground reaction forces (VGRF), and rate of force development (RFD) were assessed at landing using ANOVAs. Alpha was set at 0.05.

Results

Maximum hip flexion velocity decreased (p < 0.01). Minimum knee flexion velocity increased (p < 0.02). Minimum knee ad/abduction velocity decreased (p < 0.001). Ankle ROM decreased (p < 0.001). aVGRF decreased (p < 0.02). RFD had a non-significant trend (p = 0.076). NAEMG was significant between muscle groups (p < 0.02).

Conclusion

Distinct changes in velocity parameters are attributed to the altered mechanical behavior of the knee joint tissues and may contribute to changes in the loading of the leg during landing.

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e. muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-cost Motion Capture Technique

Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) "skeleton" joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.

Independent and Joint Associations of Physical Activity and Dietary Behavior with Older Adults' Lower Limb Strength

Studies have indicated that sufficient physical activity levels and balanced dietary behavior are independently related to physical function in older populations; however, their joint association with physical function remain unclear. This study examined the independent and combined associations of sufficient physical activity and balanced selection of foods with lower limb strength among 122 older Taiwanese adults living in community (mean age: 69.9 ± 5.0 years). The assessments included accelerometer-measured moderate-to-vigorous physical activity (MVPA) and self-reported selection of foods. Lower limb strength performance was measured using the five times sit-to-stand test. Binary logistic regression analyses were performed to estimate the associations in question before and after adjusting for potential confounders. The results showed that in the adjusted model, lower limb strength had no significant independent association with either meeting the recommended level of MVPA or balanced selection of foods. Compared to older adults who neither met the recommended MVPA level nor reported a balanced selection of foods, those who conformed to both these criteria were more likely to have better lower limb strength (odds ratio = 6.28, 95% confidence interval = 1.36–29.01) after adjusting for covariates. Health promotion initiatives addressing disability prevention for older adults need to consider promoting both MVPA and food selection.

Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review

The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings.

May I Keep an Eye on Your Training? Gait Assessment Assisted by a Mobile Robot

A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from physiological gait patterns during training is important. Such immediate feedback also concerns the correct usage of forearm crutches in three-point gait. In the project ROGER, a mobile Socially Assistive Robot (SAR) to support patients after surgery in hip endoprosthetics is going to be developed. The current implementation status of the robotic application developed for the use in a real-world scenario is presented below.

Evaluation of Joint Motion Sensing Efficiency According to the Implementation Method of SWCNT-Coated Fabric Motion Sensor

The purpose of this study was to investigate the effects of the shape and attachment position of stretchable textile piezoresistive sensors coated with single-walled carbon nanotubes on their performance in measuring the joint movements of children. The requirements for fabric motion sensors suitable for children are also identified. The child subjects were instructed to wear integrated clothing with sensors of different shapes (rectangular and boat-shaped), attachment positions (at the knee and elbow joints or 4 cm below the joints). The change in voltage caused by the elongation and contraction of the fabric sensors was measured for the flexion-extension motions of the arms and legs at 60°/s (three measurements of 10 repetitions each for the 60° and 90° angles, for a total of 60 repetitions). Their reliability was verified by analyzing the agreement between the fabric motion sensors and attached acceleration sensors. The experimental results showed that the fabric motion sensor that can measure children’s arm and leg motions most effectively is the rectangular-shaped sensor attached 4 cm below the joint. In this study, we developed a textile piezoresistive sensor suitable for measuring the joint motion of children, and analyzed the shape and attachment position of the sensor on clothing suitable for motion sensing. We showed that it is possible to sense joint motions of the human body by using flexible fabric sensors integrated into clothing.

How age and surface inclination affect joint moment strategies to accelerate and decelerate individual leg joints during walking

A joint moment also causes motion at other joints of the body. This joint coupling-perspective allows more insight into two age-related phenomena during gait. First, whether increased hip kinetic output compensates for decreased ankle kinetic output during positive joint work. Second, whether preserved joint kinetic patterns during negative joint work in older age have any functional implication. Therefore, we examined how age and surface inclination affect joint moment strategies to accelerate and/or decelerate individual leg joints during walking. Healthy young (age: 22.5 ± 4.1 years, n = 18) and older (age: 76.0 ± 5.7 years, n = 22) adults walked at 1.4 m/s on a split-belt instrumented treadmill at three grades (0%, 10%, -10%). Lower-extremity moment-induced angular accelerations were calculated for the hip (0% and 10%) and knee (0% and -10%) joints. During level and uphill walking, both age groups showed comparable ankle moment-induced ipsilateral (p = 0.774) and contralateral (p = 0.047) hip accelerations, although older adults generated lower ankle moments in late stance. However, ankle moment-induced contralateral hip accelerations were smaller (p = 0.001) in an older adult subgroup (n = 13) who showed larger hip extension moments in early stance than young adults. During level and downhill walking, leg joint moment-induced knee accelerations were unaffected by age (all p > 0.05). These findings suggest that during level and uphill walking increased hip flexor mechanical output in older adults does not arise from reduced ankle moments, contrary to increased hip extensor mechanical output. Additionally, results during level and downhill walking imply that preserved eccentric knee extensor function is important in maintaining knee stabilization in older age.

Rib kinematics analysis in oblique and lateral impact tests

PURPOSE

Understanding thorax kinematics and rib breaking mechanisms in conditions of oblique and lateral impact is crucial in safety systems development. To increase knowledge level on this subject, simulation and experimental tests are necessary. The purpose of this study was to obtain single rib kinematics in the case of oblique and lateral impact conditions using numerical simulation approach.

METHODS

Two impact tests using human body model of a 50th percentile man (THUMS v4.0.1 AM50) were performed in LS-Dyna R7.1.1. Impactor was a rigid cylinder with a diameter of 152 mm, and velocity equal to 6.7 m/s. Impact angle measured to sagittal plane was 30 and 90°, respectively in oblique and lateral impact case.

RESULTS

Kinematics of ribs from 3rd to 6th were analyzed. Results shown significant similarities between oblique impact and kinematics of ribs tested in frontal impact conditions in the literature, with maximal costochondral joint displacement relatively to costovertebral joint varying from 65.4 mm (3rd rib) to 82.0 mm (5th rib). Deformation of rib in lateral impact conditions was different than during oblique impact test, with distinctive "flattening" approximately in the middle of the rib. Maximal relative displacement varies from 16.4 mm (6th rib) to 26.6 mm (5th rib) and its location depends on the analyzed rib.

CONCLUSIONS

Oblique impact scenario may be simulated for the single rib on an experimental way using set-up of the frontal impact. Experimental simulation of the lateral impact for the single rib should not use the same set-up, as the kinematics analysis showed significant differences between simulated cases.

A model-based motion capture marker location refinement approach using inverse kinematics from dynamic trials

Marker-based motion capture techniques are commonly used to measure human body kinematics. These techniques require an accurate mapping from physical marker position to model marker position. Traditional methods utilize a manual process to achieve marker positions that result in accurate tracking. In this work, we present an optimization algorithm for model marker placement to minimize marker tracking error during inverse kinematics analysis of dynamic human motion. The algorithm sequentially adjusts model marker locations in 3-D relative to the underlying rigid segment. Inverse kinematics is performed for a dynamic motion capture trial to calculate the tracking error each time a marker position is changed. The increase or decrease of the tracking error determines the search direction and number of increments for each marker coordinate. A final marker placement for the model is reached when the total search interval size for every coordinate falls below a user-defined threshold. Individual marker coordinates can be locked in place to prevent the algorithm from overcorrecting for data artifacts such as soft tissue artifact. This approach was used to refine model marker placements for eight able-bodied subjects performing walking trials at three stride frequencies. Across all subjects and stride frequencies, root mean square (RMS) tracking error decreased by 38.4% and RMS tracking error variance decreased by 53.7% on average. The resulting joint kinematics were in agreement with expected values from the literature. This approach results in realistic kinematics with marker tracking errors well below accepted thresholds while removing variance in the model-building procedure introduced by individual human tendencies.

Swaying slower reduces the destabilizing effects of a compliant surface on voluntary sway dynamics

The ability to control weight shifting (voluntary sway) is a crucial factor for stability during standing. Postural tracking of an oscillating visual target when standing on a compliant surface (e.g. foam) is a challenging weight shifting task that may alter the stability of the system and the muscle activation patterns needed to compensate for the perturbed state. The purpose of this study was to examine the effects of surface stability and sway frequency on the muscle activation of the lower limb, during visually guided voluntary postural sway. Seventeen volunteers performed a 2-min voluntary sway task in the anterior-posterior direction following with their projected center of pressure (CoP_AP) a periodically oscillating visual target on a screen. The target oscillated at a frequency of 0.25 Hz or 0.125 Hz, while the participants swayed on solid ground (stable surface) or on a foam pad (unstable surface), resulting in four experimental conditions. The electromyogram (EMG) of 13 lower limb muscles was measured and the target–CoP_AP coupling was evaluated with coherence analysis, whereas the difference in the stability of the system between the conditions was estimated by the maximum Lyapunov exponent (MLE). The results showed that slower oscillations outperformed the faster in terms of coherence and revealed greater stability. On the other hand, unstable ground resulted in an undershooting of the CoP_AP to the target and greater MLE. Regarding the EMG data, a decreased triceps surae muscle activation at the low sway frequency compared to the higher was observed, whereas swaying on foam induced higher activation on the tibialis anterior as well. It is concluded that swaying voluntarily on an unstable surface results in reduced CoP_AP and joint kinematics stability, that is accomplished by increasing the activation of the distal leg muscles, in order to compensate for this perturbation. The reduction of the sway frequency limits the effect of the unstable surface, on the head and upper body, improves the temporal component of coherence between CoP and target, whereas EMG activity is decreased. These findings might have implications in rehabilitation programs.