Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people's privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body's when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.

Running Economy in the Vertical Kilometer

New and promising variables are being developed to analyze performance and fatigue in trail running, such as mechanical power, metabolic power, metabolic cost of transport and mechanical efficiency. The aim of this study was to analyze the behavior of these variables during a real vertical kilometer field test. Fifteen trained trail runners, eleven men (from 22 to 38 years old) and four women (from 19 to 35 years old) performed a vertical kilometer with a length of 4.64 km and 835 m positive slope. During the entire race, the runners were equipped with portable gas analyzers (Cosmed K5) to assess their cardiorespiratory and metabolic responses breath by breath. Significant differences were found between top-level runners versus low-level runners in the mean values of the variables of mechanical power, metabolic power and velocity. A repeated-measures ANOVA showed significant differences between the sections, the incline and the interactions between all the analyzed variables, in addition to differences depending on the level of the runner. The variable of mechanical power can be statistically significantly predicted from metabolic power and vertical net metabolic COT. An algebraic expression was obtained to calculate the value of metabolic power. Integrating the variables of mechanical power, vertical velocity and metabolic power into phone apps and smartwatches is a new opportunity to improve performance monitoring in trail running.

Dynamical Analyses Show That Professional Archers Exhibit Tighter, Finer and More Fluid Dynamical Control Than Neophytes

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes.

Human movement patterns of farmers and forest workers from the Thailand-Myanmar border

Background: Human travel patterns play an important role in infectious disease epidemiology and ecology. Movement into geographic spaces with high transmission can lead to increased risk of acquiring infections. Pathogens can also be distributed across the landscape via human travel. Most fine scale studies of human travel patterns have been done in urban settings in wealthy nations. Research into human travel patterns in rural areas of low- and middle-income nations are useful for understanding the human components of epidemiological systems for malaria or other diseases of the rural poor. The goal of this research was to assess the feasibility of using GPS loggers to empirically measure human travel patterns in this setting, as well as to quantify differing travel patterns by age, gender, and seasonality among study participants. Methods: In this pilot study we recruited 50 rural villagers from along the Myanmar-Thailand border to carry GPS loggers for the duration of a year. The GPS loggers were programmed to take a time-stamped reading every 30 minutes. We calculated daily movement ranges and multi-day trips by age and gender. We incorporated remote sensing data to assess patterns of days and nights spent in forested or farm areas, also by age and gender. Results: Our study showed that it is feasible to use GPS devices to measure travel patterns, though we had difficulty recruiting women and management of the project was relatively intensive. We found that older adults traveled farther distances than younger adults and adult males spent more nights in farms or forests. Conclusion: The results of this study suggest that further work along these lines would be feasible in this region. Furthermore, the results from this study are useful for individual-based models of disease transmission and land use.

Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic

No studies have yet examined high-resolution shifts in the spatial patterns of human movement in Australia throughout 2020 and 2021, a period coincident with the repeated enactment and removal of varied governmental restrictions aimed at reducing community transmission of SARS-CoV-2. We compared overlapping timeseries of COVID-19 pandemic-related restrictions, epidemiological data on cases and vaccination rates, and high-resolution human movement data to characterize population-level responses to the pandemic in Australian cities. We found that restrictions on human movement and/or mandatory business closures reduced the average population-level weekly movement volumes in cities, as measured by aggregated travel time, by almost half. Of the movements that continued to occur, long movements reduced more dramatically than short movements, likely indicating that people stayed closer to home. We also found that the repeated lockdowns did not reduce their impact on human movement, but the effect of the restrictions on human movement waned as the duration of restrictions increased. Lastly, we found that after restrictions ceased, the subsequent surge in SARS-CoV-2 transmission coincided with a substantial, non-mandated drop in human movement volume. These findings have implications for public health policy makers when faced with anticipating responses to restrictions during future emergency situations.

Human locomotion over obstacles reveals real-time prediction of energy expenditure for optimized decision-making

Despite decades of evidence revealing a multitude of ways in which animals are adapted to minimize the energy cost of locomotion, little is known about how energy expenditure shapes adaptive gait over complex terrain. Here, we show that the principle of energy optimality in human locomotion can be generalized to complex task-level locomotor behaviours requiring advance decision-making and anticipatory control. Participants completed a forced-choice locomotor task requiring them to choose between discrete multi-step obstacle negotiation strategies to cross a 'hole' in the ground. By modelling and analysing mechanical energy cost of transport for preferred and non-preferred manoeuvres over a wide range of obstacle dimensions, we showed that strategy selection was predicted by relative energy cost integrated across the complete multi-step task. Vision-based remote sensing was sufficient to select the strategy associated with the lowest prospective energy cost in advance of obstacle encounter, demonstrating the capacity for energetic optimization of locomotor behaviour in the absence of online proprioceptive or chemosensory feedback mechanisms. We highlight the integrative hierarchic optimizations that are required to facilitate energetically efficient locomotion over complex terrain and propose a new behavioural level linking mechanics, remote sensing and cognition that can be leveraged to explore locomotor control and decision-making.

Lower extremity Interlimb coordination associated brain activity in young female athletes: A biomechanically instrumented neuroimaging study

Bilateral sensorimotor coordination is required for everyday activities, such as walking and sitting down/standing up from a chair. Sensorimotor coordination functional neuroimaging (fMRI) paradigms (e.g., stepping, cycling) increase activity in the sensorimotor cortex, supplementary motor area, insula, and cerebellum. Although these paradigms are designed to assay coordination, performance measures are rarely collected simultaneously with fMRI. Therefore, we aimed to identify neural correlates of lower extremity coordination using a bilateral, in-phase, multi-joint coordination task with concurrent MRI-compatible 3D motion analysis. Seventeen female athletes (15.0 ± 1.4 years) completed a bilateral, multi-joint lower-extremity coordination task during brain fMRI. Interlimb coordination was quantified from kinematic data as the correlation between peak-to-peak knee flexion cycle time between legs. Standard preprocessing and whole-brain analyses for task-based fMRI were completed in FSL, controlling for total movement cycles and neuroanatomical differences, with interlimb coordination as a covariate of interest. A clusterwise multi-comparison correction was applied at z > 3.1 and p < .05. Less interlimb coordination during the task was associated with greater activation in the posterior cingulate and precuneus (zmax  = 6.41, p < .01) and the lateral occipital cortex (zmax  = 7.55, p = .02). The inability to maintain interlimb coordination alongside greater activity in attention- and sensory-related brain regions may indicate a failed compensatory neural strategy to execute the task. Alternatively, greater activity could be secondary to reduced afferent acuity that may be elevating central demand to maintain in-phase lower extremity motor coordination. Future research aiming to improve sensorimotor coordination should consider interventional approaches uniquely capable of promoting adaptive neuroplasticity to enhance motor control.

Considerations for Applying Entropy Methods to Temporally Correlated Stochastic Datasets

The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative of a broad range of biomechanical and physiological variables. To simulate a variety of processes encountered in biomechanical applications, autoregressive fractionally integrated moving averaged (ARFIMA) models were used to produce temporally correlated data spanning the fractional Gaussian noise/fractional Brownian motion model. We then applied ARFIMA modeling and SampEn to the datasets to quantify the temporal correlations and regularity of the simulated datasets. We demonstrate the use of ARFIMA modeling for estimating temporal correlation properties and classifying stochastic datasets as stationary or nonstationary. We then leverage ARFIMA modeling to improve the effectiveness of data cleaning procedures and mitigate the influence of outliers on SampEn estimates. We also emphasize the limitations of SampEn to distinguish among stochastic datasets and suggest the use of complementary measures to better characterize the dynamics of biomechanical variables. Finally, we demonstrate that parameter normalization is not an effective procedure for increasing the interoperability of SampEn estimates, at least not for entirely stochastic datasets.

Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences

Detrended Fluctuation Analysis (DFA) is the most popular fractal analytical technique used to evaluate the strength of long-range correlations in empirical time series in terms of the Hurst exponent, H. Specifically, DFA quantifies the linear regression slope in log-log coordinates representing the relationship between the time series' variability and the number of timescales over which this variability is computed. We compared the performance of two methods of fractal analysis-the current gold standard, DFA, and a Bayesian method that is not currently well-known in behavioral sciences: the Hurst-Kolmogorov (HK) method-in estimating the Hurst exponent of synthetic and empirical time series. Simulations demonstrate that the HK method consistently outperforms DFA in three important ways. The HK method: (i) accurately assesses long-range correlations when the measurement time series is short, (ii) shows minimal dispersion about the central tendency, and (iii) yields a point estimate that does not depend on the length of the measurement time series or its underlying Hurst exponent. Comparing the two methods using empirical time series from multiple settings further supports these findings. We conclude that applying DFA to synthetic time series and empirical time series during brief trials is unreliable and encourage the systematic application of the HK method to assess the Hurst exponent of empirical time series in behavioral sciences.

Single-Leg Hop Stabilization Throughout Concussion Recovery: A Preliminary Biomechanical Assessment

CONTEXT

Aberrant movement patterns among individuals with concussion history have been reported during sport-related movement. However, the acute postconcussion kinematic and kinetic biomechanical movement patterns during a rapid acceleration-deceleration task have not been profiled and leaves their progressive trajectory unknown. Our study aimed to examine single-leg hop stabilization kinematics and kinetics between concussed and healthy-matched controls acutely (≤7 d) and when asymptomatic (≤72 h of symptom resolution).

DESIGN

Prospective, cohort laboratory study.

METHODS

Ten concussed (60% male; 19.2 [0.9] y; 178.7 [14.0] cm; 71.3 [18.0] kg) and 10 matched controls (60% male; 19.5 [1.2] y; 176.1 [12.6] cm; 71.0 [17.0] kg) completed the single-leg hop stabilization task under single and dual task (subtracting by 6's or 7's) at both time points. Participants stood on a 30-cm tall box set 50% of their height behind force plates while in an athletic stance. A synchronized light was illuminated randomly, queuing participants to initiate the movement as rapidly as possible. Participants then jumped forward, landed on their nondominant leg, and were instructed to reach and maintain stabilization as fast as possible upon ground contact. We used 2 (group) × 2 (time) mixed-model analyses of variance to compare single-leg hop stabilization outcomes separately during single and dual task.

RESULTS

We observed a significant main group effect for single-task ankle plantarflexion moment, with greater normalized torque (mean difference = 0.03 N·m/body weight; P = .048, g = 1.18) for concussed individuals across time points. A significant interaction effect for single-task reaction time indicated that concussed individuals had slower performance acutely relative to asymptomatic (mean difference = 0.09 s; P = .015, g = 0.64), while control group performance was stable. No other main or interaction effects for single-leg hop stabilization task metrics were present during single and dual task (P ≥ .051).

CONCLUSIONS

Greater ankle plantarflexion torque coupled with slower reaction time may indicate stiff, conservative single-leg hop stabilization performance acutely following concussion. Our findings shed preliminary light on the recovery trajectories of biomechanical alterations following concussion and provide specific kinematic and kinetic focal points for future research.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

A person's walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg.

Quantifying the effect of human population mobility on malaria risk in the Peruvian Amazon

The impact of human population movement (HPM) on the epidemiology of vector-borne diseases, such as malaria, has been described. However, there are limited data on the use of new technologies for the study of HPM in endemic areas with difficult access such as the Amazon. In this study conducted in rural Peruvian Amazon, we used self-reported travel surveys and GPS trackers coupled with a Bayesian spatial model to quantify the role of HPM on malaria risk. By using a densely sampled population cohort, this study highlighted the elevated malaria transmission in a riverine community of the Peruvian Amazon. We also found that the high connectivity between Amazon communities for reasons such as work, trading or family plausibly sustains such transmission levels. Finally, by using multiple human mobility metrics including GPS trackers, and adapted causal inference methods we identified for the first time the effect of human mobility patterns on malaria risk in rural Peruvian Amazon. This study provides evidence of the causal effect of HPM on malaria that may help to adapt current malaria control programmes in the Amazon.

Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson’s correlation with an RPE (rate of perceived exertion) between r = 0.79 and r = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.

A Wearable System Based on Multiple Magnetic and Inertial Measurement Units for Spine Mobility Assessment: A Reliability Study for the Evaluation of Ankylosing Spondylitis

Spinal mobility assessment is essential for the diagnostic of patients with ankylosing spondylitis. BASMI is a routine clinical evaluation of the spine; its measurements are made with goniometers and tape measures, implying systematic errors, subjectivity, and low sensitivity. Therefore, it is crucial to develop better mobility assessment methods. The design, implementation, and evaluation of a novel system for assessing the entire spine’s motion are presented. It consists of 16 magnetic and inertial measurement units (MIMUs) communicated wirelessly with a computer. The system evaluates the patient’s movements by implementing a sensor fusion of the triaxial gyroscope, accelerometer, and magnetometer signals using a Kalman filter. Fifteen healthy participants were assessed with the system through six movements involving the entire spine to calculate continuous kinematics and maximum range of motion (RoM). The intrarater reliability was computed over the observed RoM, showing excellent reliability levels (intraclass correlation >0.9) in five of the six movements. The results demonstrate the feasibility of the system for further clinical studies with patients. The system has the potential to improve the BASMI method. To the best of our knowledge, our system involves the highest number of sensors, thus providing more objective information than current similar systems.

A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Sessions and Systems

Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.

The Influence of Physical Education on Self-Efficacy in Overweight Schoolgirls: A 12-Week Training Program

The purpose of this randomized controlled study was to investigate the impact of a 12-week physical education (PE) program on the self-efficacy of overweight schoolgirls. We randomly assigned 60 overweight schoolgirls (15–17 years) to either an experimental moderate to vigorous aerobic exercise (∼90 min, three times a week) group (n = 30) or a control group (CG) (n = 30) that received non-specific regular PE lessons with activities chosen by the curricular teacher mainly focused on team games and sports skills that aimed to achieve general psycho-physical wellness (∼90 min, three times a week). To assess the starting level of students and significant changes reached, at baseline and after training, a battery of standardized assessment motor tests and a psychometric scale (generalized self-efficacy scale, GES) were administered. At the end of the intervention, the experimental group reported a considerable decrease in body mass index (BMI) and a large improvement in self-efficacy (p < 0.001). No significant changes were found in the CG. The results suggested that the 12-week moderate to a vigorous aerobic exercise program is an effective weight loss intervention and a vehicle to promote a range of outcomes important to the qualitative growth of adolescents. In fact, it could provide a positive and significant impact on the self-efficacy of overweight schoolgirls.

Hierarchical Control of Visually-Guided Movements in a 3D-Printed Robot Arm

The control architecture guiding simple movements such as reaching toward a visual target remains an open problem. The nervous system needs to integrate different sensory modalities and coordinate multiple degrees of freedom in the human arm to achieve that goal. The challenge increases due to noise and transport delays in neural signals, non-linear and fatigable muscles as actuators, and unpredictable environmental disturbances. Here we examined the capabilities of hierarchical feedback control models proposed by W. T. Powers, so far only tested in silico. We built a robot arm system with four degrees of freedom, including a visual system for locating the planar position of the hand, joint angle proprioception, and pressure sensing in one point of contact. We subjected the robot to various human-inspired reaching and tracking tasks and found features of biological movement, such as isochrony and bell-shaped velocity profiles in straight-line movements, and the speed-curvature power law in curved movements. These behavioral properties emerge without trajectory planning or explicit optimization algorithms. We then applied static structural perturbations to the robot: we blocked the wrist joint, tilted the writing surface, extended the hand with a tool, and rotated the visual system. For all of them, we found that the arm in machina adapts its behavior without being reprogrammed. In sum, while limited in speed and precision (by the nature of the do-it-yourself inexpensive components we used to build the robot from scratch), when faced with the noise, delays, non-linearities, and unpredictable disturbances of the real world, the embodied control architecture shown here balances biological realism with design simplicity.

Consistent inconsistencies in braking: a spatial analysis

The dynamic system that is the bipedal body in motion is of interest to engineers, clinicians and biological anthropologists alike. Spatial statistics is more familiar to public health researchers as a way of analysing disease clustering and spread; nonetheless, this is a practical approach to the two-dimensional topography of the foot. We quantified the clustering of the centre of pressure (CoP) on the foot for peak braking and propulsive vertical ground reaction forces (GRFs) over multiple, contiguous steps to assess the consistency of the location of peak forces on the foot during walking. The vertical GRFs of 11 participants were collected continuously via a wireless insole system (MoticonReGo AG) across various experimental conditions. We hypothesized that CoPs would cluster in the hindfoot for braking and forefoot for propulsion, and that braking would demonstrate more consistent clustering than propulsion. Contrary to our hypotheses, we found that CoPs during braking are inconsistent in their location, and CoPs during propulsion are more consistent and clustered across all participants and all trials. These results add to our understanding of the applied forces on the foot so that we can better predict fatigue failures and better understand the mechanisms that shaped the modern bipedal form.

Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study

Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick-hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=-0.973,&nbsp;95%&nbsp;CI&nbsp;-0.981,-0.962,&nbsp;&nbsp;p<0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.

A unified energy-optimality criterion predicts human navigation paths and speeds

Navigating our physical environment requires changing directions and turning. Despite its ecological importance, we do not have a unified theoretical account of non-straight-line human movement. Here, we present a unified optimality criterion that predicts disparate non-straight-line walking phenomena, with straight-line walking as a special case. We first characterized the metabolic cost of turning, deriving the cost landscape as a function of turning radius and rate. We then generalized this cost landscape to arbitrarily complex trajectories, allowing the velocity direction to deviate from body orientation (holonomic walking). We used this generalized optimality criterion to mathematically predict movement patterns in multiple contexts of varying complexity: walking on prescribed paths, turning in place, navigating an angled corridor, navigating freely with end-point constraints, walking through doors, and navigating around obstacles. In these tasks, humans moved at speeds and paths predicted by our optimality criterion, slowing down to turn and never using sharp turns. We show that the shortest path between two points is, counterintuitively, often not energy-optimal, and, indeed, humans do not use the shortest path in such cases. Thus, we have obtained a unified theoretical account that predicts human walking paths and speeds in diverse contexts. Our model focuses on walking in healthy adults; future work could generalize this model to other human populations, other animals, and other locomotor tasks.

Real-time conversion of inertial measurement unit data to ankle joint angles using deep neural networks

Joint angle quantification from inertial measurement units (IMUs) is commonly performed using kinematic modelling, which depends on anatomical sensor placement and/or functional joint calibration; however, accurate three-dimensional joint motion measurement remains challenging to achieve. The aims of this study were firstly to employ deep neural networks to convert IMU data to ankle joint angles that are indistinguishable from those derived from motion capture-based inverse kinematics (IK) - the reference standard; and secondly, to validate the robustness of this approach across contrasting walking speeds in healthy individuals. Kinematics data were simultaneously calculated using IMUs and IK from 9 subjects during walking on a treadmill at 0.5 m/s, 1.0 m/s and 1.5 m/s. A generative adversarial network was trained using gait data at two of the walking speeds to predict ankle kinematics from IMU data alone for the third walking speed. There were significant differences between IK and IMU joint angle predictions for ankle eversion and internal rotation during walking at 0.5 m/s and 1.0 m/s (p < 0.001); however, no significant differences in joint angles were observed between the generative adversarial network prediction and IK at any speed or plane of joint motion (p < 0.05). The RMS difference in ankle joint kinematics between the generative adversarial network and IK for walking at 1.0 m/s was 3.8°, 2.1° and 3.5° for dorsiflexion, inversion and axial rotation, respectively. The modeling approach presented for real-time IMU to ankle joint angle conversion, which can be readily expanded to other joints, may provide enhanced IMU capability in applications such as telemedicine, remote monitoring and rehabilitation.

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations

New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.

A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality

An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK.

Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness

Physical fatigue is a recurrent problem in running that negatively affects performance and leads to an increased risk of being injured. Identification and management of fatigue helps reducing such negative effects, but is presently commonly based on subjective fatigue measurements. Inertial sensors can record movement data continuously, allowing recording for long durations and extensive amounts of data. Here we aimed to assess if inertial measurement units (IMUs) can be used to distinguish between fatigue levels during an outdoor run with a machine learning classification algorithm trained on IMU-derived biomechanical features, and what is the optimal configuration to do so. Eight runners ran 13 laps of 400 m on an athletic track at a constant speed with 8 IMUs attached to their body (feet, tibias, thighs, pelvis, and sternum). Three segments were extracted from the run: laps 2–4 (no fatigue condition, Rating of Perceived Exertion (RPE) = 6.0 ± 0.0); laps 8–10 (mild fatigue condition, RPE = 11.7 ± 2.0); laps 11–13 (heavy fatigue condition, RPE = 14.2 ± 3.0), run directly after a fatiguing protocol (progressive increase of speed until RPE ≥ 16) that followed lap 10. A random forest classification algorithm was trained with selected features from the 400 m moving average of the IMU-derived accelerations, angular velocities, and joint angles. A leave-one-subject-out cross validation was performed to assess the optimal combination of IMU locations to detect fatigue and selected sensor configurations were considered. The left tibia was the most recurrent sensor location, resulting in accuracies ranging between 0.761 (single left tibia location) and 0.905 (all IMU locations). These findings contribute toward a balanced choice between higher accuracy and lower intrusiveness in the development of IMU-based fatigue detection devices in running.

Biomechanics without Borders: Teaching Biomechanics in Brazil and South Africa

The "Biomechanics without Borders: Teaching Biomechanics in Brazil and South Africa" involved academics from different countries combining efforts to improve remote education. In addition to the live discussions, the event resulted in the availability of online content to help academic staffs improve teaching strategies in the field of human movement sciences.

Three-dimensional analysis of the effect of human movement on indoor airflow patterns

Human activity is known to leave significant effects on indoor airflow patterns. These patterns are carefully designed for many facilities such as cleanrooms, pharmaceutical settings, and healthcare environments, where human-induced wakes contribute to the transport of contaminants. Therefore, the knowledge about these wakes as it relates to indoor air quality is critical. As a result, a series of experiments were conducted in a controlled chamber to study the three-dimensional effects of true human walking on airflow. Experiments were designed to capture the effect of human walking under three different flow conditions, and for two different walking schemes. The results show that the effect of walking on the airflow is not negligible and can sustain up to 10 seconds after the moving body has passed. Walking on a straight line creates significant change in the velocity normal to the walking path and vertical to the plane of walking movement. These changes were detectable till 1.0 m away from the walking track. Also, the similarity between airflow patterns of walking once and twice illustrated a promising opportunity of predicting the flow patterns of random walk from a set of base cases.

Single- Versus Dual-Task Functional Movement Paradigms: A Biomechanical Analysis

CONTEXT

Laboratory-based movement assessments are commonly performed without cognitive stimuli (ie, single-task) despite the simultaneous cognitive processing and movement (ie, dual task) demands required during sport. Cognitive loading may critically alter human movement and be an important consideration for truly assessing functional movement and understanding injury risk in the laboratory, but limited investigations exist.

OBJECTIVE

To comprehensively examine and compare kinematics and kinetics between single- and dual-task functional movement among healthy participants while controlling for sex.

DESIGN

Cross-sectional study.

SETTING

Laboratory. Patients (or Other Participants): Forty-one healthy, physically active participants (49% female; 22.5 ± 2.1 y; 172.5 ± 11.9 cm; 71.0 ± 13.7 kg) enrolled in and completed the study.

INTERVENTION(S)

All participants completed the functional movement protocol under single- and dual-task (subtracting by 6s or 7s) conditions in a randomized order. Participants jumped forward from a 30-cm tall box and performed (1) maximum vertical jump landings and (2) dominant and (3) nondominant leg, single-leg 45° cuts after landing.

MAIN OUTCOME MEASURES

The authors used mixed-model analysis of variances (α = .05) to compare peak hip, knee, and ankle joint angles (degrees) and moments (N·m/BW) in the sagittal and frontal planes, and peak vertical ground reaction force (N/BW) and vertical impulse (Ns/BW) between cognitive conditions and sex.

RESULTS

Dual-task resulted in greater peak vertical ground reaction force compared with single-task during jump landing (mean difference = 0.06 N/BW; 95% confidence interval [CI], 0.01 to 0.12; P = .025) but less force during dominant leg cutting (mean difference = -0.08 N/BW; 95% CI, -0.14 to -0.02; P = .015). Less hip-flexion torque occurred during dual task than single task (mean difference = -0.09 N/BW; 95% CI, -0.17 to -0.02). No other outcomes were different between single and dual task (P ≥ .053).

CONCLUSIONS

Slight, but potentially important, kinematic and kinetic differences were observed between single- and dual-task that may have implications for functional movement assessments and injury risk research. More research examining how various cognitive and movement tasks interact to alter functional movement among pathological populations is warranted before clinical implementation.

Quantifying Achievable Levels of Improvement in Knee Joint Biomechanics During Gait After Total Knee Arthroplasty Relative to Osteoarthritis Severity

Total knee arthroplasty (TKA) surgery improves knee joint kinematics and kinetics during gait for most patients, but a lack of evidence exists for the level and incidence of improvement that is achieved. The objective of this study was to quantify patient-specific improvements in knee biomechanics relative to osteoarthritis (OA) severity levels. Seventy-two patients underwent 3-dimensional (3D) gait analysis before and 1 year after TKA surgery, as well as 72 asymptomatic adults and 72 with moderate knee OA. A combination of principal component analysis and discriminant analyses were used to categorize knee joint biomechanics for patients before and after surgery relative to asymptomatic, moderate, and severe OA. Post-TKA, 63% were categorized with knee biomechanics consistent with moderate OA, 29% with severe OA, and 8% asymptomatic. The magnitude and pattern of the knee adduction moment and angle (frontal plane features) were the most significant contributors in discriminating between pre-TKA and post-TKA knee biomechanics. Standard of care TKA improves knee biomechanics during gait to levels most consistent with moderate knee OA and predominately targets frontal plane features. These results provide evidence for the level of improvement in knee biomechanics that can be expected following surgery and highlight the biomechanics most targeted by surgery.

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

BACKGROUND

Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global).

OBJECTIVE

Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local).

METHODS

We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems.

RESULTS

This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project.

CONCLUSIONS

Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/24432.

Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon's signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.

Learning a Motor Skill from Video and Static Pictures in Physical Education Students-Effects on Technical Performances, Motivation and Cognitive Load

The purpose of the current study was to compare the effectiveness of a video and three different formats of static pictures (simultaneous-permanent pictures, sequential-transient pictures and sequential-permanent pictures) on the acquisition and retention of a complex judo skill in novice young adults. One hundred and thirty-three first-year students in the certificate in Physical Education (PE) were randomly assigned to either: a static-simultaneous-permanent pictures condition (n = 30), a static-sequential-transient pictures condition (n = 29), a static-sequential permanent pictures condition (n = 36) or a video condition (n = 38). They were instructed to observe and reproduce a complex judo technique (Ippon-Seoi-Nage) immediately after the learning phase (including a sequence of three trials—the acquisition phase) and after one week without observation (the retention phase). The results showed that the continuous video generated better learning performances than all static pictures formats. Moreover, it has been shown that sequential-permanent pictures presentation was more effective than static simultaneous-permanent pictures and sequential-transient pictures. In addition to the human movement effect, complementary explanations in terms of cognitive load theory, perceptual continuity, mental animation and intrinsic motivation are suggested. Implications of the results for the effective design of instructional materials within PE context are discussed.

Prolonged Standing Task Affects Adaptability of Postural Control in People With Parkinson's Disease

BACKGROUND

Studies on short-term upright quiet standing tasks have presented contradictory findings about postural control in people with Parkinson's disease (pwPD). Prolonged trial durations might better depict body sway and discriminate pwPD and controls.

OBJECTIVE

The aim of this study was to investigate postural control in pwPD during a prolonged standing task.

METHODS

A total of 26 pwPD and 25 neurologically healthy individuals performed 3 quiet standing trials (60 s) before completing a constrained prolonged standing task for 15 minutes. Motion capture was used to record body sway (Vicon, 100 Hz). To investigate the body sway behavior during the 15 minutes of standing, the analysis was divided into three 5-minute-long phases: early, middle, and late. The following body sway parameters were calculated for the anterior-posterior (AP) and medial-lateral (ML) directions: velocity, root-mean-square, and detrended fluctuations analysis (DFA). The body sway area was also calculated. Two-way ANOVAs (group and phases) and 1-way ANOVA (group) were used to compare these parameters for the prolonged standing and quiet standing, respectively.

RESULTS

pwPD presented smaller sway area (P < .001), less complexity (DFA; AP: P < .009; ML: P < .01), and faster velocity (AP: P < .002; ML: P < .001) of body sway compared with the control group during the prolonged standing task. Although the groups swayed similarly (no difference for sway area) during quiet standing, they presented differences in sway area during the prolonged standing task (P < .001).

CONCLUSIONS

Prolonged standing task reduced adaptability of the postural control system in pwPD. In addition, the prolonged standing task may better analyze the adaptability of the postural control system in pwPD.

Lossless Compression of Human Movement IMU Signals

Real-time human movement inertial measurement unit (IMU) signals are central to many emerging medical and technological applications, yet few techniques have been proposed to process and represent this information modality in an efficient manner. In this paper, we explore methods for the lossless compression of human movement IMU data and compute compression ratios as compared with traditional representation formats on a public corpus of human movement IMU signals for walking, running, sitting, standing, and biking human movement activities. Delta coding was the highest performing compression method which compressed walking, running, and biking data by a factor of 10 and compressed sitting and standing data by a factor of 18 relative to the original CSV formats. Furthermore, delta encoding was shown to approach the a posteriori optimal linear compression level. All methods were implemented and released as open source C code using fixed point computation which can be integrated into a variety of computational platforms. These results could serve to inform and enable human movement data compression in a variety of emerging medical and technological applications.

The "Journal of Functional Morphology and Kinesiology" Journal Club Series: Highlights on Recent Papers in Corrective Exercise

We are glad to introduce the Journal Club of Volume Five, fourth Issue. This edition is focused on relevant studies published in the last few years in the field of corrective exercise, chosen by our Editorial Board members and their colleagues. We hope to stimulate your curiosity in this field and to share a passion for sport with you, seen also from the scientific point of view. The Editorial Board members wish you an inspiring lecture.

Open-Source 3D Printable GPS Tracker to Characterize the Role of Human Population Movement on Malaria Epidemiology in River Networks: A Proof-of-Concept Study in the Peruvian Amazon

Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.

Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton

Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.

Sensor-to-Segment Calibration Methodologies for Lower-Body Kinematic Analysis with Inertial Sensors: A Systematic Review

Kinematic analysis is indispensable to understanding and characterizing human locomotion. Thanks to the development of inertial sensors based on microelectronics systems, human kinematic analysis in an ecological environment is made possible. An important issue in human kinematic analyses with inertial sensors is the necessity of defining the orientation of the inertial sensor coordinate system relative to its underlying segment coordinate system, which is referred to sensor-to-segment calibration. Over the last decade, we have seen an increase of proposals for this purpose. The aim of this review is to highlight the different proposals made for lower-body segments. Three different databases were screened: PubMed, Science Direct and IEEE Xplore. One reviewer performed the selection of the different studies and data extraction. Fifty-five studies were included. Four different types of calibration method could be identified in the articles: the manual, static, functional, and anatomical methods. The mathematical approach to obtain the segment axis and the calibration evaluation were extracted from the selected articles. Given the number of propositions and the diversity of references used to evaluate the methods, it is difficult today to form a conclusion about the most suitable. To conclude, comparative studies are required to validate calibration methods in different circumstances.

Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple Video Cameras

There is a need within human movement sciences for a markerless motion capture system, which is easy to use and sufficiently accurate to evaluate motor performance. This study aims to develop a 3D markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), and these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. Quantitatively, of all the mean absolute errors calculated, approximately 47% were <20 mm, and 80% were <30 mm. However, 10% were >40 mm. The primary reason for mean absolute errors exceeding 40 mm was that OpenPose failed to track the participant's pose in 2D images owing to failures, such as recognition of an object as a human body segment or replacing one segment with another depending on the image of each frame. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30 mm or less.

Allumo: Preprocessing and Calibration Software for Wearable Accelerometers Used in Posture Tracking

Inertial measurement units have recently shown great potential for the accurate measurement of joint angle movements in replacement of motion capture systems. In the race towards long duration tracking, inertial measurement units increasingly aim to ensure portability and long battery life, allowing improved ecological studies. Their main advantage over laboratory grade equipment is their usability in a wider range of environment for greater ecological value. For accurate and useful measurements, these types of sensors require a robust orientation estimation that remains accurate over long periods of time. To this end, we developed the Allumo software for the preprocessing and calibration of the orientation estimate of triaxial accelerometers. This software has an automatic orientation calibration procedure, an automatic erroneous orientation-estimate detection and useful visualization to help process long and short measurement periods. These automatic procedures are detailed in this paper, and two case studies are presented to showcase the usefulness of the software. The Allumo software is open-source and available online.

Local human movement patterns and land use impact exposure to zoonotic malaria in Malaysian Borneo

Human movement into insect vector and wildlife reservoir habitats determines zoonotic disease risks; however, few data are available to quantify the impact of land use on pathogen transmission. Here, we utilise GPS tracking devices and novel applications of ecological methods to develop fine-scale models of human space use relative to land cover to assess exposure to the zoonotic malaria Plasmodium knowlesi in Malaysian Borneo. Combining data with spatially explicit models of mosquito biting rates, we demonstrate the role of individual heterogeneities in local space use in disease exposure. At a community level, our data indicate that areas close to both secondary forest and houses have the highest probability of human P. knowlesi exposure, providing quantitative evidence for the importance of ecotones. Despite higher biting rates in forests, incorporating human movement and space use into exposure estimates illustrates the importance of intensified interactions between pathogens, insect vectors and people around habitat edges.

Which Presentation Speed Is Better for Learning Basketball Tactical Actions Through Video Modeling Examples? The Influence of Content Complexity

The present experiment examined the effect of content complexity on perceived cognitive load and game performance when learning basketball tactical actions from videos modeling examples displayed at different speeds. A two (presentation speed: slow vs. normal) × three (content complexity: low vs. medium vs. high) design between subjects was adopted in the experiment. Following the learning phase, 120 secondary school students were quasi-randomly assigned to six experimental conditions and required to rate their perceived cognitive and game performance. Data analyses revealed that for low complexity content, both speeds of presentation have similar effects on learning. Conversely, for medium and high complexity contents, participants exposed to the slow-presentation speed learned more efficiently than those exposed to the normal-presentation speed. The findings recommend the use of slow-speed videos when learning basketball tactical actions, particularly in playing systems with medium or high levels of complexity.

Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning

Full-body motion capture typically requires sensors/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors/markers can be reduced using deep learning or offline methods. However, this requires large training datasets and/or sufficient computational resources. Therefore, we investigate the following research question: "What is the performance of a shallow approach, compared to a deep learning one, for estimating time coherent full-body poses using only five inertial sensors?". We propose to incorporate past/future inertial sensor information into a stacked input vector, which is fed to a shallow neural network for estimating full-body poses. Shallow and deep learning approaches are compared using the same input vector configurations. Additionally, the inclusion of acceleration input is evaluated. The results show that a shallow learning approach can estimate full-body poses with a similar accuracy (~6 cm) to that of a deep learning approach (~7 cm). However, the jerk errors are smaller using the deep learning approach, which can be the effect of explicit recurrent modelling. Furthermore, it is shown that the delay using a shallow learning approach (72 ms) is smaller than that of a deep learning approach (117 ms).

Sloppy, But Acceptable, Control of Biological Movement: Algorithm-Based Stabilization of Subspaces in Abundant Spaces

In this paper, we develop an algorithm-based approach to the problem of stability of salient performance variables during motor actions. This problem is reformulated as stabilizing subspaces within high-dimensional spaces of elemental variables. Our main idea is that the central nervous system does not solve such problems precisely, but uses simple rules that achieve success with sufficiently high probability. Such rules can be applied even if the central nervous system has no knowledge of the mapping between small changes in elemental variables and changes in performance. We start with a rule ”Act on the most nimble” (the AMN-rule), when changes in the local feedback-based loops occur for the most unstable variable first. This rule is implemented in a task-specific coordinate system that facilitates local control. Further, we develop and supplement the AMN-rule to improve the success rate. Predictions of implementation of such algorithms are compared with the results of experiments performed on the human hand with both visual and mechanical perturbations. We conclude that physical, including neural, processes associated with everyday motor actions can be adequately represented with a set of simple algorithms leading to sloppy, but satisfactory, solutions. Finally, we discuss implications of this scheme for motor learning and motor disorders.

Important Movement Concepts: Clinical Versus Neuroscience Perspectives

Human movement is complex, presenting clinical and research challenges regarding how it is described and investigated. This paper discusses the commonalities and differences on how human movement is conceptualized from neuroscientific and clinical perspectives with respect to postural control; the limitations of linear measures; movement efficiency with respect to metabolic energy cost and selectivity; and, how muscle synergy analysis may contribute to our understanding of movement variability. We highlight the role of sensory information on motor performance with respect to the base of support and alignment, illustrating a potential disconnect between the clinical and neuroscientific perspectives. The purpose of this paper is to discuss the commonalities and differences in how movement concepts are defined and operationalized by Bobath clinicians and the neuroscientific community to facilitate a common understanding and open the dialogue on the research practice gap.

Effects of Ankle Muscle Fatigue and Visual Behavior on Postural Sway in Young Adults

Ankle muscle fatigue has been shown to increase body sway. In addition, body sway in quiet upright standing is reduced when saccadic eye movements are performed. The purpose of this study was to investigate the effects of visual information manipulation on postural control during ankle muscle fatigue in young adults. Twenty young adults performed: (1) two 60-s trials in quiet bipedal standing with eyes open, eyes closed, and while performing saccadic eye movements; (2) maximum voluntary isometric contractions in a leg press device, custom-made to test ankle plantar flexion force; (3) a calf raise exercise on top of a step to induce ankle muscle fatigue; and (4) a repetition of items 1 and 2. Postural sway parameters were compared with two-way ANOVAs (vision condition × fatigue; p < 0.05). Ankle muscle fatigue increased anterior-posterior and medial-lateral displacement and RMS of sway, as well as sway area. Saccadic eye movements reduced anterior-posterior displacement and RMS of sway and area of sway compared to eyes open and eyes closed conditions. Both saccadic eye movements and eyes closed increased the frequency of AP sway compared to the eyes open condition. Finally, anterior-posterior displacement, anterior-posterior RMS, and both anterior-posterior and medial-lateral sway frequency were affected by an interaction of fatigue and vision condition. Without muscle fatigue, closing the eyes increased anterior-posterior displacement and RMS of sway, compared to eyes open, while during muscle fatigue closing the eyes closed reduced anterior-posterior displacement and had no significant effect on anterior-posterior RMS. In conclusion, body sway was increased after induction of ankle muscle fatigue. Saccadic eye movements consistently reduced postural sway in fatigued and unfatigued conditions. Surprisingly, closing the eyes increased sway in the unfatigued condition but reduced sway in the fatigued condition.