Human Locomotion Research Articles

Pacing of Human Locomotion on Land and in Water: 1500 m Swimming vs. 5000 m Running

The study of pace strategy in different environments could help to understand its dependence on athletes’ energetic limits or on sport-specific factors. The aim of this study was to analyse the pacing strategy of finalists during seven swimming and running world events held in 2021–2022. The speed of 32 swimmers every 50 m in 1500 m freestyle competitions, and the speed of 55 runners every 100 m in 5000 m track competitions, were analysed. Differences between swimming and running were statistically significant for Total Time (p = 0.00, ES = 1.9), Average Time of splits (p = 0.00, ES = 2.0), Median Time of splits (p = 0.00, ES = 2.0), and Maximal length of split sequences (p = 0.00, ES = 1.3), and non-significantly different for number of Sequences of splits (p = 0.12, ES = 0.5), Percentage of total splits faster than the median speed (p = 0.08, ES = 0.2), Percentage of splits faster than the median speed in the first half (p = 0.16, ES = 0.4) and Percentage of splits faster than the median speed in the second half (p = 0.21, ES = 0.3). In conclusion, despite similar metabolic requirements of 1500 m swimming and 5000 m running, the influence of specific environment and sport type on the pacing strategy of world level competitions seems to be supported.

Walking is the most common form of human locomotion. From a motor control perspective difficulty of walking is the use of high heeled shoes

Walking is the most common form of human locomotion. From a motor control perspective difficulty of walking is the use of high heeled shoes

Sex differences in age-related differences in joint motion during gait in community-dwelling middle-age and older individuals

BackgroundWalking is the most important mode of human locomotion; however, the ability to walk often decreases with age. Age-related differences in lower-limb kinematics during gait may differ depending on sex. However, the question of the compounded effects of age and sex on gait kinematics remains unsolved. Research questionThe present study aimed to clarify the interaction between age and sex in differences in gait kinematics of community-dwelling middle-age and older individuals. MethodsThis study included 836 community-dwelling middle-age and older adults (61.8 % female). Joint motion during comfortable gait was measured using magnetic and inertial measurement units. Hip, knee, and ankle joint angles were calculated in the sagittal plane. Participants were divided into four groups according to age: 50–59, 60–69, 70–79, and 80–89 years. The interaction of sex and age on spatiotemporal gait parameters and the peak value of joint angles was analyzed using two-way analysis of variance (ANOVA) and Tukey’s post hoc test. ResultsGait speed (F = 43.92, P < 0.001), step length (F = 73.00, P < 0.001), hip extension (F = 12.89, P = 0.002), knee flexion (F = 39.99, P < 0.001), and ankle plantar flexion (F = 27.43, P < 0.001) significantly decreased with age. Significant differences according to sex were observed in all parameters except gait speed. Significant age and sex interaction effects were observed for knee flexion (F = 4.97, P = 0.002) and ankle dorsiflexion (F = 4.04, P = 0.007). SignificanceA significant interaction effect of age and sex was observed for peak angle of knee flexion and ankle dorsiflexion during gait. In particular, the knee flexion angle among females during gait began to decrease from 60 years of age, and the decreasing trend was faster and more prominent than that among males.

Correction to: 'Assessing the stability of human locomotion: a review of current measures' (2013) by Bruijn et al.

Open AccessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Bruijn S. M., Meijer O. G., Beek P. J. and van Dieën J. H. 2023Correction to: ‘Assessing the stability of human locomotion: a review of current measures’ (2013) by Bruijn et al.J. R. Soc. Interface.202023020720230207http://doi.org/10.1098/rsif.2023.0207SectionOpen AccessCorrectionCorrection to: ‘Assessing the stability of human locomotion: a review of current measures’ (2013) by Bruijn et al. S. M. Bruijn S. M. Bruijn http://orcid.org/0000-0003-0290-2131 Google Scholar Find this author on PubMed Search for more papers by this author , O. G. Meijer O. G. Meijer http://orcid.org/0000-0002-8394-6859 Google Scholar Find this author on PubMed Search for more papers by this author , P. J. Beek P. J. Beek http://orcid.org/0000-0002-0917-8548 Google Scholar Find this author on PubMed Search for more papers by this author and J. H. van Dieën J. H. van Dieën http://orcid.org/0000-0002-7719-5585 Google Scholar Find this author on PubMed Search for more papers by this author S. M. Bruijn S. M. Bruijn http://orcid.org/0000-0003-0290-2131 Google Scholar Find this author on PubMed Search for more papers by this author , O. G. Meijer O. G. Meijer http://orcid.org/0000-0002-8394-6859 Google Scholar Find this author on PubMed Search for more papers by this author , P. J. Beek P. J. Beek http://orcid.org/0000-0002-0917-8548 Google Scholar Find this author on PubMed Search for more papers by this author and J. H. van Dieën J. H. van Dieën http://orcid.org/0000-0002-7719-5585 Google Scholar Find this author on PubMed Search for more papers by this author Published:03 May 2023https://doi.org/10.1098/rsif.2023.0207This article corrects the followingReview ArticleAssessing the stability of human locomotion: a review of current measureshttps://doi.org/10.1098/rsif.2012.0999 S. M. Bruijn, O. G. Meijer, P. J. Beek and J. H. van Dieën volume 10issue 83Journal of The Royal Society Interface06 June 2013 J. R. Soc. Interface 10, 20120999. (Published online 6 June 2013) (https://doi.org/10.1098/rsif.2012.0999) An attentive reader pointed out a mistake in our paper. On page 6 the paper reads ‘Chang et al. [95] reported that walking over an unstable support surface increases values of λS, but not of λL.’, which actually should have read: ‘Chang et al. [95] reported that walking over an unstable support surface decreases values of λL, but not of λS.’. This mistake does not further influence the conclusions we reach regarding these stability measures. Previous Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsRelated articlesAssessing the stability of human locomotion: a review of current measures06 June 2013Journal of The Royal Society Interface This IssueMay 2023Volume 20Issue 202 Article InformationDOI:https://doi.org/10.1098/rsif.2023.0207PubMed:37132230Published by:Royal SocietyOnline ISSN:1742-5662History: Manuscript received11/04/2023Manuscript accepted11/04/2023Published online03/05/2023 License:© 2023 The Authors.Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. Citations and impact Subjectsbiomechanics

RELATIONSHIP BETWEEN MUSCULOSKELETAL DISORDERS AND PSYCHOLOGICAL DISTRESS AMONG HEALTH SCIENCES UNDERGRADUATE STUDENTS IN SELANGOR

Musculoskeletal disorders (MSDs) are injuries or pain that affect the human locomotor system and may affect everyone at least once in a lifetime. The accompanying chronicity, pain and disability are usually associated with mental health impairment. This study was carried out to examine the prevalence of MSDs and its relationship with psychological distress among undergraduate students from several health sciences programs. A set of self-administered questionnaires consisting of the Standardised Nordic Musculoskeletal Questionnaire and the Depression, Anxiety and Stress Scale-21 (DASS-21) was given out to 330 undergraduate students of a public university in Malaysia. Multiple linear regression analysis was used to test the hypothesis on the relationship between MSDs and psychological distress (depression, stress, and anxiety). The prevalence of MSDs during the past 12 months was found to be high (86.6%), with lower back pain being the most common symptom (63.3%), followed by neck pain (53.6%) and shoulder pain (50.6%). Half of the students (57.0%) reported depression, 37.3% reported anxiety, and 28.2% reported stress. The occurrence of MSDs in the regions of the shoulder, and hips or thighs or buttock regions have significant positive relationship with stress and anxiety scores of the students (p &lt; 0.05). Meanwhile, the occurrence of MSDs in the region of the wrists or hands, knees, and shoulders have significant positive relationship with the depression score (p &lt; 0.05). In conclusion, MSDs in the shoulder, hips or thighs or buttock regions wrists or hands, and knees regions were significantly associated with psychological distress. Further intervention programs should be implemented to reduce psychological distress associated with MSDs among the students.

Motor control insights on walking planner and its stability

The application of biomechanics and motor control models in the control of bipedal robots (humanoids and exoskeletons) has revealed limitations of our understanding of human locomotion. A recently proposed model uses the potential energy for bipedal structures to model the bipedal dynamics, allowing us to predict the system dynamics from its kinematics. This work proposes a task-space planner for human-like straight locomotion that targets application in rehabilitation robotics and computational neuroscience. The proposed architecture is based on the potential energy model and employs locomotor strategies from human data. The model generates Centre of Mass (CoM) trajectories, foot swing trajectories and the Base of Support (BoS) over time. The data show that the proposed architecture can generate behaviour in line with human walking strategies for both the CoM and the foot swing. Despite the CoM vertical trajectory not being as smooth as a human trajectory, the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory compared to the inverted pendulum models. The proposed model can also assess stability based on the body kinematics embedding currently used in clinical practice. However, the model also implies a shift in the interpretation of the spatiotemporal parameters of the gait, which are now determined by the conditions for the equilibrium and not vice versa. In other words, locomotion is a dynamic reaching where the motor primitives are also determined by gravity.

Energy-Efficient Actuator Design Principles for Robotic Leg Prostheses and Exoskeletons: A Review of Series Elasticity and Backdrivability

AbstractRobotic leg prostheses and exoskeletons have traditionally been designed using highly-geared motor-transmission systems that minimally exploit the passive dynamics of human locomotion, resulting in inefficient actuators that require significant energy consumption and thus provide limited battery-powered operation or require large onboard batteries. Here we review two of the leading energy-efficient actuator design principles for legged and wearable robotic systems: series elasticity and backdrivability. As shown by inverse dynamic simulations of walking, there are periods of negative joint mechanical work that can be used to increase efficiency by recycling some of the otherwise dissipated energy using series elastic actuators and/or backdriveable actuators with energy regeneration. Series elastic actuators can improve shock tolerance during foot-ground impacts and reduce the peak power and energy consumption of the electric motor via mechanical energy storage and return. However, actuators with series elasticity tend to have lower output torque, increased mass and architecture complexity due to the added physical spring, and limited force and torque control bandwidth. High torque density motors with low-ratio transmissions, known as quasi-direct drives, can likewise achieve low output impedance and high backdrivability, allowing for safe and compliant human-robot physical interactions, in addition to energy regeneration. However, torque-dense motors tend to have higher Joule heating losses, greater motor mass and inertia, and require specialized motor drivers for real-time control. While each actuator design has advantages and drawbacks, designers should consider the energy-efficiency of robotic leg prostheses and exoskeletons during daily locomotor activities besides continuous level-ground walking.

Reaction of human walking to transient block of vision: analysis in the context of indirect, referent control of motor actions.

Human locomotion may result from monotonic shifts in the referent position, R, of the body in the environment. R is also the spatial threshold at which muscles can be quiescent but are activated depending on the deflection of the current body configuration Q from R. Shifts in R are presumably accomplished with the participation of proprioceptive and visual feedback and responsible for transferring stable body balance (equilibrium) from one place in the environment to another, resulting in rhythmic activity of multiple muscles by a central pattern generator (CPG). We tested predictions of this two-level control scheme. In particular, in response to a transient block of vision during locomotion, the system can temporarily slow shifts in R. As a result, the phase of rhythmical movements of all four limbs will be changed for some time, even though the rhythm and other characteristics of locomotion will be fully restored after perturbation, a phenomenon called long-lasting phase resetting. Another prediction of the control scheme is that the activity of multiple muscles of each leg can be minimized reciprocally at specific phases of the gait cycle both in the presence and absence of vision. Speed of locomotion is related to the rate of shifts in the referent body position in the environment. Results confirmed that human locomotion is likely guided by feedforward shifts in the referent body location, with subsequent changes in the activity of multiple muscles by the CPG. Neural structures responsible for shifts in the referent body configuration causing locomotion are suggested.

Identification of Motor Control Objectives in Human Locomotion via Multi-Objective Inverse Optimal Control

Abstract Predictive simulations of human motion are a precious resource for a deeper understanding of the motor control policies encoded by the central nervous system. They also have profound implications for the design and control of assistive and rehabilitation devices, for ergonomics, as well as for surgical planning. However, the potential of state-of-the-art predictive approaches is not fully realized yet, making it difficult to draw convincing conclusions about the actual optimality principles underlying human walking. In the present study, we propose a novel formulation of a bilevel, inverse optimal control strategy based on a full-body three-dimensional neuromusculoskeletal model. In the lower level, prediction of walking is formulated as a principled multi-objective optimal control problem based on a weighted Chebyshev metric, whereas the contributions of candidate control objectives are systematically and efficiently identified in the upper level. Our framework has proved to be effective in determining the contributions of the selected objectives and in reproducing salient features of human locomotion. Nonetheless, some deviations from the experimental kinematic and kinetic trajectories have emerged, suggesting directions for future research. The proposed framework can serve as an inverse optimal control platform for testing multiple optimality criteria, with the ultimate goal of learning the control objectives that best explain observed human motion.2

Prediction of Contralateral Lower-Limb Joint Angles Using Vibroarthrography and Surface Electromyography Signals in Time-Series Network

Multisource biosignals are being increasingly used in human–machine interaction applications. In particular, a critical problem in exoskeleton-assisted rehabilitation for patients with hemiplegia is the generation of rhythmic and symmetrical locomotion. To support lower limb rehabilitation, we propose a model for predicting contralateral joint angles using multisource biosignals. First, a vibroarthrography (VAG) sensor is attached to the affected leg, and surface electromyography sensors are attached to the sound leg. The corresponding signals are used to estimate the hip, knee, and ankle joint angles of the affected leg. Second, an algorithm based on a temporal convolution network (TCN) is introduced to predict the contralateral lower-limb joint angles during human locomotion. The TCN is compared with a long short-term memory (LSTM) network and a convolutional neural network. Experiments were conducted by 10 healthy participants. The results of the proposed model were compared with measurements from encoders in three joints mounted on an exoskeleton to verify the applicability of the proposed model. In addition, by using the outputs of a motion capture system as the ground truth, the experimental results validated the model prediction performance. The prediction root mean square error (RMSE) of the TCN was 52%–70% lower than that of the LSTM network and CNN at different paces. The prediction RMSE using VAG was 20%–24% lower than that without using VAG. Note to Practitioners—We propose a model for predicting the contralateral lower-limb joint angles of an exoskeleton. The model uses measurements from multiple biosignal sensors to support exoskeleton-assisted hemiplegia rehabilitation. A sensing system based on acoustic signals and bioelectric signals is developed to improve the joint angle prediction in the affected leg of patients with hemiplegia. A temporal convolution network is implemented to estimate joint angles, and training considering the root mean square error and Pearson correlation coefficient as evaluation indicators is performed. Unlike similar methods that include no sensory feedback from the affected leg, the proposed method incorporates VAG signals from the affected leg and sEMG signals from the sound leg to handle different degrees of hemiplegia and support the generation of rhythmic and symmetrical locomotion for gait rehabilitation using an exoskeleton.

A gait functional classification of adolescent idiopathic scoliosis (AIS) based on spatio-temporal parameters (STP)

BackgroundTherapeutic decisions for patients with adolescent idiopathic scoliosis (AIS) are mostly based on static measurements performed on two-dimensional standing full-spine radiographs. However, the trunk plays an essential role in the human locomotion, and the functional consequences during daily activities of this specific and common spinal deformity are not factored in. Research questionDoes patients with AIS have specific gait patterns based on spatio-temporals parameters measurements ? Methods90 AIS patients (aged 10–18 years) with preoperative simplified gait analysis were retrospectively included for analysis between 2017 and 2020. Spatio-temporal parameters (STP) were measured on a 3-m baropodometric gaitway and included the measurement of 15 normalized gait parameters. A hierarchical cluster analysis was performed to identify group of patients based on the similarities of their gait patterns, and functional variables’ inter-group differences were also measured. The subject distribution was calculated to identify the structural characteristics of the subjects according to their gait patterns. ResultsThree gait patterns were identified. Cluster 1 (46%) was defined by asymmetry, Cluster 2 (16%) by instability, and Cluster 3 (36%) by variability. Each cluster was significantly different from the other ones on at least 6 different parameters (p < 0.05). Furthermore, each cluster was associated with one type of curve: Lenke 1 for Cluster 1 (57.5%), Lenke 6 for Cluster 2 (40%) and Lenke 5 for Cluster 3 (43.5%). SignificancePatients with severe AIS have a dynamic signature during gait identified on STP. Understanding consequences of this deformity on gait may be an interesting avenue to study the pathological mechanisms involved in their dynamic motor organization. Furthermore, these results might also be a first step to study the effectiveness of the different therapies.

Recent Updates on Investigation of Neural Control of Human Locomotion by Non-invasive Neural Recording Techniques

本稿では，脳波や筋電図など非侵襲神経機能計測を使用したヒト二足歩行の神経制御に関する研究の近況について概説する．歩行の神経制御研究は1800年代から脈々と続いている長い歴史を持つ研究分野であり，従来はネコなどの四足動物を対象とした脳の部分破壊術を用いた研究が主流であった．しかしながら，近年の計測技術・解析技術のめざましい進歩により，ヒトにおいても対象とできる神経領域が拡大してきている．四足動物では四肢の左右・屈筋伸筋の交代性活動の生成を司る脊髄や脳幹による制御が中心であり定常歩行時では大脳皮質の関与は少ない．一方，二足直立歩行を行うヒトでは定常歩行時でさえ大脳皮質が顕著に活動する．このような違いを考慮すると，ヒト二足直立歩行の神経制御機序の理解のためには，実際にヒトが歩行中の神経活動を計測することが重要である．高密度脳波や高密度筋電図を使用した健常者・神経疾患患者の歩行実験により，ヒト二足歩行制御における大脳皮質から脳深部，脊髄の役割が徐々に明らかになりつつある．本稿ではそのようなヒト二足歩行制御の神経制御に関する近年の研究についてまとめる．

Modification of the locomotor pattern when deviating from the characteristic heel-to-toe rolling pattern during walking.

Humans are amongst few animals that step first on the heel, and then roll on the ball of the foot and toes. While this heel-to-toe rolling pattern has been shown to render an energetic advantage during walking, the effect of different foot contact strategies, on the neuromuscular control of adult walking gaits has received less attention. We hypothesised that deviating from heel-to-toe rolling pattern affects the energy transduction and weight acceptance and re-propulsive phases in gait along with the modification of spinal motor activity. Ten subjects walked on a treadmill normally, then placed their feet flat on the ground at each step and finally walked on the balls of the feet. Our results show that when participants deviate from heel-to-toe rolling pattern strategy, the mechanical work increases on average 85% higher (F = 15.5; p < 0.001), mainly linked to a lack of propulsion at late stance. This modification of the mechanical power is related to a differential involvement of lumbar and sacral segment activation. Particularly, the delay between the major bursts of activation is on average 65% smaller, as compared to normal walking (F = 43.2; p < 0.001). Similar results are observable in walking plantigrade animals, but also at the onset of independent stepping in toddlers, where the heel-to-toe rolling pattern is not yet established. These indications seem to bring arguments to the fact that the rolling of the foot during human locomotion has evolved to optimise gait, following selective pressures from the evolution of bipedal posture.

Hip–Spine Syndrome: A Focus on the Pelvic Incidence in Hip Disorders

Since Offierski and MacNab reported a close association between the hip and spine as hip-spine syndrome in 1983, many studies on spinal alignment in hip disorders have been conducted. Notably, the pelvic incidence angle (PI) is the most important parameter and is determined by the anatomical variations in the sacroiliac joint and hip. Studies on the association of the PI with hip disorders can help in understanding the pathophysiology of hip-spine syndrome. A PI increase has been observed during the evolution of bipedal locomotion in humans and in the acquisition of gait during child development. Although the PI is a fixed parameter that is stable and unaffected by posture from adulthood onwards, it has become clear that it increases in the standing position in older people. While it may be associated with a greater risk of developing or progressing to spinal disorders, the association between the PI and hip disorders remains controversial because of the multifactorial nature of hip osteoarthritis (HOA) and the wide range of PIs in HOA (18-96°), making the interpretation of results difficult. However, several hip disorders (i.e., femoroacetabular impingement and rapid destructive coxarthrosis) have been shown to be associated with the PI. Further investigation on this topic is, therefore, warranted.

Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling.

Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants' pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent's capacity to converge during the training process. As a result, the models' convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance.

보행 보조 로봇의 보행 시점 판별 성능 향상을 위한 자이로 센서 신호 처리 기법

Wearable robots have been developed to aid or substitute the gait locomotion of humans. To assist gait locomotion based on the intention of a wearer, a gait pattern analysis is required with a wearable sensor by measuring body information, i.e., a joint angular velocity. However, measuring a precise joint angular velocity is difficult because the attachment position of a sensor has a curvature and an anatomical joint axis which is invisible. Therefore, a sensor calibration algorithm, which aligns a sensor axis into an anatomical joint axis, is required to provide an apposite assist for a wearer. Hence, in this paper, a new and simple sensor calibration algorithm is proposed with a one sensor which contributes to simplifying and lightening the robot system. Since a wearer shakes the body or collides with the ground when walking, the attachment position of a sensor may be changed. Thus, a continuous sensor compensation algorithm is also proposed. The effectiveness of this new algorithm is demonstrated by gait locomotion experiments on various paths.

RunScribe Sacral Gait Lab™ Validation for Measuring Pelvic Kinematics during Human Locomotion at Different Speeds.

Optoelectronic motion capture systems are considered the gold standard for measuring walking and running kinematics parameters. However, these systems prerequisites are not feasible for practitioners as they entail a laboratory environment and time to process and calculate the data. Therefore, this study aims to evaluate the validity of the three-sensor RunScribe Sacral Gait Lab™ inertial measurement unit (IMU) in measuring pelvic kinematics in terms of vertical oscillation, tilt, obliquity, rotational range of motion, and the maximum angular rates during walking and running on a treadmill. Pelvic kinematic parameters were measured simultaneously using an eight-camera motion analysis system (Qualisys Medical AB, GÖTEBORG, Sweden) and the three-sensor RunScribe Sacral Gait Lab™ (Scribe Lab. Inc. San Francisco, CA, USA) in a sample of 16 healthy young adults. An acceptable level of agreement was considered if the following criteria were met: low bias and SEE (<0.2 times the between-subject differences SD), almost perfect (r > 0.90), and good reliability (ICC > 0.81). The results obtained reveal that the three-sensor RunScribe Sacral Gait Lab™ IMU did not reach the validity criteria established for any of the variables and velocities tested. The results obtained therefore show significant differences between the systems for the pelvic kinematic parameters measured during both walking and running.

Achilles' new heel: Shock absorbing, gait assisting and energy harvesting

Humans have evolved to walk highly efficiently to conserve energy, making it challenging to develop assistive and energy-harvesting devices for walking. Herein, we report a heel pad-based assistance device for walking that not only optimizes the energetic economy of walking and prevents plantar fasciitis but also harvests energy from heel impact. Our footwear-embedded device improves the walking economy by offering shock absorption and walking assistance, while simultaneously providing energy-harvesting functions. We demonstrate that the use of our device reduces the activation of the gastrocnemius and soleus muscles during the foot strike by 5.8 ± 1.0 % and 4.1 ± 0.6 %, respectively. The collisional energy conserved from the impact at the touchdown is transformed into 3.8 ± 0.3 watts of electrical power (mean ± SEM). Compared with walking in normal shoes, the energy savings with the device imply that walking endurance could be increased by as much as 10 % without extra effort from the wearer. Our findings demonstrate the potential of the heel pad-based device that enhances the energy economy of walking and human bipedal locomotion.

Real-Time Gait Phase Estimation Based on Neural Network and Assistance Strategy Based on Simulated Muscle Dynamics for an Ankle Exosuit

Soft exosuits have been demonstrated to possess the potential to reduce metabolism and muscle fatigue in human locomotion. How to maximize the user’s metabolic benefit and reduction of muscle fatigue are the primary objectives of assistance patterns optimization. For the exosuit controller, accurate gait phase estimation and appropriate assistance force profile are crucial factors. To gain an accurate real-time gait phase estimation, a neural network algorithm based on multi-modal information fusion was developed. The eigenvector of which was composed of Euler angles and gyroscope data of four Inertial Measurement units fixed on thigh and calf, and gait period estimated by force sensing resistor installed on foot insoles. The mean squared error of the model is below 3 ms and the regression coefficient R exceeds 0.999 when walking at a speed of 1.5 m/s. For the assistance force profile, which was designed based on the characteristics of soleus muscle dynamics. Wherein the biomechanics of walking were derived from simulation with OpenSim software. Compared with wearing an exosuit without assistance and with assistance, the metabolic demand was reduced by an average of 7.5%, and the average peak value and RMS of calf muscles involved in palntarflexion were decreased to different degrees. Our research illustrates the feasibility of neural network-based real-time gait phase estimation and assistance strategy design obtained by simulating muscle dynamics in the OpenSim software.

Assessment of Cognitive Fatigue from Gait Cycle Analysis

Cognitive Fatigue (CF) is the decline in cognitive abilities due to prolonged exposure to mentally demanding tasks. In this paper, we used gait cycle analysis, a biometric method related to human locomotion to identify cognitive fatigue in individuals. The proposed system in this paper takes two asynchronous videos of the gait of individuals to classify if they are cognitively fatigued or not. We leverage the pose estimation library OpenPose, to extract the body keypoints from the frames in the videos. To capture the spatial and temporal information of the gait cycle, a CNN-based model is used in the system to extract the embedded features which are then used to classify the cognitive fatigue level of individuals. To train and test the model, a gait dataset is built from 21 participants by collecting walking data before and after inducing cognitive fatigue using clinically used games. The proposed model can classify cognitive fatigue from the gait data of an individual with an accuracy of 81%.