Natural Gait Research Articles

Self-selected speed provides more accurate human gait kinematics and spatiotemporal parameters than overground simulated speed on a treadmill: a cross-sectional study

BackgroundWalking speed, a key element of gait analysis, is essential for evaluating the biomechanics of the musculoskeletal system and is typically assessed on flat surfaces, such as walkways or treadmills. While many authors have compared the differences and similarities between treadmill and overground walking, no studies have yet investigated the differences between treadmill gait analysis at self-selected speed (SS) and overground simulated speed (OS). The hypothesis is that accurate kinematic measurements depend on selecting the correct gait speed; however, a mismatch between the perceived comfortable treadmill speed and actual overground speed may affect the accuracy of treadmill gait analyses. This study aimed to assess treadmill gait in healthy young adults by comparing the SS with the OS. The objectives were to determine whether participants could match SS with OS on a treadmill, examine sex differences in gait kinematics and spatiotemporal parameters (KSP) at different speeds, and identify which speed better reflects natural gait kinematics.MethodsA total of 60 healthy men and 70 healthy women, aged 22–35 years, participated in this cross-sectional study to investigate the gait kinematics and spatiotemporal differences between the SS and OS. Student’s t-test, Bonferroni adjustment, Cohen’s effect size, and quadratic regression were employed to analyse differences across walking speeds and groups.ResultsA discrepancy between OS and SS was observed in 66.4% of the participants. Our findings revealed that the adjusted R² values for KSP at SS were consistently greater than those at OS, suggesting that SS offers a more robust and accurate representation of gait kinematics, whereas OS is less reliable.ConclusionsThese findings underscore the importance of individualized speed selection in gait analysis, as it significantly impacts the accuracy of kinematic and spatiotemporal measurements. This insight is pivotal for clinicians and researchers to develop more effective rehabilitation strategies and comprehensively understand gait dynamics.

Validity and reliability of running gait measurement with the ViMove2 system.

Running biomechanics have traditionally been analysed in laboratory settings, but this may not reflect natural running gait. Wearable technology has the potential to enable precise monitoring of running gait beyond the laboratory. This study aimed to evaluate the analytical validity and intra-session reliability of temporal running gait outcomes measured by the ViMove2 wearable system in healthy adults. Seventy-four healthy adults (43 males, 31 females, aged 18-55 years) wore the inertial device, ViMove2 on the tibia. Participants ran on a treadmill for one minute at various speeds (8, 10, 12, 14km/hr), completed in a standardised shoe (Saucony Guide Runner). Running gait was measured with the ViMove2 wearable and 3D motion capture (Vicon). Temporal running gait outcomes included ground contact time (GCT) and cadence (steps/min). GCT and cadence from the ViMove2 had face validity with expected changes in outcome with different running speeds, but ViMove2 tended to over-estimate GCT, and under-estimate cadence compared to the reference, especially at slower speeds. GCT demonstrated moderate to good agreement to the reference at speeds >10km/hr, but poor agreement at 8km/hr and within female runners. Cadence had moderate to excellent agreement across speeds compared to the reference. GCT and cadence had excellent reliability across speeds, but at 8km/hr GCT had good agreement between trials. Overall, temporal gait outcomes of GCT and cadence can be measured with the ViMove2, but accuracy and reliability are impacted at slow running speeds and within female runners. Future work is needed to clarify sex or speed-dependent corrections to algorithms / outcomes to aid interpretation and application.

Modeling, Simulation, and Kinematic Validation of Transfemoral Prosthetic Mechanism With Ankle Varus–Valgus Characteristic

Abstract Inspired by the kinesiology of human bionic joints, a transfemoral prosthetic mechanism based on a functional structure of parallel mechanism is developed for the transfemoral amputees. The walking interactive simulation is implemented based on human-prosthesis modeling to verify the kinematics of the designed prosthetic mechanism, as well as to explore compatibility between the amputees and prosthesis. Then, simulation-based prosthetic optimization is performed to pursue an optimized human-prosthesis model with economic metabolic consumption while eliminating compatibility errors including the joints' misalignment error between the affected limb and healthy limb, and the assembly error between human and prosthesis, so that the potential physical health problems can be avoided efficiently. This method is valuable for the optimal design of interactive rehabilitation robots. Finally, a developed proportional-integral-derivative-based (PID-based) finite-state machine (FSM) strategy is used, and the kinematic validation is carried out. The results show that the designed prosthesis possesses ankle varus–valgus characteristic, and it has a high human-like motion accuracy due to the FSM control can track prosthetic motion in each gait event. What's more, the prosthetic optimization can be an efficient method to enhance the biomechanical performance of human-prosthetic model so that the amputees have a more natural and symmetry gait.

Gait self-learning control based on reference trajectory generation online for an asymmetric limb rehabilitation exoskeleton

Lower limb exoskeleton (LEX) are widely used to assist stoke survivors with walking dysfunction, which is lack of a more flexible trajectory and fails to address the control challenge posed by gait variability and asymmetry in rehabilitation training. This paper introduces an asymmetric self-learning lower exoskeleton (AS-LEX) based on reference trajectory generation for the affected side. Motor intent of the unaffected limb based on thresholds was identified to classify the gait phase of stance and swing. A parameterized gait trajectory was generated online, namely a combination of circular trajectory in the stance phase and an elliptical trajectory in the swing phase. Gait self-learning control is presented to make the affected limb adaptively learn the gait parameters generated by the unaffected limb. Feasibility of the AS-LEX is demonstrated experimentally using three healthy subjects. Resuls demonstrate that overground walking in a more natural speed (with a stride length 600 mm and 700 mm) make subjects more actively learn gait of the affected side from the unaffected side. Additionally, experiments of the fatigue level of the affected limb and human-robot interaction torques were carried out, and the results indicate a more natural gait and reduced interaction forces with the AS-LEX.

Brain-wide neural recordings in mice navigating physical spaces enabled by robotic neural recording headstages.

Technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales are typically much larger than the animals that are being recorded from and are thus limited to recording from head-fixed subjects. Here we have engineered robotic neural recording devices-'cranial exoskeletons'-that assist mice in maneuvering recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. We discovered optimal controller parameters that enable mice to locomote at physiologically realistic velocities while maintaining natural walking gaits. We show that mice learn to work with the robot to make turns and perform decision-making tasks. Robotic imaging and electrophysiology headstages were used to record recordings of Ca2+ activity of thousands of neurons distributed across the dorsal cortex and spiking activity of hundreds of neurons across multiple brain regions and multiple days, respectively.

Conditional Adversarial Motion Priors by a Novel Retargeting Method for Versatile Humanoid Robot Control

ABSTRACTSignificant advancements have been made in the field of humanoid robot, particularly in walking control strategies. However, achieving straight‐legged walking remains a challenge. Both the traditional model‐based and the learning‐based control methods confront with difficulties in achieving natural humanoid gait feature. To address this issue, a general motion retargeting method is developed and also evaluated for humanoid robots with different structure, size and degrees of freedom. Moreover, a conditional adversarial motion priors method is proposed based on reinforcement learning and validated on the humanoid robot GTX‐III. Through various motion segments from the motion capture database, it is shown that this method can successfully enable the humanoid robot to perform straight‐legged walking with flexible and natural transitions between different gaits within a single discriminator network.

Physiotherapy-assisted overground exoskeleton use: mixed methods feasibility study protocol quantifying the user experience, as well as functional, neural, and muscular outcomes in children with mobility impairments.

Early phase research suggests that physiotherapy paired with use of robotic walking aids provides a novel opportunity for children with severe mobility challenges to experience active walking. The Trexo Plus is a pediatric lower limb exoskeleton mounted on a wheeled walker frame, and is adjustable to fit a child's positional and gait requirements. It guides and powers the child's leg movements in a way that is individualized to their movement potential and upright support needs, and can provide progressive challenges for walking within a physiotherapy-based motor learning treatment paradigm. This protocol outlines a single group mixed-methods study that assesses the feasibility of physiotherapy-assisted overground Trexo use in school and outpatient settings during a 6-week physiotherapy block. Children ages 3-6 years (n = 10; cerebral palsy or related disorder, Gross Motor Function Classification System level IV) will be recruited by circle of care invitations to participate. Study indicators/outcomes will focus on evaluation of: (i) clinical feasibility, safety, and acceptability of intervention; (ii) pre-post intervention motor/functional outcomes; (iii) pre-post intervention brain structure characterization and resting state brain connectivity; (iv) muscle activity characterization during Trexo-assisted gait and natural assisted gait; (v) heart rate during Trexo-assisted gait and natural assisted gait; and (vi) user experience and perceptions of physiotherapists, children, and parents. This will be the first study to investigate feasibility indicators, outcomes, and experiences of Trexo-based physiotherapy in a school and outpatient context with children who have mobility challenges. It will explore the possibility of experience-dependent neuroplasticity in the context of gait rehabilitation, as well as associated functional and muscular outcomes. Finally, the study will address important questions about clinical utility and future adoption of the device from the physiotherapists' perspective, comfort and engagement from the children's perspective, and the impressions of parents about the value of introducing this technology as an early intervention. https://clinicaltrials.gov, identifier NCT05463211.

A Review of Knee Ankle-Foot Orthoses in Managing Neuromuscular Disorders: Focus on Children with Cerebral Palsy

Neuromuscular disorders (NMDs) significantly impact the musculoskeletal system, leading to lower extremity muscle weakness and impaired coordination, notably affecting the quadriceps. These impairments compromise knee stability during standing and walking, increasing fall risks and hindering mobility and independence. Knee-ankle-foot orthoses (KAFOs) are vital in enhancing gait efficiency and knee stability. Traditional locked KAFOs rigidly immobilize the knee throughout the gait cycle, offering maximal stability but limited natural movement. Advances in orthotic technology have led to the development of stance control KAFOs (SCKAFOs), which dynamically lock the knee only during the stance phase, promoting a more natural and energy-efficient gait. The E-MAG Active and NEURO TRONIC knee joints are prominent examples of SCKAFOs, featuring sophisticated electronic control mechanisms to improve functional outcomes in individuals with NMDs. This review also focuses on the application of SCKAFOs in children with cerebral palsy (CP), the most common neuromotor disorder affecting movement, posture, and muscle tone due to early brain injury. The prevalence and diverse etiologies of CP necessitate individualized therapeutic interventions. The review aims to evaluate the efficacy and satisfaction of orthotic management in pediatric CP, providing evidence-based recommendations for optimizing orthotic interventions and enhancing quality of life for affected children and their families.

3D Modelling and Simulation Based on Kinematic Analysis for Hippotherapy

The engineering perspective on the physiology and anatomy of living things has led to the emergence of the science of biomechanics. Developing computer technologies have made it possible to examine the behaviour of living things in more detail and to develop technologies that enable kinetic and kinematic analysis of movements to imitate real limbs. In this study, the natural gaits of horses (Walk, Canter and Trot) were analysed with image processing techniques, kinematic analyses were made and three-dimensional solid models were created. Giving movement ability to the extracted solid models was carried out with the simulation software prepared on the Matlab-SimMechanic platform. By comparing the findings obtained as a result of image processing and simulation, the success of transferring the movements to a developed system was evaluated. After mathematical equations were created in the MATLAB environment, the equations were tested on the developed system and their performance rates were determined. With the developed system, joint angles, ability to reach the correct position and movement ability were analysed and it was shown how the mathematical equations required to perform the movements could be derived. As a result, a design process model for movement analysis, simulation and control of different living groups is presented with the equations obtained by formulating the information obtained from the developed system.

DE-AFO: A Robotic Ankle Foot Orthosis for Children with Cerebral Palsy Powered by Dielectric Elastomer Artificial Muscle.

Conventional passive ankle foot orthoses (AFOs) have not seen substantial advances or functional improvements for decades, failing to meet the demands of many stakeholders, especially the pediatric population with neurological disorders. Our objective is to develop the first comfortable and unobtrusive powered AFO for children with cerebral palsy (CP), the DE-AFO. CP is the most diagnosed neuromotor disorder in the pediatric population. The standard of care for ankle control dysfunction associated with CP, however, is an unmechanized, bulky, and uncomfortable L-shaped conventional AFO. These passive orthoses constrain the ankle's motion and often cause muscle disuse atrophy, skin damage, and adverse neural adaptations. While powered orthoses could enhance natural ankle motion, their reliance on bulky, noisy, and rigid actuators like DC motors limits their acceptability. Our innovation, the DE-AFO, emerged from insights gathered during customer discovery interviews with 185 stakeholders within the AFO ecosystem as part of the NSF I-Corps program. The DE-AFO is a biomimetic robot that employs artificial muscles made from an electro-active polymer called dielectric elastomers (DEs) to assist ankle movements in the sagittal planes. It incorporates a gait phase detection controller to synchronize the artificial muscles with natural gait cycles, mimicking the function of natural ankle muscles. This device is the first of its kind to utilize lightweight, compact, soft, and silent artificial muscles that contract longitudinally, addressing traditional actuated AFOs' limitations by enhancing the orthosis's natural feel, comfort, and acceptability. In this paper, we outline our design approach and describe the three main components of the DE-AFO: the artificial muscle technology, the finite state machine (the gait phase detection system), and its mechanical structure. To verify the feasibility of our design, we theoretically calculated if DE-AFO can provide the necessary ankle moment assistance for children with CP-aligning with moments observed in typically developing children. To this end, we calculated the ankle moment deficit in a child with CP when compared with the normative moment of seven typically developing children. Our results demonstrated that the DE-AFO can provide meaningful ankle moment assistance, providing up to 69% and 100% of the required assistive force during the pre-swing phase and swing period of gait, respectively.

Development and calibration of large deformation-compliant six-axis force sensor

Improving the measurement accuracy and minimising the coupling between directions are the keys to researching the compliant six-axis force sensors. The use of a six-axis force sensor to accurately monitor the ground reaction force and centre of pressure during human motion is of great significance in the fields of biomechanics and pathological gait diagnosis. Although complete force information can be obtained using a commercial six-axis force sensor, its high stiffness affects the natural gait and easily leads to human fatigue. A compliant six-axis force sensor based on a flexible optical waveguide is proposed, in which the force and torque of six dimensions are detected by reasonably arranging six modular sensing units, and the mechanical decoupling of some dimensions is realised in theory. For the interdimensional coupling and error caused by machining process factors, as well as the nonlinear relationship between the input and output of the proposed compliant six-axis force sensor, a DE-RBF decoupling algorithm is proposed to decouple the calibration data. Compared with the least squares method (LSM) and the radial basis function (RBF) neural network decoupling algorithm, the obtained type-I errors were reduced by 87.7629%, 43.6265%, respectively, and type-II errors by 35.3312%, 56.9162%, respectively. The decoupling result’s maximum type-I and type-II errors were reduced from 7.7125% and 2.7382% in LSM and 3.1029% and 2.8917% in RBF to 0.5916% and 0.9558%, respectively. The measurement accuracy of the compliant six-axis force sensor was significantly higher; however, the time effectiveness of the proposed DE-RBF decoupling algorithm was slightly lower than that of the RBF neural network by 2.47%. In conclusion, the decoupling accuracy and timeliness of the proposed DE-RBF decoupling algorithm can satisfy the requirements of compliant six-axis force sensors to monitor low-frequency biomechanical signals, such as human motion.

Investigation of intersegmental coordination patterns in human walking

BackgroundIntersegmental coordination between thigh, shank, and foot plays a crucial role in human gait, facilitating stable and efficient human walking. Limb elevation angles during the gait cycle form a planar manifold describes the by the planar covariation law, a recognized fundamental aspect of human locomotion. Research questionHow does the walking speed, age, BMI, and height, affect the size and orientation of the intersegmental coordination manifold and covariation plane? MethodsThis study introduces novel metrics for quantifying intersegmental coordination, including the mean radius of the manifold, rotation of the manifold about the origin, and the orientation of the plane with respect to the coordinate planes. A statistical investigation is conducted on a publicly available human walking dataset for subjects aged 19–67 years, walking at speeds between 0.18 and 2.3 m s−1 to determine correlations of the proposed quantities. We used two sample t-test and ANOVA to find statistical significance of changes in the metrics with respect to gender and walking speed, respectively. Regression analysis was used to establish relationships between the introduced metrics and walking speed. ResultsHigh correlations are observed between walking speed and the computed metrics, highlighting the sensitivity of these metrics to gait characteristics. Conversely, negligible correlations are found for demographic parameters like age, body mass index (BMI), and height. Male and female groups exhibit no practically significant differences in any of the considered metrics. Additionally, metrics tend to increase in magnitude as walking speed increases. SignificanceThis study contributes numerical metrics to characterize ISC of lower limbs with respect to walking speed along with regression models to estimate these metrics and related kinematic quantities. These findings hold significance for enhancing clinical gait analysis, generating optimal walking trajectories for assistive devices, prosthetics, or rehabilitation, aiming to replicate natural gaits and improve the functionality of biomechanical devices.

Development of an Elderly Care Rehabilitation System Utilizing Machine Learning for Energy Monitoring and Transmission using Wireless Sensor Networks

The combination of robotic exoskeleton systems and machine learning (ML) techniques present great potential for significantly improving rehabilitation outcomes in patients with mobility impairments. This particular study examines whether ML algorithms can predict torque and angular inputs required for joint rotation using electromyography (EMG) data obtained during patient lower-limb rehabilitation. A new exoskeleton was designed for personalized and adaptable assistance in rehabilitation of walking. The predictive power of four different ML models, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM), is evaluated. Of these, the ANN model is the most effective with an accuracy rate of 98.87%, followed by KNN (94.55%), DT (91.2%) and SVM (89.45%). Subsequent patient tests confirm significant improvements in flexion and extension angles of the knee, suggesting improved mobility and restored natural gait mechanics. These results stress the potential uses of ML-enhanced exoskeleton systems in personalised rehabilitation therapy. Proposals for further work include improving model performance, tackling challenges for real-time processing, and evaluating ML performance over the long term as it affects the quality of life in individuals with mobility impairments or changes in their condition over time. This investigation into rehabilitation aims at applying technology-driven solutions to help people regain their freedom and ability to move in a way that pleases them.

Another Step Forward for Freezing of Gait in Parkinson's Disease.

The study "A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease" by Milekovic et al. introduces a novel neuroprosthesis for treating locomotor deficits in late-stage Parkinson's disease (PD). This approach employs an epidural spinal array targeting dorsal roots and electromyography to create a spatiotemporal map of muscle activation, aiming to restore natural gait patterns. Significant improvements in gait freezing and balance were observed in both non-human primate models and a human patient, resulting in improved mobility and quality of life. This innovative method, integrating real-time feedback and non-invasive motor intention decoding, marks a significant advancement in PD treatment.

An Able-Bodied Study for Potential Usage of a Knee Scooter as a Constraint-Induced Movement Therapy (CIMT) Gait Training Device.

Post-stroke gait is characterized by slow and asymmetrical hemiparetic gait. This is attributed to the paretic lower limb which has limited plantar propulsion. The most effective method to restore paretic limb function is constraint-induced movement therapy (CIMT), which promotes the usage of the paretic limb by restricting the movement of the unafflicted limb. However, due to the necessity of both lower limbs to perform gait, CIMT methods could not be directly applied for gait rehabilitation. In this study, we explore the feasibility of utilizing a knee scooter as a means to facilitate CIMT gait training. We hypothesize that if lower limb kinematics and muscle activation patterns during gait with a knee scooter match that of natural gait, the knee scooter could be utilized for CIMT gait training. We measured the lower-limb joint angles, plantar force, EMG patterns, stride length, and step times of 13 healthy subjects during gait with a knee scooter and natural gait. The results suggest that the gait patterns while using the knee scooter closely resemble those of natural gait.

Gait patterns during overground and virtual omnidirectional treadmill walking

BackgroundOmnidirectional treadmills (ODTs) offer a promising solution to the virtual reality (VR) locomotion problem, which describes the mismatch between visual and somatosensory information and contributes to VR sickness. However, little is known about how walking on ODTs impacts the biomechanics of gait. This project aimed to compare overground and ODT walking and turning in healthy young adults.MethodsFifteen young adults completed forward walk, 180° turn, and 360° turn tasks under three conditions: (1) overground, (2) on the Infinadeck ODT in a virtual environment without a handrail, and (3) on the ODT with a handrail. Kinematic data for all walking trials were gathered using 3D optical motion capture.ResultsOverall, gait speed was slower during ODT walking than overground. When controlling for gait speed, ODT walking resulted in shorter steps and greater variability in step length. There were no significant differences in other spatiotemporal metrics between ODT and overground walking. Turning on the ODT required more steps and slower rotational speeds than overground turns. The addition of the stability handrail to the ODT resulted in decreased gait variability relative to the ODT gait without the handrail.ConclusionWalking on an ODT resembles natural gait patterns apart from slower gait speed and shorter step length. Slower walking and shorter step length are likely due to the novelty of physically navigating a virtual environment which may result in a more conservative approach to gait. Future work will evaluate how older adults and those with neurological disease respond to ODT walking.

Design of a novel low-speed gait rehabilitation device incorporating a synthesized geared five-bar mechanism

People with difficulty walking due to a stroke or neurophysiological gait deficit require gait rehabilitation. Recent research indicates that the optimal speed range for gait rehabilitation of such patients is less than 0.4 m/s. The design of a novel low-speed gait rehabilitation device based on a single degree of freedom (DOF) geared five-bar mechanism is presented in this paper. For this purpose, a natural gait meta-trajectory is generated using the recently acquired low-speed gait data suitable for human walking. A less-bulky single-DOF geared five-bar mechanism is synthesized and adjusted by applying the accurate and robust shadow robot control (SRC) synthesis method. Therefore, a practical and easily controlled gait rehabilitation device is designed, which can be mounted on any commercial treadmill and enables the patient to walk as close as possible to a normal gait. This device applies to a wide variety of individuals with different physiologies due to the mechanism’s adjustability, provided by the profile blending algorithm method. This method generates a set of continuous and speed-dependent gait trajectories for various individuals. The proposed device is inherently safe and easy to control due to the limited motion range of the single-DOF mechanisms and the use of a single actuator. Furthermore, because it only has two points of contact with the body (at the hip and the ankle), it takes less time for the user to don and doff.

Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton.

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation.

Virtual muscles and reflex control generates human-like ankle torques during gait perturbations.

A biologically-inspired actuation system, including muscles, spinal reflexes, and vestibular feedback, may be capable of achieving more natural gait mechanics in powered prostheses or exoskeletons. In this study, we developed a Virtual Muscle Reflex (VMR) system to control ankle torque and tuned it using data from human responses to anteroposterior mechanical perturbations at three walking speeds. The system consists of three Hill-Type muscles, simulated in real time, and uses feedback from ground reaction force and from stretch sensors on the virtual muscle fibers. Controller gains, muscle properties, and reflex/vestibular time delays were optimized using Covariance Matrix Adaptation (CMA) to minimize the difference between the VMR torque output and the torque measured from the experiment. We repeated the procedure using a conventional finite-state impedance controller. For both controllers, the coefficient of determination () and root-mean-square error (RMSE) was calculated as a function of time within the gait cycle. The VMR had lower RMSE than the impedance controller in 70%, and in 60% of the trials, the of the VMR controller was higher than for the impedance controller. We concluded that the VMR system can better reproduce the human responses to perturbations than the impedance controller.

Rethinking Exoskeleton Simulation-Based Design: The Effect of Using Different Cost Functions.

Designing an exoskeleton that can improve user capabilities is a challenging task, and most designs rely on experiments to achieve this goal. A different approach is to use simulation-based designs to determine optimal device parameters. Most of these simulations use full trajectory tracking limb kinematics during a natural gait as a reference. However, exoskeletons typically change the natural gait kinematics of the user. Other types of simulations assume that human gait is optimized for a cost function that combines several objectives, such as the cost of transport, injury prevention, and stabilization. In this study, we use a 2D OpenSim model consisting of 10 degrees of freedom and considering 18 muscles, together with the Moco optimization tool, to investigate the differences between these two approaches with respect to running with a passive knee exoskeleton. Utilizing this model, we test the effect of a full trajectory tracking objective with different weights (representing the importance of the objective in the optimization cost function) and show that when using weights that are typically used in the literature, there is no deviation from the experimental data. Next, we develop a multi-objective cost function with foot clearance term based on peak knee angle during swing, that achieves trajectories similar (RMSE=7.4 deg) to experimental running data. Finally, we investigate the effect of different parameters in the design of a clutch-based passive knee exoskeleton (1.5 kg at each leg) and find that a design that utilizes a 2.5 Nm/deg spring achieves an improvement of up to 8% in net metabolic energy. Our results show that tracking objectives in the cost function, even with a low weight, hinders the simulation's ability to change the gait trajectory. Thus, there is a need for other predictive simulation methods for exoskeletons.