Basic Gait Research Articles

FAULT-TOLERANT ADAPTIVE CONTROL OF A WALKING HEXAPOD WITH FAILURE OF ONE LEG

The article is devoted to the improvement of the control system of a walking mobile robot with six legs (hexapod) in order to ensure its stable movement in case of damage or failure of one limb. A tripod gait is considered as the basic movement algorithm. The failure of one of the joints is considered as a failure of the limb. To ensure movement in the event of failure of one limb, a tripod walking algorithm has been developed, which ensures that each of the defect-free limbs remains in the stage of stable support during two phases of movement in a row. The evaluation of the possibility of fault-tolerant gait of the hexapod according to the developed algorithm was performed on the basis of support triangles and a quadrangle between the limbs that are in the support phase. The structural and functional synthesis of the system of fault-tolerant adaptive control of the hexapod, which consists of the following modules and subsystems, was performed: module of the dynamic model of the robot; basic gait algorithm module; subsystem of executive elements; diagnosis and decision-making subsystem; a subsystem of alternative gait algorithms. The structural and functional scheme of the control channel was developed and substantiated for the implementation of the algorithm for diagnosing the joint of one limb, making a decision about its condition and switching to the continuation of the basic gait algorithm, if the condition of the joint is assessed as defect-free. The scheme of the control channel for switching to the execution of an alternative gait algorithm, if the condition of the joint is assessed as defective, has been developed and substantiated. The proposed structural and functional solutions are generalized for the case of failure of other joints of the considered limb and other limbs of the hexapod. It is shown that based on the detection and localization of joint failures of the hexapod limbs, the reconfiguration of the gait algorithm and the control system takes place, and in this way a fault-tolerant adaptive control of the movement of the hexapod is realized. The implementation of the proposed alternative tripod gait algorithms and control channels in the fault-tolerant adaptive control system will ensure stable movement of the hexapod in case of failure of one of the limbs.

Effect of posterior-stabilized and cruciate-retaining implants on three-dimensional kinematic characteristics after total knee arthroplasty.

This study aimed to analyze the effects of posteriorstabilized (PS) and cruciate-retaining (CR) total knee arthroplasty (TKA) on early postoperative three-dimensional (3D) dynamic and kinematic characteristics in patients with unilateral knee osteoarthritis (OA). A retrospective analysis of prospectively collected data from 90 patients with unilateral TKA between February 2021 and September 2021 was conducted using a 3D kinematic analysis system before and six months after TKA. This patient group included 57 patients (10 males, 47 females; mean age: 69.5±7.5 years; range, 53 to 85 years) who underwent PS TKA and 33 patients (11 males, 22 females; mean age: 67.9±8.8 years; range, 45 to 86 years) who underwent CR TKA. The kinematic characteristics and clinical results of the two groups were compared. Clinical evaluation included the Hospital for Special Surgery knee score and range of motion (ROM). Twenty-eight healthy controls (9 males, 19 females; mean age: 64.5±2.9 years; range, 61 to 75 years) without knee OA matched for age, weight, height, and body mass index were recruited. The kinematic characteristics of the healthy control group were also evaluated. The PS group exhibited significant changes in basic gait parameters after TKA, including cadence (p=0.046), stride time (p=0.011), opposite foot off (p<0.001), opposite foot contact (p=0.038), step time (p=0.005), double support period (p<0.001), and foot off (p=0.004). No significant differences were observed in the kinematic parameters before and after TKA between the PS and CR groups, such as knee angle, moment, and force. The dynamic ROM of the CR group was greater than that of the PS group (p<0.001). Both the PS and CR groups showed significant deficiencies in flexion and extension function, including knee flexion moment, extension force, maximum flexion angle, and dynamic ROM, compared to healthy individuals. Throughout the gait cycle, both the PS and CR groups showed better knee joint stability compared to healthy individuals. At six months postoperatively, both the PS and CR groups' gait patterns did not recover to a healthy state, and the CR group's gait pattern was more similar to OA. Compared to PS TKA, CR TKA allowed for greater dynamic ROM during gait. Despite exhibiting superior knee stability during gait, both implants' knee kinematics function remained inferior compared to healthy individuals.

Association of oral frailty and gait characteristics in patients with cerebral small vessel disease

BackgroundThe objectives of this study were twofold: (1) to compare gait characteristics between cerebral small vessel disease (CSVD) patients with low-risk oral frailty (OF) and high-risk OF, particularly during dual-task walking (DTW); (2) to investigate the association of OF, the gait characteristics of DTW, and falls among older adults patients with CSVD.MethodsA total of 126 hospitalized patients diagnosed with CSVD were recruited and classified into a low-risk group (n = 90) and a high-risk group (n = 36) based on OF status in our study. Comprehensive data pertaining to basic parameters (cadence, as well as stride time, velocity and length), variability, asymmetry, and coordination were gathered during both single-task walking (STW) and DTW. Additionally, the number of falls was calculated. Subsequently, t-test or chi-squared test was used for comparison between the two groups. Furthermore, linear regression analysis was employed to elucidate the association of the OF index-8 score and gait parameters during cognitive DTW. Also, logistic regression models were utilized to assess the independent association of OF risk and falls.ResultsDuring cognitive DTW, the high-risk group demonstrated inferior performance in terms of basic parameters (p < 0.01), coefficient of variation (CV) of velocity and stride length (p < 0.05), as well as phase coordination index (PCI) when compared with the low-risk group (p < 0.05). Notably, differences in basic gait parameters were observed in cognitive DTW and STW conditions between the two groups (p < 0.01). However, only the high-risk group evinced significant variations in CV and PCI during cognitive DTW, as opposed to those during STW (p < 0.05). Furthermore, our findings also revealed the association of OF, the gait characteristics of cognitive DTW, (p < 0.01) and falls (p < 0.05).ConclusionCSVD patients with a high risk of OF need to pay more attention to their gait variability or coordination. Also, they are recommended to undergo training involving dual-task activities while walking in daily life, thereby reducing the deterioration and mitigating the risk of falls. Besides, this study has confirmed an association of OF and DTW gait as well as falls in patients with CSVD.

Cognitive load in individuals with a transfemoral amputation during single- and dual-task walking: a pilot study of brain activity in people using a socket prosthesis or a bone-anchored prosthesis.

To explore cognitive load in people with transfemoral amputations fitted with socket or bone-anchored prostheses by describing activity in the left and right dorsolateral prefrontal cortices during single- and dual-task walking. Cross-sectional pilot study. 8 socket prosthesis users and 8 bone-anchored prosthesis users. All were fitted with microprocessor-controlled prosthetic knees. Participants answered self-report questionnaires and performed gait tests during 1 single-task walking condition and 2 dual-task walking conditions. While walking, activity in the dorsolateral prefrontal cortex was measured using functional near-infrared spectroscopy. Cognitive load was investigated for each participant by exploring the relative concentration of oxygenated haemoglobin in the left and right dorsolateral prefrontal cortex. Symmetry of brain activity was investigated by calculating a laterality index. Self-report measures and basic gait variables did not show differences between the groups. No obvious between-group differences were observed in the relative concentration of oxygenated haemoglobin for any walking condition. There was a tendency towards more right-side brain activity for participants using a socket prosthesis during dual-task conditions. This pilot study did not identify substantial differences in cognitive load or lateralization between socket prosthesis users and bone-anchored prosthesis users.

Changes in gait parameters and pelvic movement during gait under the influence of light loads carried symmetrically or asymmetrically — preliminary research

Introduction: Walking is one of the basic human activities. Taking care of the correct gait pattern is important for maintaining proper body posture and preventing dysfunctions of the musculoskeletal system. Since walking often involves carrying a load, the study aimed to evaluate changes in gait parameters and pelvic movement during gait under the influence of light loads carried symmetrically or asymmetrically in a bag or backpack. Material and methods: The group of 11 women and 15 men aged between 19 and 25 were examined. The BTS G-Sensor device was used. The basic gait parameters were examined, as well as the symmetry coefficient of pelvic motion and the range of pelvic motion in each of the three planes. The test involving walking 30 meters was repeated four times: while walking without a load, with an additional load worn in a backpack on both shoulders, in a bag on the right shoulder, or in a bag worn diagonally on the left shoulder. Each time the load was equal to 10% of the participantʼs body weight. Results: Walking speed, stride length to body height ratio, gait symmetry coefficient, and left and right foot propulsive speed did not change significantly during walking while carrying a load of 10% of body weight, regardless of the method of carrying a bag or backpack. Conclusions: Although carrying a light bag or backpack does not change the basic gait parameters, even such a small load reduces the symmetry of pelvic movement and the range of pelvic motion in the frontal and transverse planes during walking.

The impact of varying trolley case usage modes and weights on body posture

ObjectiveTo study the body posture characteristics when walking with trolley case, and to explore the effects of different usage methods and weights of trolley case on body posture characteristics. MethodsFifteen subjects pushed and pulled(Condition 1 and 2) the case with three load weights of 10 %, 20 % and 30 % of their own body weight with 0 % no load as baseline for both conditions. The basic gait parameters, kinematic and kinetic data were collected using the VICON infrared motion capture system and a 3D force platform. Two repeated measures factor (condition×weight) analysis of variance was used for statistical analysis of the gait temporal and spatial parameters, as well as trunk angle, kinetic ground reaction force, shoulder joint force, and trunk moment. ResultsSignificant condition*weight interactions were detected in DLST (Double Limb Stance Time) (F=5.341，P = 0.006), GRF (Ground Reaction Force) in frontal plane (F=10.507, p < 0.001) and vertical plane (F=3.751, p = 0.021), shoulder joint force in sagittal plane (F=21.129, p < 0.001), and flexion-extension angle of the trunk in the sagittal plane (F=4.888, p < 0.010). Significant main effects were detected in walking speed (F=35.842, p < 0.001), right support time (F=12.156, p < 0.001), left swing time (F=8.506, p < 0.001), left support time (F=1.122, p < 0.001), right step length (F=33.900, p < 0.001), and left step length (F=14.960, p < 0.001) under different weights. A significant main effect was detected in sagittal GRF (F=11.77, p < 0.001), trunk rotation angle (F=4.124, p = 0.016), amplitude of COM (F=2.993, p = 0.046), under different weights. ConclusionWhen the weight of the case exceeds 20 % of the body weight, from the perspective of energy efficiency, the push method is more advantageous than the pull method. When walking with luggage, people tend to maintain the stability of their trunk posture by adjusting the force on their arms more often.

Towards automated self-administered motor status assessment: Validation of a depth camera system for gait feature analysis

BackgroundGait feature analysis plays an important role in diagnosing and monitoring diseases that compromise motor function. This article presents the results of a study, which was aimed at assessing the accuracy and precision of computer-aided gait feature analysis performed with a system based on Microsoft® Azure™ Kinect™ Cameras (AzureKinect). Research questionCan an AzureKinect-based system measure basic gait parameters with sufficient accuracy for motor status assessments? MethodsThe presented AzureKinect-based system was evaluated by measuring the step length (SL), cadence, and velocity, which are important gait features, of both healthy participants and participants with a neurological motor impairment (total number of participants: N = 24). The GAITRite® system, which is an established gold standard for gait analysis, was used as the ground truth. ResultsThe results show that the AzureKinect-based system can provide measurements of average SL, cadence, and velocity. A comparison with the ground truth revealed a mean absolute error (MAE) of 1.74 cm in SL, 4.6 cm/s in gait velocity and 6.3 steps/min for cadence. Pearson’s correlation coefficients range from r = 0.8 to r = 0.99, demonstrating a very high correlation between the measurements of the AzureKinect system and the ground truth. SignificanceThe AzureKinect-based system is able to measure basic gait parameters with sufficient accuracy. This is a first step towards a comprehensive self-measuring marker-less camera-based kinematic analysis that could be performed at home or in general medical practices.

Gait analysis algorithm for lower limb rehabilitation robot applications

Abstract. When patients with lower limb dyskinesia use robots for rehabilitation training, gait parameters are of great significance for disease diagnosis and rehabilitation evaluation. Gait measurement is usually carried out by using optical motion capture systems, pressure plates and so on. However, it is difficult to apply these systems to lower limb rehabilitation robots due to their high price, limited scope and wearing requirements. At the same time, most of the current applications in robots focus on the basic gait parameters (such as step length and step speed) for robot control or user intention recognition. Therefore, this paper proposes an online gait analysis algorithm for lower limb rehabilitation robots, which uses a lidar sensor as the gait data acquisition sensor. The device is installed on the lower limb rehabilitation robot, which not only avoids the problems of decline in the detection accuracy and failure of leg tracking caused by lidar placement on the ground, but it also calculates seven gait parameters, such as step length, stride length, gait cycle and stance time, with high precision in real time. At the same time, the walking track of the patient may not be straight, and the lidar coordinate system is also changed due to the movement of the lower limb rehabilitation robot when the patient moves forward. In order to overcome this situation, a spatial parameter-splicing algorithm based on a time series is proposed to effectively reduce the error impact on gait spatiotemporal parameters. The experimental results show that the gait analysis algorithm proposed in this paper can measure the gait parameters effectively and accurately. Except for the swing time and double support time, which are calculated with large relative errors due to their small values, the relative errors of the remaining gait parameters are kept below 8 %, meeting the requirements of clinical applications.

Sitting and Standing Intention Detection Based on Dynamical Region Connectivity and Entropy of EEG

Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human’s brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation.

Intelligent proximal-policy-optimization-based decision-making system for humanoid robots

With the advancements in technology, robots have gradually replaced humans in different aspects. Allowing robots to handle multiple situations simultaneously and perform different actions depending on the situation has since become a critical topic. Currently, training a robot to perform a designated action is considered an easy task. However, when a robot is required to perform actions in different environments, both resetting and retraining are required, which are time-consuming and inefficient. Therefore, allowing robots to autonomously identify their environment can significantly reduce the time consumed. How to employ machine learning algorithms to achieve autonomous robot learning has formed a research trend in current studies. In this study, to solve the aforementioned problem, a proximal policy optimization algorithm was used to allow a robot to conduct self-training and select an optimal gait pattern to reach its destination successfully. Multiple basic gait patterns were selected, and information-maximizing generative adversarial nets were used to generate gait patterns and allow the robot to choose from numerous gait patterns while walking. The experimental results indicated that, after self-learning, the robot successfully made different choices depending on the situation, verifying this approach’s feasibility.

Validation of Inertial Sensors to Evaluate Gait Stability.

The portability of wearable inertial sensors makes them particularly suitable for measuring gait in real-world walking situations. However, it is unclear how well inertial sensors can measure and evaluate gait stability compared to traditional laboratory-based optical motion capture. This study investigated whether an inertial sensor-based motion-capture suit could accurately assess gait stability. Healthy adult participants were asked to walk normally, with eyes closed, with approximately twice their normal step width, and in tandem. Their motion was simultaneously measured by inertial measurement units (IMU) and optical motion capture (Optical). Gait stability was assessed by calculating the margin of stability (MoS), short-term Lyapunov exponents, and step variability, along with basic gait parameters, using each system. We found that IMUs were able to detect the same differences among conditions as Optical for all but one of the measures. Bland-Altman and intraclass correlation (ICC) analysis demonstrated that mediolateral parameters (step width and mediolateral MoS) were measured less accurately by IMUs compared to their anterior-posterior equivalents (step length and anterior-posterior MoS). Our results demonstrate that IMUs can be used to evaluate gait stability through detecting changes in stability-related measures, but that the magnitudes of these measures might not be accurate or reliable, especially in the mediolateral direction.

Torque Simulation and Analysis of Quadruped Robot under Trot Gait

The quadruped robot can overcome the unevenness of the wheeled chassis on steps and gravel roads, and has good passability. The Trot gait is the basic gait of the quadruped robot, and the walking is realized by the gait of alternating footsteps. In this paper, the dynamic simulation of the static equilibrium state of the quadruped robot in different poses on the ground and stair is carried out, and the movement of the robot body under the trot gait is simulated to obtain the joint torques of the upper and lower leg of the quadruped robot, which is the driving torque of the joint motor. The model selection provides a theoretical basis, and at the same time, the motion analysis of the quadruped robot is carried out, and the moving distance of the chassis under the extreme posture is planned.

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review.

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

Consistency of vertebral motion and individual characteristics in gait sequences

Vertebral motion reveals complex patterns, which are not yet understood in detail. This applies to vertebral kinematics in general but also to specific motion tasks like gait. For gait analysis, most of existing publications focus on averaging characteristics of recorded motion signals. Instead, this paper aims at analyzing intra- and inter-individual variation specifically and elaborating motion parameters, which are consistent during gait cycles of particular persons. For this purpose, a study design was utilized, which collected motion data from 11 asymptomatic test persons walking at different speed levels (2, 3, and 4 km/h). Acquisition of data was performed using surface topography. The motion signals were preprocessed in order to separate average vertebral orientations (neutral profiles) from basic gait cycles. Subsequently, a k-means clustering technique was applied to figure out, whether a discrimination of test persons was possible based on the preprocessed motion signals. The paper shows that each test sequence could be assigned to the particular test person without additional prior information. In particular, the neutral profiles appeared to be highly consistent intra-individually (across the gait cycles as well as speed levels), but substantially different between test persons. A full discrimination of test persons was achieved using the neutral profiles with respect to flexion/extension data. Based on this, these signals can be considered as individual characteristics for the particular test persons.

Is Static Alignment a Good Predictor of Dynamic Alignment after Total Knee Arthroplasty?

Background: Total knee arthroplasty (TKA) is the only effective treatment of end-stage knee osteoarthritis (OA). Lower limb neutral alignment has been a criterion to predict prosthesis life; however, there has been recent controversy over this. Some researchers believe that lower limb static alignment does not significantly affect prosthesis life and some researchers have found that dynamic mechanical alignment may affect prosthesis life, which needs to be further studied. Methods: Eighty-seven patients with knee OA were evaluated by a three-dimensional (3D) gait analysis system before TKA and six months after TKA, dynamic mechanical alignment and basic gait parameters were then calculated. Based on the static alignment of the lower limb on the postoperative X-radiographs, they were divided into a neutral alignment group (58 cases), varus alignment group (20 cases), and valgus alignment group (9 cases). Simple linear regression was used to assess the correlation between static and dynamic alignment. One-way analysis of variance (ANOVA) was used to compare the differences in gait parameters between and within groups. Results: Eighty-seven patients were followed up for an average of six months after the operation. There was no significant difference in all gait parameters among the three groups after TKA. There was no correlation found between static alignment and dynamic alignment/knee adduction moment (KAM) after TKA, although patients showed a significant linear correlation before operation. There was a significant linear correlation between dynamic alignment and KAM before and after the operation. Conclusions: Static alignment has no significant effect on postoperative gait function. Static alignment is no longer an effective predictor of the dynamic alignment or KAM six months after TKA, although they are correlated before TKA. The dynamic alignment allows for better prediction of KAM, which may be a risk factor for the life of the prosthesis.

Classification Model for Discriminating Trunk Fatigue During Running.

Fatigue is often associated with increased injury risk. Many studies have focused on fatigue in the lower extremity muscles brought on by running, yet few have examined the relationship between fatigue of the core musculature and associated changes in running gait. To investigate the relationship between trunk fatigue and running dynamics, this study had two goals: (1) to use machine learning to determine which gait parameters are most associated with trunk fatigue; and (2) to develop a machine learning algorithm that uses those parameters to classify individuals with trunk fatigue. We hypothesized that we could effectively differentiate between the non-fatigued and fatigued states using machine learning models derived from running gait parameters. Seventy-two individuals performed a trunk fatigue protocol. Lower extremity running biomechanics were collected pre- and post- the trunk fatigue protocol using an instrumented treadmill and 10-camera motion capture system.The fatiguing protocol involved executing a series of trunk fatiguing exercises until established fatigue criteria were reached. Gait variables extracted from the non-fatigued and fatigued states served as model inputs to aid in the development of the machine learning model that would distinguish between non-fatigued and fatigued running. The machine learning protocol determined three variables - stance time, maximum propulsive GRF and maximum braking GRF - to be the best discriminators between non-fatigued and fatigued running. The SVM with Bagging was the best performing model that discriminated between non-fatigued and fatigued running with an accuracy of 82%, precision of 77%, recall of 90%, and area under the receiver operating curve of 0.91. The machine learning model was effective in classifying between non-fatigued and fatigued running using three gait parameters extracted from GRF waveforms. The ability to classify fatigue using these easy to measure GRF derived parameters enhances the ability for the model to be integrated into wearable technology and the clinical setting to aid in the detection of fatigue and potentially injury, as fatigue is often a precursor to injury.Clinical Relevance- This model has the potential to be implemented in a clinical setting to determine the onset of trunk fatigue through basic gait analysis, involving only the ground reaction forces. This model would be aimed toward injury prevention since fatigue is linked to increased risk of injury.

Hardware-in-the-Loop Test of a Prosthetic Foot

For a targeted development process of foot prostheses, a profound understanding of the dynamic interaction between humans and prostheses is necessary. In engineering, an often employed method to investigate the dynamics of mechanical systems is Hardware-in-the-Loop (HiL). This study conducted a fundamental investigation of whether HiL could be an applicable method to study the dynamics of an amputee wearing a prosthesis. For this purpose, a suitable HiL setup is presented and the first-ever HiL test of a prosthetic foot performed. In this setup, the prosthetic foot was tested on the test bench and coupled in real-time to a cosimulation of the amputee. The amputee was modeled based on the Virtual Pivot Point (VPP) model, and one stride was performed. The Center of Mass (CoM) trajectory, the Ground Reaction Forces (GRFs), and the hip torque were qualitatively analyzed. The results revealed that the basic gait characteristics of the VPP model can be replicated in the HiL test. Still, there were several limitations in the presented HiL setup, such as the limited actuator performance. The results implied that HiL may be a suitable method for testing foot prostheses. Future work will therefore investigate whether changes in the gait pattern can be observed by using different foot prostheses in the HiL test.

Use of kinematic chains piloted by inertial sensors to obtain lower-body kinematics

The increasing demand for fast and accurate gait-impaired disease diagnosis requires a real-time prediction of gait information in order to enable online information access to determining the disease progression. In addition, the wearable sensor-based information acquisition meets the new trend of take-home healthcare, the access to the great amount of data enables applying data-driven methods in this scenario. In this paper, we propose to use wearable Electromyography (EMG) and inertial measurement unit (IMU) sensors to make an ahead-of-motion prediction of basic gait information, including lower-limb kinematics and kinetics. Particularly, a novel long short term memory (LSTM)-based algorithm is trained to extract features and continuously predict lower-limb angles. Based on the predicted kinematics, the kinetics of lower limbs are calculated by a dynamic model of human segments. EMG signals recorded from nine lower limb muscles and IMU signals from each lower-limb segment were collected for training the regressor. The experimental results with cross-validation among ten subjects have demonstrated the accuracy of the angle prediction and kinetics calculation. In addition, the optimal prediction time was exploited by testing the different sets of prediction time. The implication of this research work highlights the potential of continuous prediction of kinematics and kinetics, which provides fast and accurate access to basic gait information for smart healthcare applications.

Fast and Slow Adaptations of Interlimb Coordination via Reflex and Learning During Split-Belt Treadmill Walking of a Quadruped Robot.

Interlimb coordination plays an important role in adaptive locomotion of humans and animals. This has been investigated using a split-belt treadmill, which imposes different speeds on the two sides of the body. Two types of adaptation have been identified, namely fast and slow adaptations. Fast adaptation induces asymmetric interlimb coordination soon after a change of the treadmill speed condition from same speed for both belts to different speeds. In contrast, slow adaptation slowly reduces the asymmetry after fast adaptation. It has been suggested that these adaptations are primarily achieved by the spinal reflex and cerebellar learning. However, these adaptation mechanisms remain unclear due to the complicated dynamics of locomotion. In our previous work, we developed a locomotion control system for a biped robot based on the spinal reflex and cerebellar learning. We reproduced the fast and slow adaptations observed in humans during split-belt treadmill walking of the biped robot and clarified the adaptation mechanisms from a dynamic viewpoint by focusing on the changes in the relative positions between the center of mass and foot stance induced by reflex and learning. In this study, we modified the control system for application to a quadruped robot. We demonstrate that even though the basic gait pattern of our robot is different from that of general quadrupeds (due to limitations of the robot experiment), fast and slow adaptations that are similar to those of quadrupeds appear during split-belt treadmill walking of the quadruped robot. Furthermore, we clarify these adaptation mechanisms from a dynamic viewpoint, as done in our previous work. These results will increase the understanding of how fast and slow adaptations are generated in quadrupedal locomotion on a split-belt treadmill through body dynamics and sensorimotor integration via the spinal reflex and cerebellar learning and help the development of control strategies for adaptive locomotion of quadruped robots.

An adaptive framework of real-time continuous gait phase variable estimation for lower-limb wearable robots

Phase-variable-based approaches are emerging in the control of lower-limb wearable robots, such as exoskeletons and prosthetic legs. However, real-time smooth estimation of the gait phase within each gait cycle remains an open problem. This paper presents a novel method for real-time continuous gait phase estimation during walking. The proposed framework consists of three subsystems: real-time kinematic data collection, gait phase variable estimation, and online adaptation of individual kinematics through backward data segmentation of completed gait strides. It is worth noting that we introduce an online learning mechanism for extracting and learning gait features from previous strides, in contrast with offline parameter tuning. The proposed basic gait model is initialized by human average data and is incrementally refined as a function of the individual gait features over different walking speeds. This provides a framework for long-term personalized control. Furthermore, the phase variable is constructed through the thigh angle measured by an inertial measurement unit. The resulting simple sensor system improves the usability of the proposed technique in wearable robotics. Validation experiments with seven healthy subjects, including treadmill walking and free level-ground walking, were conducted to evaluate the performance of the proposed method. In treadmill validation, the root-mean-square error (RMSE) of the phase estimator was 4.14 ± 1.68% for steady speeds, while it was 6.77 ± 2.29% for unsteady-speed walking. In level-ground validation, the average RMSE of the phase estimator was 4.59 ± 1.76%. Preliminary experiments were also conducted using a single-joint hip exoskeleton to demonstrate the usability of our method in lower-limb wearable robots.