Search Results (302)

Background: Gait kinematics address the analysis of joint angles and segment movements during walking. Although there is work in the literature to solve the problems of forward (FK) and inverse kinematics (IK), there are still problems related to the accuracy of the estimation of Cartesian and joint variables, singularities, and modeling complexity on gait analysis approaches. Objective: In this work, we propose a framework for two-dimensional gait analysis addressing the singularities in the estimation of the joint variables using quaternion-based kinematic modeling. Methods: To solve the forward and inverse kinematics problems we use the dual quaternions’ composition and Damped Least Square (DLS) Jacobian method, respectively. We assess the performance of the proposed methods with three gait patterns including normal, toe-walking, and heel-walking using the RMSE value in both Cartesian and joint spaces. Results: The main results demonstrate that the forward and inverse kinematics methods are capable of calculating the posture and the joint angles of the three-DoF kinematic chain representing a lower limb. Conclusions: This framework could be extended for modeling the full or partial human body as a kinematic chain with more degrees of freedom and multiple end-effectors. Finally, these methods are useful for both diagnostic disease and performance evaluation in clinical gait analysis environments. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was $3.6°$ and $3.3°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance were $2.3°$ and $2.8°$, respectively. In Case 2, the mean error (MEAN) of the tracking performance is $2.7°$ and $3.1°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance are $1.8°$ and $2.4°$, respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are $2.4°$, $2.9°$, $3.4°$, and $2.2°$, $2.8°$, $3.1°$, respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were $1.6°$, $2.3°$, $2.6°$, and $1.3°$, $1.7°$, $2.2°$, respectively. Full article

Gait evaluation approaches using small, lightweight inertial sensors have recently been developed, offering improvements in terms of both portability and usability. However, accelerometer outputs include both the acceleration that is generated by human motion and gravitational acceleration, which changes along with the posture of the body part to which the sensor is attached. This study presents a gait analysis method that uses the gravitational, centrifugal, tangential, and translational accelerations obtained from sensors attached to the lower legs. In this method, each sensor pose is sequentially estimated using sensor fusion to combine data obtained from a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The estimated sensor pose is then used to calculate the gravitational acceleration that is included in each axis of the sensor coordinate system. The centrifugal and tangential accelerations are determined from the gyroscope output. The translational acceleration is then obtained by subtracting the centrifugal, tangential, and gravitational accelerations from the accelerometer output. As a result, the acceleration components contained in the outputs of the accelerometers attached to the lower legs are provided. As only the acceleration components caused by walking motion are captured, thus reflecting their characteristics, it is expected that the developed method can be used for gait evaluation. Full article

Gait analysis is a valuable tool for aiding in the diagnosis of neurological diseases, providing objective measurements of human gait kinematics and kinetics. These data enable the quantitative estimation of movement abnormalities, which helps to diagnose disorders and assess their severity. In this regard, machine learning techniques and explainability methods offer an opportunity to enhance anomaly detection in gait measurements and support a more objective assessment of neurodegenerative disease, providing insights into the most relevant gait parameters used for disease identification. This study employs several classifiers and explainability methods to analyze gait data from a public dataset composed of patients affected by degenerative neurological diseases and healthy controls. The work investigates the relevance of spatial, temporal, and kinematic gait parameters in distinguishing such diseases. The findings are consistent among the classifiers employed and in agreement with known clinical findings about the major gait impairments for each disease. This work promotes the use of data-driven assessments in clinical settings, helping reduce subjectivity in gait evaluation and enabling broader deployment in healthcare environments. Full article

Background: ACL graft reconstruction is considered the gold standard for ACL injury treatment. Recently developed primary repair techniques such as InternalBrace ligament augmentation (Arthrex©) look like promising alternatives. The aim of our study is to compare functional results of two different surgical techniques using a gait analysis. Methods: A total of 42 patients who underwent surgical treatment for ACL rupture were included in this study. The first group was represented by patients who were surgically treated with ACL reconstruction. The second group included patients with acute ACL injury, who underwent primary repair with InternalBrace augmentation. Gait data were measured in the Human Motion Analysis Lab at our institution. The time interval for data collection was 6 weeks after surgery and 6 months after surgery. Results: There was no significant improvement in maximal and peak flexion for both group 1 and group 2 in the 6-week and 6-month intervals. Also, no significant improvement of maximal extension was found in group 1. In contrast, the study showed a reduction in maximal extension for group 2 in the 6-week and 6-month intervals. When comparing peak extension for the graft or InternalBrace techniques, no significant difference was found between both groups in the 6-week evaluation. However, results differed significantly in the 6-month evaluation. Conclusions: Considering the faster gain of extension, less invasiveness of the procedure, and shorter operating time, primary repair with InternalBrace augmentation seems to be a suitable option for treatment of proximal avulsions and Sherman I ACL ruptures. Full article

Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

This study aimed to evaluate how wearable hip exoskeleton assistance affects phase-dependent gait stability in healthy adults using a novel visualization technique known as gait tube analysis. Hip exoskeletons offer significant potential to enhance human locomotion through joint torque augmentation, yet their effects on gait stability across the gait cycle remain underexplored. This study introduces gait tube analysis, a novel method for visualizing center of mass velocity trajectories in three-dimensional state space, to quantify phase-dependent gait stability under hip exoskeleton assistance. We analyzed data from ten healthy adults walking under twelve conditions (ten powered with varying torque magnitude and timing, one passive, and one unassisted), assessing variability via covariance-based ellipsoid volumes. Powered conditions, notably HighLater and HighLatest, significantly increased vertical variability (VT) during early-to-mid stance (10–50% of the gait cycle), with HighLater showing the highest mean ellipsoid volume (99,937 mm³/s³; z = 2.3). Conversely, the passive PowerOff condition exhibited the lowest variability (47,285 mm³/s³; z = –1.7) but higher metabolic cost, highlighting a stability-efficiency trade-off. VT was elevated in 11 of 12 conditions (p ≤ 0.0059), and strong correlations (r ≥ 0.65) between ellipsoid volume and total variability validated the method’s robustness. These findings reveal phase-specific stability challenges and metabolic cost variations induced by exoskeleton assistance, providing a foundation for designing adaptive controllers to balance stability and efficiency in rehabilitation and performance enhancement contexts. Full article

Gait analysis provides crucial insights into neuromuscular coordination and postural control, especially in ageing populations and rehabilitation contexts. This study investigates the complexity of muscle activation and ground reaction force patterns during gait by applying detrended fluctuation analysis (DFA) to electromyography (EMG) and force-sensitive resistor (FSR) signals. Data from a two-arm randomised clinical trial (RCT) supplemented with an observational control group were used in this study. Participants performed a single-task walking protocol, with EMG recorded from the tibialis anterior and lateral gastrocnemius muscles of both legs and FSR sensors placed under the feet. Gait cycles were segmented using heel-strike detection from the FSR signal, enabling analysis of individual strides. For each gait cycle, DFA was applied to quantify the long-range temporal correlations in the EMG and FSR time series. Results revealed consistent $\alpha$-scaling exponents across cycles, with EMG signals exhibiting moderate persistence ($\alpha \approx 0.85$–$0.92$) and FSR signals showing higher persistence ($\alpha \approx 1.5$), which is indicative of stable and repeatable gait patterns. These findings support the utility of DFA as a nonlinear signal processing tool for characterising gait dynamics, offering potential markers for gait stability, motor control, and intervention effects in populations practising movement-based therapies such as Tai Chi. Future work will extend this analysis to dual-task conditions and comparative group studies. Full article

Advancements in remote gait analysis technologies enable efficient, cost-effective, and personalized real-time assessments at home. This study aims to contribute evidence as to the comparability of gait quality metrics of three methods of remote gait assessment in individuals with Parkinson’s disease (PD): (1) observation, (2) a wearable sensor, and (3) pose estimation. A cross-sectional, multiple case series study was conducted remotely. Twenty participants submitted videos performing a modified TUG test with the Heel2Toe^TM wearable. Each video was analysed by six raters using the checklist specific to PD developed for this study and the MediaPipe Pose Landmarker task estimation library. The observational ratings agreed with the Heel2Toe^TM on detecting heel strike 64% of the time and 28.5% of the time on detecting push-off. The difference in the ranks of paired observations based on the Wilcoxon signed rank sum test between the pairs of methods compared was significant for all parameters, except for push-off when estimates from MediaPipe were compared to the ratings from the Observational Checklist, W = 86 (p = 0.498). A combination of digital technologies for remote gait analysis, such as wearable sensors and pose estimation, can detect subtle nuances in gait impairments that may be overlooked by the human eye. Full article

Forestry is recognized as one of the most physically demanding professions. Walking in presents unique biomechanical challenges due to complex, irregular terrain, with several possible risks. This study investigated how human gait adapts across solid surfaces, forest trails, and natural forest environments. Fifteen healthy adult participants (average age 38.3; ten males and five females) completed 150 walking trials, with full-body motion captured via a 17 Inertial Measurement Unit (IMU) sensors (Xsens MVN Awinda system). The analysis focused on spatial and temporal gait parameters, including cadence, step length, foot strike pattern, and center of mass variability. Statistical methods (ANOVA and Kruskal–Wallis) revealed that surface type significantly influenced gait mechanics. On forest terrain, participants exhibited wider steps, reduced cadence, increased step and stride variability, and a substantial shift from heel-to-toe strikes. Gait adaptations reflect compensatory neuromuscular strategies to maintain body balance. The findings confirm that forestry terrain complexity compromises human gait stability and increases physical demands, supporting step variability and slip, trip, and fall risk. By identifying key biomechanical markers of instability, this study contributes to understanding human locomotion principles. Understanding these changes can help design safety measures for outdoor professions, particularly forestry. Full article

This study aimed to investigate the influence of different walking speeds on shoulder and elbow joint kinematics, specifically focusing on range of motion, angular velocity, and angular acceleration during arm swing. The natural rhythm of human gait was studied to develop an effective mechanical interface, particularly with respect to joint impedance and force controllability. The independent variable in this study was walking speed, operationalized at four levels—3.6 km/h (slow), 4.2 km/h (preferred walking speed, PWS), 5.4 km/h (normal), and 7.2 km/h (fast)—and defined as a within-subject factor. The dependent variables consisted of quantitative kinematic parameters, including joint range of motion (ROM, in degrees), peak and minimum joint angular velocity (deg/s), and peak and minimum joint angular acceleration (deg/s²). For each subject, data from twenty gait cycles were extracted for analysis. The kinematic variables of the shoulder and elbow were analyzed, showing increasing trends as the walking speed increased. As walking speed increases, adequate arm swing contributes to gait stability and energy efficiency. Notably, the ROM of shoulder was slightly reduced at the PWS compared to the slowest speed (3.6 km/h), which may reflect more natural and coordinated limb movements at the PWS. Dynamic covariation of torque patterns in the shoulder and elbow joints was observed, reflecting a synergistic coordination between these joints in response to human body movement. Full article

Gait refers to the walking pattern of an individual and it varies from person to person. Consequently, it can be considered to be a biometric feature, similar to the face, iris, or fingerprints, and can easily be used for human identification purposes. Person identification using gait analysis has direct applications in user authentication, visual surveillance and monitoring, and access control—to name a few. Naturally, gait analysis has attracted many researchers both from academia and industry over the past few decades. Within a small population, the accuracy of person identification could be very high; however, with the growing number of people in a given gait database, identifying a person only from gait becomes a daunting task. Hence, the focus of researchers in this field has exhibited a paradigm shift to a broader problem of sex and age prediction using different biometric parameters—with gait analysis obviously being one of them. Recent works on sex and age prediction using gait pattern obtained from the inertial sensors lacks an analysis of the features being used. In this paper, we propose a number of features inherent to gait data and analyze key features from the time–series data of accelerometer and gyroscopes for the automatic recognition of sex and the prediction of age. We have trained various traditional machine learning models and achieved the highest accuracy of 94% in sex prediction and an $R^2$ score of 0.83 in age estimation. Full article

Human gait motions differ depending on the age of the person. Previous studies have estimated age categories of walkers or have used age analysis for security or commercial surveillance purposes using images. However, few studies have estimated age from gait parameters alone. We estimated the age of people using kernel regression analysis based on their height, weight, and representative gait parameters, i.e., walking features that are interpretable with relative ease. Samples were obtained from 75 Japanese women aged 20–70 in a database. Through a variable selection based on sensitivity analysis, the established model estimated the ages of the women with a correlation coefficient of 0.78 with their actual ages, and the mean absolute error was 9.99 years. The sensitive variables included the minimum foot clearance, body weight, walking velocity, step width, and stride length. Estimation errors were significantly greater for elderly adults than for young people. Specifically, the mean absolute error for people in their 20s was $7.4$ years, whereas that for those over 60 was $13.1$ years. The proposed method uses gait parameters that can be measured with wearable devices, such as inertial measurement units; therefore, it offers an accessible approach to estimating a walker’s age with moderate certainty and promoting healthcare awareness in daily life. Full article