Search Results (477)

Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. Full article

In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson’s disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington’s Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel. Full article

This review paper investigates the current state of research on structure-to-human interaction (S2HI) in the monitoring and control of cyclo-pedestrian footbridges, focusing specifically on the biodynamic effects of oscillations on pedestrians. Its aim is, therefore, twofold: In the first half, it examines the limited but evolving understanding of human gait responses to vertical and horizontal vibrations at frequencies and amplitudes characteristic of footbridge dynamics. The second half includes a detailed analysis of various modelling strategies for simulating pedestrian and crowd dynamics, emphasising the movements and stationary behaviours induced by structural vibrations. The aim is to highlight the strengths and limitations of these modelling approaches, particularly their capability to incorporate biomechanical factors in pedestrian responses. The research findings indicate that existing studies predominantly focus on human-to-structure interaction (HSI), often neglecting the reciprocal effects of S2HI, with many results in the literature failing to adequately address the biomechanics of single pedestrians or crowds experiencing structural vibrations on cyclo-pedestrian bridges. This gap underscores the need for more precise and comprehensive studies in the field to improve the understanding of dynamic interactions between single or multiple walking individuals and footbridge vibrations, especially for vulnerable and elderly people with limited mobility. Furthermore, considerations regarding the impact of Structural Control and Health Monitoring to alleviate these issues are briefly discussed, highlighting the potential to optimise footbridge performance in terms of pedestrian comfort. Full article

Background/Objectives: Ventriculoperitoneal shunting is a validated procedure for the treatment of idiopathic normal-pressure hydrocephalus. To select shunt-responsive patients, infusion and tap tests can be used. Only gait is evaluated after the procedure to establish a potential improvement. In this study, we present our Hydro-Real-Time Neuropsychological Testing protocol to assess the feasibility of performing an ultra-fast assessment of patients during the infusion and tap test. Methods: We tested 57 patients during the infusion and tap test to obtain real-time feedback on their cognitive status. Data were obtained immediately before the infusion phase (T0), when the pressure plateau was reached (T1), and immediately after cerebrospinal fluid subtraction (T2). Based on cerebrospinal fluid dynamics, 63.15% of the patients presented a resistance to outflow > 12 mmHg/mL/min, while 88% had a positive tap test response. Results: Compared to T0, cerebrospinal fluid removal significantly improved performance on tasks exploring executive functions (counting backward, p < 0.001; verbal fluency, p < 0.001). Patients were significantly faster at counting backward at T2 vs. T1 (p < 0.05) and at T2 vs. T0 (p < 0.001) and were significantly faster at counting forward at T2 vs. T1 (p < 0.005), suggesting an improvement in speed at T2. There was a significantly smaller index at T1 vs. T0 (p = 0.005) and at T2 vs. T0 (p < 0.001), suggesting a more marked improvement in patients’ executive abilities at T2 and a smaller improvement at T1. Regarding verbal fluency, patients were worse at T1 vs. T0 (p < 0.001) and at T2 vs. T0 (p < 0.001). Conclusions: Patients’ performance can be monitored during the infusion and tap test as significant changes in executive functions are observable. In future, this protocol might help improve patients’ selection for surgery. Full article

Adhesion-based space climbing robots, with their flexibility and multi-functional capabilities, are seen as a promising candidate for in-orbit maintenance. However, challenges such as uncertain adhesion establishment, unexpected detachment, and body motion unsteadiness in microgravity environments persist. To address these issues, this paper proposes a coordinated force–position compliance control method that integrates novel adhesion establishment and rotational detachment strategies, integrated into the gait schedule for a space climbing robot. By monitoring the foot-end reaction forces in real time, the proposed method establishes adhesion without risking damaging the spacecraft exterior, and smooth detachment is achieved by rotating the foot joint instead of direct pulling. These strategies are dedicated to reducing unnecessary control actions and, accordingly, the required adhesion forces in all feet, reducing the possibility of unexpected detachment. Climbing experiments have been conducted in a suspension-based gravity compensation system to examine the merits of the proposed method. The experimental results demonstrate that the proposed rotational detaching method decreases the required pulling force by 65.5% compared to direct pulling, thus greatly reducing the disturbance introduced to the robot body and other supporting legs. When stepping on an obstacle, the compliant control method is shown to reduce unnecessarily aggressive control actions and result in a reduction in relevant normal and shear adhesion forces in the supporting legs by 44.8% and 35.1%, respectively, compared to a PID controller. Full article

Modern treat-to-target management of rheumatoid arthritis (RA) involves titration of drug therapy to achieve remission, requiring close monitoring of disease activity through frequent clinical assessments. Accelerometry offers a novel method for continuous remote monitoring of RA activity by capturing fluctuations in mobility, sedentary behaviours, physical activity and sleep patterns over prolonged periods without the expense, inconvenience and environmental impact of extra hospital visits. We aimed to (a) assess the feasibility, usability and acceptability of wearable devices in patients with active RA; (b) investigate the multivariate relationships within the dataset; and (c) explore the robustness of accelerometry outcomes to downsampling to facilitate future prolonged monitoring. Eleven people with active RA newly starting an arthritis drug completed clinical assessments at 4-week intervals for 12 weeks. Participants wore an Axivity AX6 wrist device (sampling frequency 100 Hz) for 7 days after each clinical assessment. Measures of macro gait (volume, pattern and variability), micro gait (pace, rhythm, variability, asymmetry and postural control of walking), sedentary behaviour (standing, sitting and lying) and physical activity (moderate to vigorous physical activity [MVPA], sustained inactive bouts [SIBs]) and sleep outcomes (sleep duration, wake up after sleep onset, number of awakenings) were recorded. Feasibility, usability and acceptability of wearable devices were assessed using Rabinovich’s questionnaire, principal component (PC) analysis was used to investigate the multivariate relationships within the dataset, and Bland–Altman plots (bias and Limits of Agreement) and Intraclass Correlation Coefficient (ICC) were used to test the robustness of outcomes sampled at 100 Hz versus downsampled at 50 Hz and 25 Hz. Wearable devices obtained high feasibility, usability and acceptability scores among participants. Macro gait outcomes and MVPA (first PC) and micro gait outcomes and number of SIBs (second PC) exhibited the strongest loadings, with these first two PCs accounting for 40% of the variance of the dataset. Furthermore, these device metrics were robust to downsampling, showing good to excellent agreements (ICC ≥ 0.75). We identified two main domains of mobility, physical activity and sleep outcomes of people with RA: micro gait outcomes plus MVPA and micro gait outcomes plus number of SIBs. Combined with the high usability and acceptability of wearable devices and the robustness of outcomes to downsampling, our real-world data supports the feasibility of accelerometry for prolonged remote monitoring of RA disease activity. Full article

Background: There has been a growing interest in using inertial sensors to explore the temporal aspects of the Timed Up and Go (TUG) test. The current study aimed to analyze the spatiotemporal parameters and phases of the TUG test in patients with knee osteoarthritis (KOA) and compare the results with those of non-arthritic individuals. Methods: This study included 20 patients with KOA and 60 non-arthritic individuals aged 65 to 84 years. All participants performed the TUG test, and 17 spatiotemporal parameters and phase data were collected wirelessly using the BTS G-Walk inertial sensor. Results: Significant mobility impairments were observed in KOA patients, including slower gait speed, impaired sit-to-stand transitions, and reduced turning efficiency. These findings highlight functional deficits in individuals with KOA compared to their non-arthritic counterparts. Conclusions: The results emphasize the need for targeted physiotherapy interventions, such as quadriceps strengthening, balance training, and gait retraining, to address these deficits. However, the study is limited by its small sample size, gender imbalance, and limited validation of the BTS G-Walk device. Future research should include larger, more balanced cohorts, validate sensor reliability, and conduct longitudinal studies. Despite these limitations, the findings align with previous research and underscore the potential of inertial sensors in tailoring rehabilitation strategies and monitoring progress in KOA patients. Full article

This study introduces BagStacking, an innovative ensemble learning framework designed to enhance the detection of freezing of gait (FOG) in Parkinson’s disease (PD) using accelerometer data. By synergistically combining bagging’s variance reduction with stacking’s sophisticated blending mechanisms, BagStacking achieves superior predictive performance. Evaluated on a comprehensive PD dataset provided by the Michael J. Fox Foundation, BagStacking attained a mean average precision (MAP) of 0.306, surpassing standalone LightGBM and traditional stacking methods. Furthermore, BagStacking demonstrated superior area under the curve (AUC) metrics across key FOG event classes. Specifically, it achieved AUCs of 0.88 for start hesitation, 0.90 for turning, and 0.84 for walking events, outperforming multistrategy ensemble, regular stacking, and LightGBM baselines. Additionally, BagStacking exhibited reduced runtime compared to other ensemble approaches, making it suitable for real-time clinical monitoring. These results underscore BagStacking’s effectiveness in addressing the variability inherent in FOG detection, thereby contributing to improved patient care in PD. Full article

Inappropriate, excessive, or overly strenuous training of sport horses can result in long-term injury, including the premature cessation of a horse’s sporting career. As a countermeasure, this study demonstrates the easy implementation of a biomechanical load monitoring system consisting of five commercial, multi-purpose inertial sensor units non-invasively attached to the horse’s distal limbs and trunk. From the data obtained, specific parameters for evaluating gait and limb loads are derived, providing the basis for objective exercise load management and successful injury prevention. Applied under routine in-the-field training conditions, our pilot study results show that tri-axial peak impact limb load increases progressively from walk to trot to canter, in analogy to stride frequency. While stance and swing phases shorten systematically with increasing riding speed across subjects, longitudinal and lateral load asymmetry are affected by gait at an individual level, revealing considerable variability between and within individual horses. This individualized, everyday approach facilitates gaining valuable insights into specific training effects and responses to changing environmental factors in competitive sport horses. It promises to be of great value in optimizing exercise management in equestrian sports to benefit animal welfare and long-term health in the future. Full article

Sarcopenia has been a serious concern in the context of an increasingly aging global population. Existing detection methods for sarcopenia are severely constrained by cumbersome devices, the necessity for specialized personnel, and controlled experimental environments. In this study, we developed an innovative wearable fabric system based on conductive fabric and flexible sensor array. This fabric system demonstrates remarkable pressure-sensing capabilities, with a high sensitivity of 18.8 kPa⁻¹ and extraordinary stability. It also exhibits excellent flexibility for wearable applications. By interacting with different parts of the human body, it facilitates the monitoring of various physiological activities, such as pulse dynamics, finger movements, speaking, and ambulation. Moreover, this fabric system can be seamlessly integrated into sole to track critical indicators of sarcopenia patients, such as walking speed and gait. Clinical evaluations have shown that this fabric system can effectively detect variations in indicators relevant to sarcopenia patients, proving that it offers a straightforward and promising approach for the diagnosis and assessment of sarcopenia. Full article

The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) into smart shoes has proven effective for capturing detailed foot movements and spatiotemporal gait characteristics. While IMUs enable accurate foot trajectory estimation through the double integration of acceleration data, challenges such as drift errors necessitate robust correction techniques to ensure reliable performance. This review analyzes current literature on shoe-based systems utilizing IMUs to estimate spatiotemporal gait parameters and foot trajectory characteristics, including foot–ground clearance. We explore the challenges and advancements in achieving accurate 3D foot trajectory estimation using IMUs in smart shoes and the application of advanced techniques like zero-velocity updates and error correction methods. These developments present significant opportunities for achieving reliable and efficient real-time gait assessment in everyday environments. Full article

Purposeful heading, in which players may use their heads to advance the ball in play, is a unique part of soccer. Clinical outcome measures used to aid in the diagnosis of a concussion have long been a cornerstone of the contemporary measurements associated with the short- and long-term effects of monitoring repetitive head impacts (RHI) and soccer heading exposure. The effects of RHI in the youth population are still unknown, therefore, the purpose of this study was to examine if heading exposure is predictive of changes in self-reported symptoms, neurocognitive functioning, gait, and balance in female youth soccer players over the course of one soccer season. Small improvements in neurocognitive functioning and gait and slight deficits in balance were observed from pre- to post-season. All changes were not clinically relevant and likely due to a practice effect. The low heading exposure in our cohort of youth soccer players was likely not enough to elicit any changes in clinical measures. In general, our clinical outcomes did not change after a season of soccer play and change scores were not predicted by heading exposure. Full article

The evaluation of gait biomechanics using portable inertial measurement units (IMUs) offers real-time feedback and has become a crucial tool for detecting gait disorders. However, many of these devices have not yet been fully validated. The aim of this study was to assess the concurrent validity and relative reliability of the RunScribe™ system for measuring spatiotemporal gait parameters during walking. A total of 460 participants (age: 36 ± 13 years; height: 173 ± 9 cm; body mass: 70 ± 13 kg) were asked to walk on a treadmill at 5 km·h⁻¹. Spatiotemporal parameters of step frequency (SF), step length (SL), step time (ST), contact time (CT), swing time (SwT), stride time (StT), stride length (StL) and normalized stride length (StL%) were measured through RunScribe™ and OptoGait™ systems. Bland–Altman analysis indicated small systematic biases and random errors for all variables. Pearson correlation analysis showed strong correlations (0.70–0.94) between systems. The intraclass correlation coefficient supports these results, except for contact time (ICC = 0.64) and swing time (ICC = 0.34). The paired t-test showed small differences in SL, StL and StL% (≤0.25) and large in CT and SwT (1.2 and 2.2, respectively), with no differences for the rest of the variables. This study confirms the accuracy of the RunScribe™ system for assessing spatiotemporal parameters during walking, potentially reducing the barriers to continuous gait monitoring and early detection of gait issues. Full article

Smartphone-based accelerometers offer a cost-effective and portable alternative to traditional gait analysis systems, with high reliability in measuring key parameters such as walking speed, cadence, and distance. This study assessed their validity compared to the GAITRite system, a widely recognized gold-standard tool, using data from 30 healthy adults walking at 3 self-selected speeds: preferred, slow, and fast. The results demonstrated a high degree of agreement between the two systems, with intraclass correlation coefficients (ICCs) ranging from 0.778 to 0.999. Although the findings emphasize the potential of smartphone accelerometers for clinical and real-world applications, certain limitations were noted, including participant homogeneity and minor discrepancies at extreme walking speeds. To address these limitations, incorporating data from additional sensors, such as gyroscopes and magnetometers, may enhance the accuracy and reliability of spatial parameter estimation. Overall, the findings support the use of smartphone accelerometers as a promising tool for advancing gait monitoring technologies, particularly in the contexts of telerehabilitation and mobility assessments. Full article

Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial to assess the measurement accuracy of these solutions since residual errors may arise from environmental, methodological, or processing factors. This study aims to enhance validation by employing machine learning techniques to investigate the impact of such errors on the overall assessment of gait profiles. Two datasets of gait trials, collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system, were considered. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathologic gait peculiarities. Machine learning models show the equivalence and the high level of agreement and concordance between the measurement systems in assessing gait profiles (accuracy: 98.7%). In addition, they demonstrate data interchangeability and integrability despite residual errors identified by traditional statistical metrics. These findings suggest that validation procedures can extend beyond strict measurement differences to comprehensively assess gait performance. Full article