Search Results (247)

As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. Full article

This study proposes a hybrid approach combining threshold-based algorithm and deep learning to detect four major gait events—initial contact (IC), toe-off (TO), opposite initial contact (OIC), and opposite toe-off (OTO)—using only a smartphone’s built-in inertial sensor placed in the user’s pocket. The algorithm enables estimation of spatiotemporal gait parameters such as cadence, stride length, loading response (LR), pre-swing (PSw), single limb support (SLS), double limb support (DLS), and swing phase and symmetry. Gait data were collected from 20 healthy individuals and 13 hemiparetic stroke patients. To reduce sensitivity to sensor orientation and suppress noise, sum vector magnitude (SVM) features were extracted and filtered using a second-order Butterworth low-pass filter at 3 Hz. A deep learning model was further compressed using knowledge distillation, reducing model size by 96% while preserving accuracy. The proposed method achieved error rates in event detection below 2% of the gait cycle for healthy gait and a maximum of 4.4% for patient gait in event detection, with corresponding parameter estimation errors also within 4%. These results demonstrated the feasibility of accurate and real-time gait monitoring using a smartphone. In addition, statistical analysis of gait parameters such as symmetry and DLS revealed significant differences between the normal and patient groups. While this study is not intended to provide or guide rehabilitation treatment, it offers a practical means to regularly monitor patients’ gait status and observe gait recovery trends over time. Full article

This research addresses critical challenges in clinical gait analysis by developing an automated video quality assessment framework to support Edinburgh Visual Gait Score (EVGS) scoring. The proposed methodology uses the MoveNet Lightning pose estimation model to extract body keypoints from video frames, enabling detection of multiple persons, tracking the person of interest, assessment of plane orientation, identification of overlapping individuals, detection of zoom artifacts, and evaluation of video resolution. These components are integrated into a unified quality classification system using a random forest classifier. The framework achieved high performance across key metrics, with 96% accuracy in detecting multiple persons, 95% in assessing overlaps, and 92% in identifying zoom events, culminating in an overall video quality categorization accuracy of 95%. This performance not only facilitates the automated selection of videos suitable for analysis but also provides specific video improvement suggestions when quality standards are not met. Consequently, the proposed system has the potential to streamline gait analysis workflows, reduce reliance on manual quality checks in clinical practice, and enable automated EVGS scoring by ensuring appropriate video quality as input to the gait scoring system. Full article

Smartphone-embedded inertia sensors are widely available nowadays. We have developed a smartphone application that could assess temporal gait characteristics using the built-in inertia measurement unit with the aim of enabling mass screening for gait abnormality. This study aimed to examine the test–retest reliability and concurrent validity of the smartphone-based gait assessment in assessing temporal gait parameters in level-ground walking. Twenty-six healthy young adults (mean age: 20.8 ± 0.7) were recruited. Participants walked at their comfortable pace on a 10 m pathway repetitively in two walking sessions. Gait data were simultaneously collected by the smartphone application and a VICON system during the walk. Gait events of heel strike and toes off were detected from the sensors signal by a peak detection algorithm. Further gait parameters were calculated and compared between the two systems. Pearson Product–Moment Correlation was used to evaluate the concurrent validity of both systems. Test–retest reliability was examined by the intraclass correlation coefficients (ICCs) between measurements from two sessions scheduled one to four weeks apart. The validity of smartphone-based gait assessment was moderate to excellent for parameters involving only heel strike detection (r = 0.628–0.977), poor to moderate for parameters involving detection of both heel strike and toes off (r = 0.098–0.704), and poor for the proportion of gait phases within a gait cycle. Reliability was good to fair for heel strike-related parameters (ICC = 0.845–0.388), good to moderate for heel strike and toes-off-related parameters (ICC = 0.827–0.582), and moderate to fair for proportional parameters. Validity was adversely affected when toe off was involved in the calculation, when there was an insufficient number of effective steps taken, or when calculating sub-phases with short duration. The use of smartphone-based gait assessment is recommended in calculating step time and stride time, and we suggest collecting no less than 100 steps per leg during clinical application for better validity and reliability. Full article

Subtle locomotor asymmetries are common in horses and may go unnoticed during routine pre-race clinical inspections, particularly when based solely on subjective evaluation. This study aimed to describe vertical head and pelvic movement asymmetries in racehorses that passed official pre-race inspections at a traditional racing event. Twenty-four horses were analysed using a markerless AI-based gait analysis system while trotting in-hand and during lungeing in both directions. Asymmetry parameters (HDmin, HDmax, PDmin, and PDmax) were extracted from video recordings, with values ≥0.5 considered clinically relevant. Vertical asymmetries were detected in 71% of horses during straight-line evaluation and in 79% during at least one lungeing direction. Some horses showed relevant asymmetries only under specific movement conditions, underscoring the complementary role of straight-line and lungeing assessments in comprehensive gait evaluation. These results suggest that objective gait analysis could enhance pre-race veterinary assessments, especially in traditional racing, where horses are subjected to significant biomechanical stress, including variable surface properties and repetitive directional loading. In such complex and dynamic environments, relying solely on visual assessment may result in the underdiagnosis of subtle locomotor alterations. The AI-based tools offer potential to improve the detection of subtle irregularities and support evidence-based decisions in performance horse management. Further investigations are warranted to validate the clinical relevance of currently adopted asymmetry thresholds, refine their diagnostic value, and support their integration into standardized pre-race evaluation protocols. Full article

The running cycle is distinguished from the gait cycle by the presence of a flight phase and distinct biomechanical characteristics. Despite existing frameworks for the temporal segmentation of running, these models remain underutilized in comprehensive biomechanical analyses, particularly for delineating phases, subphases, and key events. This study aims to provide a review of historical and contemporary temporal models of the running cycle and to introduce a unified structure designed to enhance analytical precision. The proposed framework divides the running cycle into two primary phases: (a) contact (subdivided into braking and propulsion subphases) and (b) flight, together with three critical events: (1) initial contact, (2) transition of braking–propulsion, (3) toe-off. While leg swing is not considered a phase in this framework due to temporal overlap with other phases, its recognized importance in running mechanics warrants its integrated analysis under the proposed temporal phase delineation. Additionally, methodologies for identifying these events through dynamometry and motion capture are evaluated, emphasizing their role in contextualizing kinetic and kinematic data. By integrating this temporal structure, the study aims to standardize biomechanical assessments of running technique, fostering more consistent comparisons across studies. Such integration has the potential to not only refine interpretations of running mechanics but also to enable practical advancements in athletic training, injury mitigation, and performance optimization. Full article

Background/Objectives: The global increase in life expectancy has led to a higher prevalence of cognitive and physical decline in older adults, particularly in those with mild cognitive impairment (MCI). This study aimed to evaluate the effects of a combined cognitive stimulation and resistance training intervention on cognitive performance, physical function, and fall risk in older adults with MCI. Methods: A randomized controlled trial was conducted with 80 community-dwelling older adults diagnosed with MCI. Participants were randomly assigned to an experimental group (EG), which received a 12-week intervention consisting of cognitive stimulation and progressive strength training, or a control group (CG), which maintained their usual routine. Pre- and post-intervention assessments included measures of cognitive function, verbal fluency, attention, processing speed, executive function, gait, balance, fall risk, and lower- and upper-body strength. Results: The EG showed significant improvements compared with the CG in cognitive impairment, verbal fluency, processing speed, balance, gait, and risk of falls (all p < 0.05), with effect sizes ranging from moderate to large. Notably, strength gains were observed in both lower body and grip strength. Attention and executive function also improved in the EG, although with smaller effect sizes. No adverse events were reported. Conclusions: A combined intervention of cognitive stimulation and resistance training is effective in improving multiple domains of cognitive and physical function in older adults with MCI. These findings support the integration of multidomain interventions in clinical and community settings to promote autonomy, reduce fall risk, and delay cognitive and functional decline. Future studies should explore the long-term sustainability of these effects and the individual contribution of each intervention component. Full article

Complex walking tasks such as turning or walking with head movements are frequently used to assess dysfunction in an individual’s vestibular, nervous and musculoskeletal systems. Compared to other methods, wearable inertial measurement units (IMUs) allow quantitative analysis of these tasks in less restricted settings, allowing for a more scalable clinical measurement tool with better ecological validity. This study investigates the use of ear-worn IMUs to identify gait events during complex walking tasks, having collected data on 68 participants with a diverse range of ages and movement-related conditions. The performance of an existing gait event detection algorithm was compared with a new one designed to be more robust to lateral head movements. Our analysis suggests that while both algorithms achieve high initial contact sensitivity across all walking tasks, our new algorithm attains higher terminal contact sensitivity for turning and walking with horizontal head turns, resulting in more accurate estimates of stance and swing times. This provides scope to enable more detailed assessment of complex walking tasks during clinical testing and in daily life settings. Full article

Background: To evaluate the efficacy and safety of outpatient exercise training in clinically stabilized patients with chronic thromboembolic pulmonary hypertension (CTEPH) after balloon pulmonary angioplasty (BPA). Methods: Twenty-four patients with CTEPH after BPA were enrolled in this prospective single-center study. Patients were assigned to the exercise and control groups. The exercise group comprised 12 patients who received 15 weeks of exercise training, with usual care. The control group received only the usual care, without exercise training. The exercise program included aerobic exercise thrice weekly and resistance exercise once or twice weekly. The assessments employed included a 6-min walk test, cardiopulmonary exercise testing, and an emPHasis-10 questionnaire. Results: In the exercise group, the 6-min walk distance was significantly longer (510.0 [467.5, 595.0] m vs. 425.0 [395.0, 465.0] m, p = 0.020), the time taken to walk 10 m was shorter (6.4 [5.9, 7.5] s vs. 8.9 [8.1, 9.1] s, p = 0.020), and the walking speed was faster (1.6 [1.3, 1.7] m/s vs. 1.1 [1.1, 1.2] m/s, p = 0.020) at 15 weeks compared with the results for the control group. The quality of life tended to improve at 15 weeks compared with that before the exercise training. However, hemodynamics did not change significantly before and after the exercise training, and no fatal arrhythmias or syncope were observed. Conclusions: Exercise training improved gait performance, without any adverse events, in patients with CTEPH after BPA. Therefore, exercise training as an adjunct to medical therapy may be a safe potential therapy for patients with CTEPH after BPA. Full article

As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics. Full article

Wearable sensors are used in gait analysis to obtain spatiotemporal parameters, with gait events serving as critical markers for foot and lower limb movement. Summarizing detection methods is essential, as accurately identifying gait events and phases are key to deriving precise spatiotemporal parameters through wearable technology. However, a clear understanding of how these sensors, particularly angular velocity and acceleration signals within inertial measurement units, individually or collectively, contribute to the detection of gait events and gait phases is lacking. This review aims to summarize the current state of knowledge on the application for both gyroscopes, with particular emphasis on the role of angular velocity signals, and inertial measurement units with both angular velocity and acceleration signals in identifying gait events, gait phases, and calculating gait spatiotemporal parameters. Gyroscopes remain the primary tool for gait events detection, while inertia measurement units enhance reliability and enable spatiotemporal parameter estimation. Rule-based methods are suitable for controlled environments, whereas machine learning offers flexibility to analyze complex gait conditions. In addition, there is a lack of consensus on optimal sensor configurations for clinical applications. Future research should focus on standardizing sensor configurations and developing robust, adaptable detection methodologies suitable for different gait conditions. Full article

Bacillus Calmette–Guerin (BCG) central nervous system (CNS) infections are one of the rarest complications following BCG exposure. A 77-year-old male, with bladder cancer previously treated with BCG instillation, presented with fever, confusion, and brain magnetic resonance imaging (MRI) consistent with encephalitis one month after the last BCG instillation. Cerebrospinal fluid (CSF) showed marked hypoglycorrhachia, hyperproteinorrachia, and lymphocytic pleocytosis. Despite CSF culture negativity, the presentation was considered suggestive of BCG-related encephalitis, and the empirical standard antitubercular treatment (rifampin, isoniazid and ethambutol), plus dexamethasone, was initiated. Following initial improvement, gait ataxia and hemiplegia were observed at the 4-month follow-up. MRI revealed an excluded enlarged left lateral ventricle with signs of ventriculitis, requiring surgical drainage. CSF collected during neurosurgery resulted positive on PCR for M. tuberculosis complex. Adjunctive linezolid was initiated, replaced by levofloxacin due to adverse events after 2 weeks. The patient was discharged following a normal CSF analysis. Oral antitubercular therapy was prescribed for 14 months and there were no signs of relapse at the 24-month follow-up. Previously, 16 cases of CNS BCGitis have been reported, without any cases of clinical relapse during antitubercular treatment. Furthermore, our study reports the use of linezolid as a 4th antitubercular drug for CNS BCGitis. Full article

Spinal deformities, particularly thoracolumbar kyphosis, affect approximately one-third of patients with Parkinson’s disease (PD) and significantly impair their quality of life and mobility. Conventional treatments, including levodopa and surgical interventions, have limited efficacy, necessitating alternative therapies. In this report, a 76-year-old woman with PD and severe thoracolumbar kyphosis (TK: 77.7°; sagittal vertical axis [SVA]: 95.55 mm) experienced postural instability and gait impairment. She underwent integrative postural rehabilitation (acupuncture, pharmacopuncture, Chuna spinal manual therapy, thermotherapy, and bodyweight exercises). A 4-week inpatient treatment improved spinal alignment (TK: 61.1°; SVA: 77.84 mm), gait, postural stability (MDS-UPDRS score improved by 3 points), and functional outcomes, with reductions in the Oswestry Disability Index (70 to 31) and pain severity (Numeric Rating Scale: 50 to 40). No adverse events were observed. Integrative postural rehabilitation can mitigate paraspinal muscle atrophy and fatty infiltration by promoting protein synthesis, neurotrophic factor expression, and proprioceptive neuromodulation. Our literature review suggests that proprioceptive stimulation and exercise enhances postural stability and gait, aligning with the outcomes of this case. This report suggests that integrative rehabilitation may improve kyphotic deformities and related motor dysfunctions in patients with PD. Further research is warranted to validate the treatment’s efficacy and long-term benefits. Full article

Remote monitoring technologies have transformed healthcare delivery by enabling the in-home management of chronic conditions, improving patient autonomy, and supporting clinical oversight. Passive sensing, a subset of remote monitoring, facilitates unobtrusive, real-time data collection without active user engagement. Leveraging devices such as smartphones, wearables, and smart home sensors, these technologies offer advantages over traditional self-reports and intermittent evaluations by capturing behavioural, physiological, and environmental metrics. This systematic review evaluates the clinical utility of passive sensing technologies used in remote monitoring, with a specific emphasis on their impact on clinical outcomes and feasibility in real-world healthcare settings. A PRISMA-guided search identified 26 studies addressing conditions such as Parkinson’s disease, dementia, cancer, cardiopulmonary disorders, and musculoskeletal issues. Findings demonstrated significant correlations between sensor-derived metrics and clinical assessments, validating their potential as digital biomarkers. These technologies demonstrated feasibility and ecological validity in capturing continuous, real-world health data and offer a unified framework for enhancing patient care through three main applications: monitoring chronic disease progression, detecting acute health deterioration, and supporting therapeutic interventions. For example, these technologies successfully identified gait speed changes in Parkinson’s disease, tracked symptom fluctuations in cancer patients, and provided real-time alerts for acute events such as heart failure decompensation. Challenges included long-term adherence, scalability, data integration, security, and ownership. Future research should prioritise validation across diverse settings, long-term impact assessment, and integration into clinical workflows to maximise their utility. Full article

The Edinburgh Visual Gait Score (EVGS) is a commonly used clinical scale for assessing gait abnormalities, providing insight into diagnosis and treatment planning. However, its manual implementation is resource-intensive and requires time, expertise, and a controlled environment for video recording and analysis. To address these issues, an automated approach for scoring the EVGS was developed. Unlike past methods dependent on controlled environments or simulated videos, the proposed approach integrates pose estimation with new algorithms to handle operational challenges present in the dataset, such as minor camera movement during sagittal recordings, slight zoom variations in coronal views, and partial visibility (e.g., missing head) in some videos. The system uses OpenPose for pose estimation and new algorithms for automatic gait event detection, stride segmentation, and computation of the 17 EVGS parameters across the sagittal and coronal planes. Evaluation of gait videos of patients with cerebral palsy showed high accuracy for parameters such as hip and knee flexion but a need for improvement in pelvic rotation and hindfoot alignment scoring. This automated EVGS approach can minimize the workload for clinicians through the introduction of automated, rapid gait analysis and enable mobile-based applications for clinical decision-making. Full article