Search Results (327)

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in general, full-body motion estimation requires motion capture systems or multiple inertial sensors, making it necessary to directly measure movement itself. In this study, we propose estimating full-body posture using inverse kinematics based on trunk posture and limb-end information collected through wearable devices. To enhance estimation accuracy in this underdetermined problem, we employ Physics-Informed Neural Networks (PINNs), which efficiently learn using physical laws as a loss function, along with a high-precision inverse kinematics model of a digital human. Through this approach, we enable high-accuracy full-body posture estimation even with wearable devices in underdetermined scenarios. Full article

The present study aims to investigate the multi-year effects (5 years) of individualized whole-body vibration (WBV) on locomotion, postural control, and handgrip strength in a 68-year-old man with relapse remitting multiple sclerosis (PwRRMS). The dose–response relationship induced by a single session was quantified by determining the surface electromyographic activity (sEMG) of the participant. The participant wore an orthosis to limit the lack of foot dorsiflexion in the weakest limb during walking in daily life. The gait alteration during walking was assessed at 1, 2 and 3 km/h (without the orthosis) through angle–angle diagrams by quantifying the area, perimeter and shape of the loops, and the sEMG of leg muscles was recorded in both limbs. The evaluation of postural control was conducted during upright standing by quantifying the displacement of the center of pressure (CoP). The handgrip strength was assessed by measuring the force–time profile synchronized with the sEMG activity of upper arm muscles. The participant improved his ability to walk at higher speeds (2–3 km/h) without the orthosis. There were greater improvements in the area and perimeter of angle–angle diagrams for the weakest limb (Δ = 36–51%). The sEMG activity of the shank muscles increased at all speeds, particularly in the tibialis anterior of weakest limbs (Δ = 10–68%). The CoP displacement during upright standing decreased (Δ = 40–60%), whereas the handgrip strength increased (Δ = 32% average). Over the 5-year period of intervention, the individualized WBV improved locomotion, postural control and handgrip strength without side effects. Future studies should consider the possibility of implementing an individualized WBV in PwRRMS. Full article

Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational health issues, particularly affecting public transport drivers due to prolonged sitting, constrained postures, and poorly adaptable cabins. This study addresses the ergonomic risks associated with tram driving, aiming to evaluate biomechanical load and postural stress in relation to drivers’ anthropometric characteristics. A combined methodological approach was applied, integrating two standardized observational tools—RULA and REBA—with anthropometric modeling based on three representatives European morphotypes (SmallW, MidM, and TallM). ErgoFellow 3.0 software was used for digital posture evaluation, and lumbar moments at the L4/L5 vertebral level were calculated to estimate lumbar loading. The analysis was simulation-based, using digital human models, and no real subjects were involved. The results revealed uniform REBA (Rapid Entire Body Assessment) and RULA (Rapid Upper Limb Assessment) scores of 6 across all morphotypes, indicating moderate to high risk and a need for ergonomic intervention. Lumbar moments ranged from 51.35 Nm (SmallW) to 101.67 Nm (TallM), with the tallest model slightly exceeding the recommended ergonomic thresholds. These findings highlight a systemic mismatch between cabin design and user variability. In conclusion, ergonomic improvements such as adjustable seating, better control layout, and driver education are essential to reduce the risk of WMSDs. The study proposes a replicable methodology combining anthropometric, observational, and biomechanical tools for evaluating and improving transport workstation design. Full article

Objective: The objective of the study was to investigate the relationship between the level of physical activity and body composition, and the levels of motor skills and strength in overweight and obese men. Methods: The research involved 64 men. Body composition, physical activity, motor control, Motor Control Test (MCT), and strength variables were evaluated. Body composition was assessed by DXA, and the participants were classified into two groups according to the percentage of total fat mass: greater and less than 27.65%. Physical activity was assessed using accelerometry, and motor control was measured with posturography, which provided a composite score of motor performance and postural control effectiveness. Strength was assessed using hand, leg, and back dynamometers. Results: The participants with a higher percentage of body fat had a lower DSI (Dynamic Strength Index) (p < 0.001) and significantly reduced PAL (physical activity level) and energy expenditure (p < 0.001). No significant differences were found in the muscle strength of the upper limbs (p = 0.06) and lower limbs (p = 0.419). With regard to MCT, there was a significant difference between groups in the backward direction (p = 0.041), with the group with the highest percentage of body fat showing lower values. Conclusions: Individuals with a higher percentage of body fat tend to have lower levels of strength, physical activity, and energy expenditure, which can lead to impaired balance. The findings highlight the need for targeted interventions to improve body composition and levels of strength and physical activity, with a positive impact on general health and quality of life. Emphasis should be placed on improving physical activity levels in male individuals with a higher percentage of fat mass to improve their body composition and dynamic strength levels, which are beneficial to life, particularly to help improve postural control. Full article

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. 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

Objectives: This study aims to identify the effectiveness of exercise in chronic tension-type headache and chronic migraine. Methods: The PICOS (Population, Intervention, Comparator, Outcomes, Study design) strategy was followed, where P—patients with chronic tension-type headache or chronic migraine; I—exercise; C—conventional treatment; O—pain reduction; and S—RCTs (randomized controlled trials) and quasi-experimental trials. Studies with a high risk of bias according to the RoB 2 (Risk of Bias) scale and with a score < 6 according to the PEDro (Physiotherapy Evidence Database) scale were excluded. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement was followed. The databases Pubmed, Web of Science, and Scopus were searched in November 2024. The PEDro and RoB2 scales were used to assess the risk of bias and internal validity of the studies. The results were synthesized qualitatively. Results: Ten studies with a total sample of 848 subjects were analyzed, comparing therapeutic interventions with an exercise versus conventional treatment. In most of the studies, the exercise group significantly outperformed the control group in pain reduction. Discussion: The findings suggest that exercise improves central pain modulation and reinforces the potential of body strategies such as postural integration. The main limitations found were the limited evidence on exercise’s role in chronic tension-type headache or migraine and the risk of bias due to the difficulty of blinding patients, therapists, and evaluators. Conclusions: The studies analyzed have shown that exercise could be an effective strategy to support the management of chronic tension-type headache and migraine. Full article

Background/Objectives: Spasticity in children with cerebral palsy (CP) can impair motor-related functions. The objective of this exploratory, prospective study was to examine if transcutaneous electrical nerve stimulation (TENS) in a whole-body suit leads to changes in spasticity and other related effects. Methods: Thirty-one children with CP GMFCS III–V, with a median age of 11.0 years (age range of 7–17 years), were consecutively included, and they used the suit with TENS for 24 weeks. The primary outcome was spasticity measured using the Modified Ashworth Scale (MAS). Functional motor-related tasks were evaluated by the Goal Attainment Scale (SMART GAS). The Modified Tardieu Scale (MTS), passive Range of Motion (pROM), GMFM-66, and Posture and Postural Ability Scale (PPAS) assessments were performed. Results: Seventeen subjects (17/31) completed the 24 weeks. Dropout was due to difficulty in donning the suit. The level of overall spasticity, most pronounced in the proximal arms and legs, was reduced according to the MAS, but not the MTS or pROM. Subject-relevant motor-related goals improved significantly in standing/walking and hand/arm function. Changes in the GMFM-66 and PPAS were not significant. Conclusions: Although there were statistically significant but underpowered changes in the MAS after 24 weeks, there were no clinically relevant effects. Exploratorily, we found observer-reliant motor-related functional improvements, which, however, we were unable to detect when trying to quantify them. Donning the suit led to dropout throughout the study. Caregivers need to allocate time, mental capacity and have the physical skill set for donning the suit for long-term use. Full article

Background. Sleep disorders are a group of conditions that disrupt normal sleep patterns and are among the most common clinical challenges faced today. An innovative device that employs nanotechnology to deliver beneficial effects on the human body is the Taopatch^® (Tao Technologies, Vedelago, Italy). This study aims to assess the effectiveness of such nanotechnology-based devices in improving sleep quality. Methods. This study included only female participants, as a review of the literature indicated that sleep disorders are more prevalent in women than in men. A total of 30 subjects (with a mean age of 44.8 ± 3.44 years) were randomly divided into two groups: an experimental group and a control group. Sleep quality was evaluated three times throughout the study for each participant using the Pittsburgh Sleep Quality Index (PSQI). The Taopatch^® devices were applied using a specialized pillow. Results. The experimental group showed significantly better sleep quality (p < 0.001) compared to the control group. Conclusions. Our findings suggest that the application of the Taopatch^® has a positive impact on sleep quality by optimizing posture, aligning the cervical spine, and promoting muscle relaxation. This device uses advanced nanotechnology to enhance various physiological functions, contributing to better overall well-being. Full article

Background and Objectives: Children with cerebral palsy (CP) improve walking abilities through partial body weight-supported treadmill training (PBWSTT) and loaded treadmill training (LTT), but there is no consensus on the most effective method. This study aimed to evaluate the effects of PBWSTT and LTT on spatiotemporal gait parameters in children with CP. Materials and Methods: A randomized clinical trial involved 25 children aged 12+ with spastic diplegic CP from various outpatient clinics in Taif and Makkah between January 2024 and January 2025. Participants were randomly assigned to PBWSTT (30% body weight support, n = 12) or LTT (60% lower limb weight loading, n = 13) with 45 min sessions three times per week for eight weeks, including conventional therapy. Results: The spatiotemporal gait parameters (such as gait speed, cadence, stride length, swing phase, and swing width) significantly improved within the PBWSTT and LTT groups, but no significant difference was found between the groups. The gross motor function measure, dimension E (for walking, running, and jumping), showed significantly higher improvement in the PBWSTT group compared to the LTT group (p = 0.047). Conclusions: This study indicates that PBWSTT and LTT can improve gait parameters in children with CP, with PBWSTT promoting postural control and LTT improving mobility. These findings suggest that the proposed rehabilitation strategies can significantly improve the functional outcomes of pediatric cerebral palsy patients. Full article

(1) Background: Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern in the construction industry owing to the physically demanding tasks and poor ergonomic conditions present. Limited data exist on the prevalence of WMSDs and their ergonomic determinants among construction workers in India. This study investigated the prevalence of work-related musculoskeletal disorders (WMSDs) and the associated ergonomic risk factors among construction workers in India. (2) Methods: This cross-sectional study was conducted among 250 construction workers in India. Data on musculoskeletal disorders were collected using the Nordic Musculoskeletal Questionnaire (NMQ), and ergonomic risk was assessed using the Rapid Entire Body Assessment (REBA) tool. Logistic regression was used to identify factors associated with WMSDs, and Spearman’s correlation was used to assess the relationship between the REBA scores and the number of affected body regions. (3) Results: The prevalence of WMSDs was 60.4%. The most affected regions were the lower back (48%), knees (45%), shoulders (40%), and the neck (30%). The REBA scores indicated that 60% of the workers were at high or very high ergonomic risk and 30% at medium risk. Workers in the high/very high-risk category had significantly higher odds of developing WMSDs (OR = 4.5, 95% CI: 1.8–11.2, p = 0.001). Age above 40 years was also significantly associated with WMSDs (OR = 3.5, 95% CI: 1.2–10.2, p = 0.02). (4) Conclusions: This study demonstrated a high prevalence of WMSDs among Indian construction workers and established a clear association with poor ergonomic conditions. Targeted ergonomic interventions, including posture improvement, tool redesign, and safety training, are essential for reducing the risk of WMSDs in this population. Full article

Human pose estimation is an important research direction in the field of computer vision, aiming to detect and locate key points of the human body from images or videos and infer human posture. It plays a significant role in many applications, such as action recognition, motion analysis, virtual reality, and human–computer interaction. As a popular research topic, it is often studied by beginners in deep learning. However, the task of human pose estimation is rather complex, and the mainstream datasets are huge. Even on high-end single-GPU devices, training models requires a considerable amount of time. To help beginners learn efficiently on devices with limited performance, this paper introduces the method of mixed-precision training into the model and combines it with early stopping to reduce the training time. The experimental results show that after introducing mixed-precision training, the training speed of the model was significantly improved and there was no significant decrease in model accuracy. Full article

In addition to axial motor complications such as abnormal posture, instability, falls, and gait variability, neurodegenerative diseases like Parkinsonian syndromes include executive dysfunction, Parkinson’s disease dementia, and neuropsychiatric symptoms. These motor disorders significantly affect mobility, quality of life, and well-being. Recently, physical activity of various intensities monitored both remotely and face-to-face via digital health technologies, mobile platforms, or sensory cues has gained relevance in managing idiopathic and atypical Parkinson’s disease (PD and APD). Remote monitoring solutions, including home-based digital health assessments using semi-structured activities, offer unique advantages. Real-world gait parameters like walking speed can now be continuously assessed with body-worn sensors. Developing effective strategies to slow pathological aging and mitigate neurodegenerative progression is essential. This study presents outcomes of using digital health technologies (DHTs) for remote assessment of motor function, physical activity, and daily living tasks, aiming to reduce disease progression in PD and APD. In addition to wearable inertial sensors, clinical rating scales and digital biomarkers enhance the ability to characterize and monitor motor symptoms. By reviewing recent literature, we identified emerging trends in quantifying and intervening in neurodegeneration using tools that evaluate both remote and face-to-face physical activity. Our findings confirm that DHTs offer accurate detection of motor fluctuations and support clinical evaluations. In conclusion, DHTs represent a scalable, effective strategy for improving the clinical management of PD and APD. Their integration into healthcare systems may enhance patient outcomes, support early intervention, and help delay the progression of both motor and cognitive symptoms in aging individuals. Full article

Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications. Full article