Search Results (234)

Introduction and Aim: Sarcopenia has emerged as a key prognostic factor in patients with chronic limb-threatening ischemia (CLTI), with potential implications for clinical decision-making. This study aimed to assess the association between sarcopenia and clinical outcomes, mortality, and amputation, using simple, accessible screening tools in a CLTI population. Methods: In this prospective, single-center study conducted between December 2023 and December 2024, 170 patients with CTLI were enrolled. Sarcopenia screening was performed using the SARC-F (strength, assistance in walking, rising from a chair, climbing stairs, falls) questionnaires, handgrip strength measurement, and calf circumference, adjusted for body mass index and sex. The primary outcome was 6-month all-cause mortality and/or major amputation. Results: Sarcopenia was identified in 77 patients (45.3%). Compared to non-sarcopenic individuals, sarcopenic patients were significantly older. They exhibited greater functional impairment, as well as poorer nutritional and muscle status. They also had significantly higher in-hospital mortality (16.9% vs. 3.2%, p = 0.002), 30-day mortality (24.7% vs. 4.3%, p = 0.001), and 6-month mortality (50.6% vs. 15.1%, p = 0.001). Sarcopenia was significantly associated with the primary outcome in univariate analysis (HR: 2.05; 95% CI: 1.31–3.20; p = 0.002) and remained an independent predictor after multivariate adjustment (HR: 1.95; 95% CI: 1.01–3.79; p = 0.048). Conclusions: Sarcopenia is a strong, independent predictor of poor outcome in patients with CLTI. Its detection through simple tools offers an easy and cost-effective strategy to improve risk stratification and guide early intervention through exercise-based therapy. Full article

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems. 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

Background: Prehabilitation programs improve postoperative outcomes in vulnerable patients undergoing major surgery. However, current screening tools such as the Malnutrition Universal Screening Tool (MUST) may lack the sensitivity needed to identify those who would benefit most. Muscle quality assessed by Computed Tomography (CT), specifically muscle radiodensity in Hounsfield Units (HUs), has emerged as a promising alternative for risk stratification. Objective: To evaluate the prognostic performance of CT-derived muscle radiodensity in predicting adverse postoperative outcomes in colorectal cancer patients, and to compare it with the performance of the MUST score. Methods: This single-center cross-sectional study included 201 patients with non-metastatic colon cancer undergoing elective laparoscopic resection. Patients were stratified based on enrollment in a multimodal prehabilitation program, either within an Enhanced Recovery After Surgery (ERAS) protocol or a non-ERAS pathway. Nutritional status was assessed using MUST, SARC-F questionnaire (strength, assistance with walking, rise from a chair, climb stairs, and falls), and the Global Leadership Initiative on Malnutrition (GLIM) criteria. CT scans at the L3 level were analyzed using automated segmentation to extract muscle area and radiodensity. Postoperative complications and hospital stay were compared across nutritional screening tools and CT-derived metrics. Results: MUST shows limited sensitivity (<27%) for predicting complications and prolonged hospitalization. In contrast, CT-derived muscle radiodensity demonstrates higher discriminative power (AUC 0.62–0.69), especially using a 37 HU threshold. In the non-ERAS group, patients with HU ≤ 37 had significantly more complications (33% vs. 15%, p = 0.036), longer surgeries, and more severe events (Clavien–Dindo ≥ 3). Conclusions: Opportunistic CT-based assessment of muscle radiodensity outperforms traditional screening tools in identifying patients at risk of poor postoperative outcomes, and may enhance patient selection for prehabilitation strategies like the ERAS program. Full article

Background: The ankle joint plays a key role in stabilizing the lower limb during interaction with ground reaction forces. Instability can result in pain, weakness, and impaired movement. Although assessing ankle stability is important, few studies examine existing in vivo methodologies for dynamic load assessment, limiting effective injury management. Objective: To identify in vivo techniques using objective measurement tools for assessing ankle stability during dynamic exercise. Methods: A scoping review was performed based on PRISMA-ScR criteria. Five databases—PubMed, PEDro, Embase, SPORTDiscus, and CDSR—were searched from inception to September 2024. Results: Out of 1678 records, 32 studies met the inclusion criteria. A total of 1142 subjects were included: 293 females (25.6%), 819 males (71.7%), and 30 unspecified (2.62%). Six categories of dynamic exercise were identified: analytical, functional, balance, stair climbing, running, and walking. The techniques used included 3D motion capture, force and pressure platforms, dynamometry, electromyography, accelerometers, pressure and speed sensors, instrumented treadmills, and inertial measurement units. Conclusions: The 3D motion capture systems (240 Hz) and the force platforms (1000 Hz) were most frequently used in functional tasks and walking. Combining these with multisegmented foot models appears optimal, though tool selection depends on study goals. This review enhances our understanding of ankle stability assessment. Full article

The current pre-clinical testing standards for total hip replacements (THRs), ISO standards, use simplified loading waveforms that do not fully replicate real-world biomechanics. These standards provide a benchmark of data that may not accurately predict in vivo wear, necessitating the evaluation of physiologically relevant loading conditions. Previous studies have incorporated activities of daily living (ADLs) such as walking, jogging and stair negotiation into wear simulations. However, these studies primarily used simplified adaptations that increased axial forces and applied accelerated sinusoidal waveforms, rather than fully replicating the complex kinematics experienced by THR patients. To address this gap, this study applied patient-derived ADL profiles—jogging and stair negotiation—using a three-station hip simulator, obtained through 3D motion analysis of total hip arthroplasty patients, processed via a musculoskeletal multibody modelling approach to derive realistic hip contact forces (HCFs). The results indicate that jogging significantly increased wear rates compared to the ISO walking gait waveform, with wear increasing from 15.24 ± 0.55 to 28.68 ± 0.87 mm³/Mc. Additionally, wear was highly sensitive to changes in lubricant protein concentration, with an increase from 17 g/L to 30 g/L reducing wear by over 60%. Contrary to predictive models, stair descent resulted in higher volumetric wear (8.62 ± 0.43 mm³/0.5 Mc) compared to stair ascent (4.15 ± 0.31 mm³/0.5 Mc), despite both profiles having similar peak torques. These findings underscore the limitations of current ISO standards in replicating physiologically relevant wear patterns. The application of patient-specific loading profiles highlights the need to integrate ADLs into pre-clinical testing protocols, ensuring a more accurate assessment of implant performance and longevity. Full article

In recent years, research on human activity recognition (HAR) on smartphones has received extensive attention due to its portability. However, the discrimination issues between similar activities such as leaning forward and walking forward, as well as going up and down stairs, are hard to deal with. This paper conducts HAR based on the sensors of smartphones, i.e., accelerometers and gyroscopes. First, a feature extraction method for sensor data from both the time domain and frequency domain is designed to obtain more than 300 features, aiming to enhance the accuracy and stability of recognition. Then, the LightGBM (version 4.5.0) algorithm is utilized to comprehensively analyze the above-mentioned extracted features, with the goal of improving the accuracy of similar activity recognition. Through simulation experiments, it is demonstrated that the feature extraction method proposed in this paper has improved the accuracy of HAR. Compared with classical machine learning algorithms such as random forest (version 1.5.2) and XGBoost (version 2.1.3), the LightGBM algorithm shows improved performance in terms of the accuracy rate, which reaches 94.98%. Moreover, after searching for the model parameters using grid search, the prediction accuracy of LightGBM can be increased to 95.35%. Finally, using feature selection and dimensionality reduction, the efficiency of the model is further improved, achieving a 70.14% increase in time efficiency without reducing the accuracy rate. Full article

Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities—running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting—while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system’s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities. Full article

This systematic review, conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD42024619693), aimed to evaluate the effects of physical exercise interventions on muscle function and fall risk in older adults with and without sarcopenia. Methods: A comprehensive search of PubMed and Web of Science databases identified 11 randomized controlled trials (RCTs) published between 2015 and 2025. A total of 792 participants (mean age 75.13 ± 4.71 years; 65.53% women, 34.47% men) were included. Interventions varied in type—strength, balance, aerobic, and multi-component programs—with a minimum duration of 8 weeks. Results: The reviewed studies showed that physical exercise interventions significantly improved neuromuscular function, physical performance, and postural control in older adults. Positive effects were observed in gait speed, stair-climbing ability, grip strength, muscle mass, and bone density. Specific modalities such as Tai Chi improved postural control and neuromuscular response; dynamic resistance and functional training increased muscle strength and improved posture; Nordic walking reduced postural sway; and multi-component and combined walking-resistance training enhanced mobility and force efficiency. Programs integrating strength and balance components yielded the most consistent benefits. However, reporting on FITT (Frequency, Intensity, Time, Type) principles was limited across studies. Conclusions: Exercise interventions are effective in improving neuromuscular outcomes and reducing fall risk in older adults, both with and without sarcopenia. The findings support the need for tailored, well-structured programs and greater methodological standardization in future research to facilitate broader clinical application and maximize health outcomes. Full article

In the human–exoskeleton interaction process, accurately recognizing gait phases is crucial for effectively assessing the assistance provided by the exoskeleton. However, due to the similarity in muscle activation patterns between adjacent gait phases, the recognition accuracy is often low, which can easily lead to confusion in surface electromyography (sEMG) feature extraction. This paper proposes a real-time recognition method based on multi-scale fuzzy approximate root mean entropy (MFAREn) and an Efficient Multi-Scale Attention Convolutional Neural Network (EMACNN), building upon the concept of fuzzy approximate entropy. MFAREn is used to extract the dynamic complexity and energy intensity features of sEMG signals, serving as the input matrix for EMACNN to achieve fast and accurate gait phase recognition. This study collected sEMG signals from 10 subjects performing continuous lower limb gait movements in five common motion scenarios for experimental validation. The results show that the proposed method achieves an average recognition accuracy of 95.72%, outperforming the other comparison methods. The method proposed in this paper is significantly different compared to other methods (p < 0.001). Notably, the recognition accuracy for walking in level walking, stairs ascending, and ramp ascending exceeds 95.5%. This method demonstrates a high recognition accuracy, enabling sEMG-based gait phase recognition and meeting the requirements for effective human–exoskeleton interaction. Full article

The complexity of subway station space can impact the efficiency of passenger navigation. The subway spatial environment is a key factor affecting indoor wayfinding for pedestrians; however, the research framework that examines how various environment factors influence pedestrians during different stages of wayfinding remains ambiguous. This study examines how environmental elements may affect users to varying degrees at different stages of wayfinding, which in turn affects their wayfinding efficiency, recording and analyzing the wayfinding performance and eye-tracking data of 32 participants. The findings reveal that different environment factors exert varying degrees of influence on pedestrians at different stages of wayfinding. Significantly, signage (p = 0.000) proves to have the most substantial impact on wayfinding, followed by stairs/escalators (p < 0.05), but the participants walked to the wrong platform in the TD2 scenario because they were guided by the Line 2 signs in front of the stairs/escalators. Thus, the influence of signage is not entirely positive. This study contributes to an understanding of the differences in the influence of environmental elements on wayfinding during different wayfinding stages, and provides suggestions for the spatial environmental design of subway stations and the improvement of wayfinding efficiency. Full article

Consumer-grade wrist-based wearable devices have grown in popularity among researchers to continuously collect metrics such as physical activity and heart rate. However, manufacturers rarely disclose the preprocessing sensor data algorithms, and user-generated data are typically shared leading to data governance issues. Open-source technology may address these limitations. This study evaluates the validity of the Bangle.js2 for step counting and heart rate during lab-based validation and agreement with other wearable devices (steps: Fitbit Charge 5; heart rate: Polar H10) in free-living conditions. A custom open-source application was developed to capture the sensor data from the Bangle.js2. Participants (n = 47; 25 males; 27 ± 11 years) were asked to complete a lab-based treadmill validation (3 min stages at 2, 3, 4, and 5 mph) and stair climbing procedure followed by a 24 h free-living period. The Bangle.js2 demonstrated systematic undercounting of steps at slower walking speeds with acceptable error achieved at 5 km/h. During free-living conditions, the Bangle.js2 demonstrated strong agreement with the Fitbit Charge 5 for per-minute step counting (CCC = 0.90) and total steps over 24 h (CCC = 0.96). Additionally, the Bangle.js2 demonstrated strong agreement with the Polar H10 for minute-averaged heart rate (CCC = 0.78). In conclusion, the Bangle.js2 is a valid open-source hardware and software solution for researchers interested in step counting and heart rate monitoring in free-living conditions. Full article

Background/Objectives: Today, digital technologies are integral to children’s lives; their increasing use, however, may raise health concerns. This study aims to examine the effect of screen time on physical activity and lifestyle factors in German school children. Methods: As part of the prospective hand-on-heart-study (“Hand-aufs-Herz”), a comprehensive cardiovascular system check-up examination was conducted on 922 German schoolchildren. The pupils were asked for a self-report on their daily physical activities and club sports. The examinations on-site contained measurements of the pupils’ weight and height as well as their physical fitness, which was assessed by a stair-climbing test. Results: A large proportion of pupils had a screen time of more than 2 h daily, regardless of the day of the week (63–76%). In fact, pupils with a screen time ≥ 2 h were more likely to achieve poor grades in school (weekday ORs 3.23, 95% CI 1.76, 5.95; weekend ORs 3.28, 95% CI 1.53, 7.00) and not be members of a sports club (weekday ORs 2.35, 95% CI 1.68, 3.29; weekend ORs 2.13, 95% CI 1.44, 3.14). Pupils who did not meet both recommendations for physical activity and screen time walked <5000 steps daily (60%), had a high proportion of overweight/obesity (40%), were non-swimmers (38.5%), spent ≥7 h sitting (35.8%), and slept fewer hours than recommended (30%). It has also been shown that longer screen time has a negative impact on the lifestyle of children and young people. Conclusions: Our results show that excessive screen time in children is linked to higher weight and an unhealthy lifestyle, increasing long-term cardiovascular risks. Public health initiatives aimed at reducing screen time, promoting physical activity, and encouraging healthier habits are essential to improve children’s overall health and prevent future chronic diseases. Full article

Background: Silver–Russell syndrome (SRS) is a genetic disorder characterized by prenatal and postnatal growth retardation. Affected individuals commonly present with low birth weight, intrauterine growth restriction, postnatal short stature, hemihypotrophy, characteristic facial features, and body asymmetry. Methods: This study includes 24 Taiwanese children with SRS aged 2 years to 13 years and 3 months who were recruited at MacKay Memorial Hospital and other Taiwan hospitals between January 2013 and December 2024. Functional independence was assessed using the Functional Independence Measure for Children (WeeFIM) to evaluate self-care, mobility, and cognition domains. Results: The mean total WeeFIM score was 106.9 ± 23.2 (range: 54–126), with mean self-care, mobility, and cognition scores of 44.4 ± 13.8 (maximum 56), 32.4 ± 5.1 (maximum 35), and 30.2 ± 6.0 (maximum 35), respectively. The results of the restricted cubic spline analysis reveal a clear positive linear correlation before school age (approximately 72 months), followed by a plateau (p for nonlinearity < 0.05). Traceable molecular data were available for thirteen participants, of whom nine (69%) had loss of methylation at chromosome 11p15 (11p15LOM), and four (31%) had maternal uniparental disomy of chromosome 7 (upd(7)mat). Of the 24 children, 46% required assistance with bathing, which was strongly correlated with self-care ability and body height. In contrast, most of the children had independence in mobility tasks such as walking and stair climbing. However, some required support in cognitive tasks, including problem-solving, comprehension, and expression. Overall, the included children reached a functional plateau later than the normative population, with the greatest delays in self-care and mobility domains. Conclusions: This study highlights that Taiwanese children with SRS require support in self-care and cognitive tasks. Functional independence in self-care and mobility domains was positively associated with body height. The WeeFIM questionnaire effectively identified strengths and limitations, emphasizing the need for individualized support in daily activities. Full article

Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a surface electromyography (EMG)-based scheme for lower limb motion recognition to enhance human-computer interaction in intelligent prostheses. Addressing the loss of phase information in existing methods, the approach combines S-transform energy concentration and multi-channel fusion analysis. EMG signals from six lower limb muscles of 10 subjects performing four movements (level walk, stair ascent, stair descent, and obstacle crossing) were analyzed. Correlation analysis identified the most relevant and least correlated muscles, optimizing signal quality. Using support vector machines (SVM), motion recognition accuracy was evaluated for single-channel and multi-channel signals. Results indicated that the semi-tendon and rectus femoris muscles achieved 80.71% accuracy with simple time-frequency features, while the medial gastrocnemius and rectus femoris reached 93.70% accuracy with S-transform energy concentration. Multi-channel fusion (rectus femoris, biceps femoris, and medial gastrocnemius) based on S-transform achieved over 96% accuracy, demonstrating superior recognition performance and potential for improving adaptive human-robot interaction in prosthetic control. Full article