Search Results (300)

Background: Frailty is a major determinant of adverse outcomes in older adults with cardiovascular disease. Automated digital tools may facilitate routine frailty assessment in hospital settings; however, their validity and prognostic relevance in acutely hospitalized patients remain insufficiently established. Methods: In this prospective cohort study, 113 hospitalized cardiology patients underwent frailty assessment using both manual Short Physical Performance Battery (mSPPB) and an automated electronic SPPB (eSPPB) system. Agreement between methods was evaluated using Pearson correlation, intraclass correlation coefficients (ICCs), and Bland–Altman analysis. Frailty was defined as SPPB < 5. The association between frailty and 30-day mortality was assessed using logistic regression and Kaplan–Meier survival analysis. Results: Seventeen patients (15.0%) were classified as frail. Automated and manual SPPB scores were highly correlated (r = 0.994, p < 0.001) and demonstrated good agreement (ICC = 0.80). Bland–Altman analysis showed a mean difference of −1.63 points (95% limits of agreement −4.41 to 1.16). Frailty was associated with significantly higher 30-day mortality (17.6% vs. 2.1%, p = 0.009), corresponding to a tenfold increase in mortality odds (OR 10.07; 95% CI 1.5–67.5). An exploratory model showed apparent discriminative performance (AUC 0.83; 95% CI 0.71–0.95). Conclusions: Automated eSPPB demonstrated good agreement with manual assessment and was significantly associated with short-term mortality in hospitalized cardiovascular patients. These findings support the validity and potential clinical utility of automated frailty assessment for risk stratification in acute cardiology settings. Full article

Accurate and robust battery health prognostics are critical for reliable battery management in electronic devices and electric vehicles. Previous studies have demonstrated that combining electrochemical impedance spectroscopy (EIS) with machine learning enables accurate health-state forecasting in LiCoO₂ coin cells. However, the applicability of this EIS-AI paradigm across diverse chemistries and industrial-grade battery formats remains unvalidated, limiting its practical deployment in energy storage systems. Here, we develop an EIS–AI battery prognostic framework and validate its performance on LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) cylindrical cells and LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811) pouch cells. A hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture is developed to estimate state of health (SoH) and predict remaining useful life (RUL) from EIS spectra. Trained on an in-house dataset comprising over 13,000 impedance spectra from 22 cells (8 NMC111 and 14 NMC811), the model achieves robust performance, with average coefficients of determination (R²) exceeding 0.92 for SoH estimation and 0.90 for RUL prediction across various batteries and cycling protocols. Salient feature analysis further reveals chemistry- and protocol-dependent frequency regimes associated with degradation. These results demonstrate that impedance spectra constitute physically informative descriptors for data-driven battery prognostics and provide a scalable and interpretable pathway for deploying EIS-AI frameworks in real-world battery management systems (BMSs). Full article

Objective: To validate the Oldfry instrumental device for efficiently detecting the risk of falls and frailty in older adults. Design and Methods: An observational, analytical, cross-sectional, multicenter, non-randomized study to validate an instrumental device. It was conducted in several nursing homes for the elderly in the province of Castellón, Comunidad Valenciana, Spain, from February to April 2024. The estimated necessary sample size was 149 people. Specific selection criteria and voluntary acceptance to participate in the study were established. Sociodemographic, anthropometric, and other variables such as fall history in the past year were collected. A descriptive and comparative analysis of the variables was performed. The validity and reliability of the device in its measurements were determined to compare the results of the Timed Up and Go (TUG) test and the Short Physical Performance Battery test (SPPB), with respect to the Oldfry instrumental device. Informed consent was obtained from all participants, and the study was approved by the Bioethics Committee of the University Jaume I. Results: The sample consisted of 151 participants with a median age of 84 years (IQR [78.0–91.0]), comprising 39.10% men and 60.90% women, 65 years of age or older. Oldfry presents a sensitivity of 45.90% and a specificity of 72.7% for the risk of falls with a correlation R: 0.773 and an ICC concordance: 0.821. For frailty assessment, it shows a sensitivity of 91.90% and a specificity of 9.10% with an R: 0.854 and ICC: 0.805. Conclusions: This device has proven to be an effective tool for detecting both the risk of falls and frailty in older adults residing in institutions, showing high levels of reliability, sensitivity, and high concordance and correlation in both measurements. Future studies are anticipated to evaluate the benefits of this application. Full article

Aging is a progressive and heterogeneous biological process influenced by multiple factors that may compromise physical and cognitive capacities and increase the risk of frailty, functional decline, and falls in older adults. Falls represent a major public health concern due to their impact on independence and long-term care demand. Immersive virtual reality (IVR) delivered through active video games (exergames) has emerged as a preventive strategy that integrates sensory, motor, and cognitive stimulation within controlled and engaging environments, particularly where traditional programs face challenges related to adherence and individual adaptation. This study aims to determine the feasibility and implementation of an IVR-based program for falls prevention in older adults at risk of frailty in primary health care (PHC). A quasi-experimental pre–post design will be conducted with an intervention group (IVR/exergames) and a conventional control group, including a total sample of 40 participants (20 per group). The protocol comprises three phases: baseline assessment and IVR familiarization; a 12-week intervention delivered twice weekly; and post-intervention assessment. The primary outcome will be fall risk assessed using the Timed Up and Go (TUG) test. Secondary outcomes include physical performance (Short Physical Performance Battery, SPPB, and handgrip dynamometry) and psychological aspects related to falls (Falls Efficacy Scale International, FES-I, and Activities-specific Balance Confidence Scale, ABC). Feasibility indicators will include recruitment, adherence, retention, and cybersickness. A reduction in TUG time is expected, providing preliminary evidence on the feasibility of integrating IVR-based programs for falls prevention within PHC systems. Full article

Background and Objectives: The contribution of body composition to muscle weakness and physical performance in older adults remains incompletely defined. This study aimed to evaluate the discriminative capacity of total and segmental body composition variables to identify muscle weakness and low physical performance in older adults. Materials and Methods: A cross-sectional study was conducted in 268 community-dwelling older adults (72.2 ± 8.2 years; 81.3% women). Body composition (lean mass, fat mass, and bone mineral content [BMC], total and segmental) was assessed using dual-energy X-ray absorptiometry. Muscle weakness was assessed by handgrip strength (≤27 kg in men; ≤16 kg in women), and low physical performance by the Short Physical Performance Battery ≤8. Sex-stratified receiver operating characteristic (ROC) analyses were performed. Results: No significant differences were found between sexes for age (p = 0.307) or body mass index (p = 0.892). However, men exhibited significantly higher waist circumference (105.2 ± 11.9 vs. 97.8 ± 12.4 cm; p < 0.001) and handgrip strength (30.3 ± 6.8 vs. 18.3 ± 4.6 kg; p < 0.001) than women. Regarding body composition, men presented higher total lean mass (50.4 ± 6.9 vs. 37.2 ± 4.6 kg; p < 0.001) and total bone mineral content (2666 ± 483 vs. 1940 ± 286 g; p < 0.001). Conclusions: Body composition variables showed higher discriminative capacity for muscle weakness than for low physical performance. The ability of lean mass and BMC to identify low physical performance was modest in both sexes, suggesting that structural body composition variables alone may be insufficient to discriminate complex functional impairment in older adults. Full article

Frailty is a highly prevalent and adverse syndrome among older adults, and there are many different assessments for screening both frailty (robust + pre-frail vs. frail) and frailty process (robust vs. pre-frail + frail). Previous studies have suggested that the Short Physical Performance Battery (SPPB) can be used as a quick screening tool for frailty, demonstrating excellent agreement when compared to Fried’s phenotype as a criterion. However, to the best of our knowledge, no study has assessed the SPPB’s diagnostic accuracy using the FRAIL questionnaire as the criterion. In this cross-sectional study, we compared frailty (SPPB ≤ 8) and frailty process (SPPB ≤ 10) classifications for 371 community-dwelling older adults (≥60 yrs) by the SPPB to the FRAIL questionnaire using McNemar tables and a receiver operator characteristic analysis. The SPPB and the FRAIL questionnaire significantly differed in their appraisal of both frailty and frailty process (p < 0.001). For frailty, the SPPB scored a sensitivity of 62.9%, a specificity of 78.6%, and an area under the curve of 0.78. In addition, for the frailty process, the SPPB scored a sensitivity of 77.6%, a specificity of 55.3%, and an area under the curve of 0.70. The SPPB demonstrated limited diagnostic accuracy compared to the criterion FRAIL questionnaire. Our findings indicate that the SPPB should not be the sole method of assessing frailty among older adults. To address the complexity of frailty, clinicians should attempt to implement multiple assessments that combine biological, social, and functional aspects of frailty. Pre-registered on ClinicalTrials.gov (NCT05778604). Full article

Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The proposed approach integrates a physics-based baseline model for operational normalization, a multi-sensor fusion attention mechanism to model cross-modality interactions, and a lightweight transformer architecture for efficient temporal representation learning. Weak supervision is derived from physics-consistent residual analysis with temporal smoothing, enabling scalable training without dense manual annotations. To support reliable deployment, evidential uncertainty modeling and conformal calibration are incorporated to obtain statistically controlled decision thresholds. Experiments conducted on a real driving cycle dataset from IEEE DataPort demonstrate that SFF consistently outperforms classical machine learning methods, deep neural networks, and standard transformer models in terms of early-warning lead time, false alarm rate, and inference efficiency while maintaining competitive discriminative performance. Cross-scenario evaluations under diverse thermal conditions further confirm the robustness and generalization capability of the proposed framework. Full article

Background: Frailty is highly prevalent among older adults with cardiovascular disease (CVD) and strongly predicts disability and mortality after cardiac events. Although cardiac rehabilitation (CR) improves prognosis, frail older patients often face barriers to participating in in-person programs. eHealth-based, home-delivered CR programs incorporating tele-rehabilitation and remote monitoring may improve accessibility, yet evidence regarding their effectiveness on frailty status remains limited. Methods: We designed a multicenter, randomized, parallel-group trial enrolling people ≥65 years recently hospitalized for acute heart failure (AHF) and/or acute coronary syndrome (ACS). Participants were randomized 1:1 to an eHealth home-based tele-rehabilitation program or the usual care. The primary endpoint is frailty prevalence at follow-up, defined by an Essential Frailty Toolset (EFT) score ≥3, with co-primary outcomes being between-group differences in the mean levels of EFT and Short Physical Performance Battery (SPPB) scores after 3–6 months. Secondary endpoints include mortality and hospitalization, among others. Results: The full protocol and study procedures are reported. Between May 2024 and December 2025, 589 patients were screened at the two Italian centers involved; 442 met eligibility criteria and 209 were enrolled and randomized. Baseline characteristics were largely comparable between groups. The mean age was 77 ± 9 years, 70% were male, and 55% had ACS. Lower-than-expected enrollment was mainly attributable to refusal related to difficulties in using digital devices. Conclusions: This randomized trial will evaluate whether a multidomain, eHealth-based CR intervention can reduce the prevalence or degree of frailty in older people after AHF or ACS. We report the study protocol and baseline characteristics of the enrolled cohort, highlighting the challenge of digital illiteracy in contemporary older populations. Full article

With the widespread adoption of electric vehicles, predicting user charging consumption can enhance the operational efficiency of charging infrastructure. However, differences in user charging habits result in charging station operators obtaining data that is non-continuous and event-driven, lacking internal battery state information. This makes traditional methods difficult to apply directly. This paper explores how to accurately predict user charging consumption based on non-continuous observation data from charging stations. To this end, we propose a three-stage solution: (1) Design a method for segmenting the temporal sequence of users’ internal charging behavior based on statistical significance testing, enabling unsupervised recognition of homogeneous sequences of user behavior patterns; (2) establish a continuous-time reconstruction mechanism based on a physics-inspired power decay model to convert discrete homogenous sequences into equidistant daily sequences of charging consumption; (3) utilize seasonal and trend decomposition using Loess (STL) time-series decomposition to extract the component from the reconstructed sequence and input it as a feature into the Long Short-Term Memory (LSTM) prediction model. Through experimental validation using real charging data, the proposed method significantly enhances prediction performance, providing an effective solution for forecasting user charging consumption in actual charging stations. Full article

Background: Population aging represents a major public health challenge, accompanied by an increasing prevalence of chronic diseases and age-related functional decline. Declines in lower-extremity physical function are particularly important, as they are strongly associated with mobility limitations, loss of independence, increased risk of falls, hospitalization, and mortality in older adults. Reliable and valid tools to assess physical performance are therefore essential in both clinical and research settings. The Short Physical Performance Battery (SPPB) is a widely used instrument for assessing lower-extremity physical performance in older adults and is recommended within the diagnostic algorithm of the European Working Group on Sarcopenia in Older People (EWGSOP2) for evaluating physical performance severity. However, the SPPB has not yet been psychometrically validated in the Croatian older population. This study aimed to evaluate the reliability and validity of the SPPB in Croatian older adults. Methods: This study examined the metric properties of the SPPB in a sample of 153 older adults recruited from nursing homes and community settings. Results: The SPPB demonstrated acceptable internal consistency (Cronbach’s alpha = 0.74) and good test–retest reliability (ICC = 0.893) for the total score. Convergent and construct validity were supported by significant associations with established measures of functional mobility and muscle strength. Conclusions: The Croatian version of the SPPB is a reliable and valid instrument for assessing lower-extremity physical performance in older adults. Its use is supported in clinical practice and research settings in Croatia. Further studies should examine responsiveness and predictive validity in nationally representative samples. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries is one of the core functions of a battery management system and is of great significance for ensuring the safe operation of electric vehicles and optimizing energy utilization. However, due to the strong nonlinearity, time-varying characteristics, and interference from complex operating conditions within the battery, high-precision SOC estimation faces severe challenges. To address the problems that a single data-driven method lacks physical constraints and a single model-driven method struggles to characterize complex nonlinearities, this paper proposes a series-connected LSTM-UKF fusion estimation method. This method first utilizes a Long Short-Term Memory network to learn the dynamic characteristics of the battery from historical voltage and current data, capturing the long-term dependencies of SOC changes to achieve an initial prediction. Subsequently, using this predicted value as the observation input, an Unscented Kalman Filter based on a second-order RC equivalent circuit model is introduced for optimal state correction, effectively suppressing model uncertainty and measurement noise. Simulation validation under various dynamic conditions, such as constant current discharge and FUDS, shows that compared to single LSTM or UKF algorithms, the proposed fusion method has significant advantages in estimation accuracy, convergence speed, and robustness. Its root mean square error is reduced to 0.0031, and it maintains stable estimation performance under different operating conditions. This study provides an effective data-model fusion solution for high-precision SOC estimation of lithium-ion batteries under complex operating conditions. Full article

Residential photovoltaic (PV)–battery systems are increasingly deployed to reduce electricity costs under time-of-use and demand-charge tariffs, yet their economic value depends critically on how storage is operated over time. Effective control must simultaneously address short-term energy costs, peak-demand exposure, and long-term battery degradation, all under substantial uncertainty in load and PV generation. While optimization-based approaches can achieve strong performance with accurate forecasts, they are sensitive to forecast errors, whereas learning-based methods often neglect degradation effects or deplete the battery prematurely, leading to suboptimal peak-shaving behavior. This paper proposes a forecast-free, degradation-aware reinforcement learning (RL) framework for residential PV–battery energy management that jointly addresses demand-charge mitigation and battery aging. The proposed controller internalizes both calendar aging and rainflow-based cycling degradation within its objective and incorporates demand-aware reward shaping with time-varying penalties on on-peak grid imports. In addition, a complementary state-of-charge reserve mechanism discourages premature battery depletion and improves responsiveness to late on-peak demand surges, despite the absence of explicit load or PV forecasts. Physical feasibility is guaranteed through an execution-time safety layer that enforces all device and operational constraints by construction. The proposed framework is evaluated on high-resolution residential datasets and compared against optimization-based baselines, including a day-ahead scheduler with perfect foresight and a receding-horizon MPC controller using short-horizon forecasts. Overall, the results show that the proposed RL controller substantially reduces demand charges and total electricity costs relative to forecast-based MPC while maintaining degradation-aware operation, demonstrating the potential of forecast-free reinforcement learning as a practical control strategy for residential PV–battery systems under demand-charge tariffs. Full article

Background/Objectives: Walking impairment and fatigue are common in multiple sclerosis (MS) and contribute to reduced physical function and quality of life (QoL). This study evaluated the feasibility, safety, and pre–post changes associated with a 12-week treadmill walking training (TWT) programme on walking performance, physical function, fatigue, and QoL in people with MS. Methods: Single-arm pilot study with pre–post assessments (T1–T2). Eleven adults with MS (Expanded Disability Status Scale [EDSS] ≤ 6) completed supervised TWT for 12 weeks (two 25 min sessions/week) at the Complejo Asistencial Universitario de Soria (Spain). Outcomes included SF-36, Timed Up and Go (TUG), 4 m gait speed, Short Physical Performance Battery (SPPB), and Modified Fatigue Impact Scale (MFIS). Within-participant changes were analysed using paired t-tests or Wilcoxon signed-rank tests as appropriate; effect sizes were reported as appropriate for the statistical test. Results: SF-36 total score did not change significantly (p = 0.160), while general health (p = 0.039) and vitality (p = 0.043) improved. Walking performance improved (TUG, p = 0.007; 4 m gait speed, p < 0.001), and physical function increased (SPPB, p = 0.003). Fatigue impact decreased (MFIS total, p = 0.015; physical, p = 0.007; psychosocial, p = 0.026), whereas the cognitive subscale did not change significantly (p = 0.094). Adherence was 91.7%, and no adverse events were reported. Conclusions: In this pilot sample, a 12-week TWT programme was feasible and safe and was associated with improvements in walking performance, physical function, and fatigue, with QoL changes limited to specific SF-36 domains. These findings support proceeding to a randomised controlled trial to establish efficacy. These findings should be interpreted as preliminary and exploratory, given the single-arm pre–post study design. Full article

This research focuses on the passive behavior changes of 3 Ah pouch LiFePO₄ (LFP) batteries during low-temperature storage, a point often neglected in previous studies. This experiment examines the low-temperature non-operational endurance of fully charged batteries (FCB) at 25 °C, −10 °C, and −35 °C. Battery performance reliability under these conditions is evaluated through capacity retention and internal resistance (IR) analysis. Microstructural changes on the surfaces of thawed battery electrodes are acquired using scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. After seven freeze–thaw cycles, the maximum usable capacity is marginally affected. Notably, a pronounced increase in polarization resistance (R_p) has been observed, particularly at −10 °C conditions, with an increase of about 40.57 mΩ. Microstructural analyses reveal that low-temperature storage significantly led to cracking of the electrolyte layer and of the particles in the anode material. Subsequently, at room temperature (RT, 25 °C), external short circuit (ESC) tests were performed on thawed batteries. At 50C, the peak temperatures recorded at the center of the FCB₋₁₀, FCB₂₅, and FCB₋₃₅ batteries are 104.35 °C, 94.67 °C, and 90.56 °C, respectively. The batteries exhibit rupture at approximately 47 s, 60 s, and 70 s during the ESC process. The results show that battery FCB₋₃₅ exhibits a slower temperature rise and delayed physical damage during ESC. Full article

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article