Search Results (772)

New wearable technology opens new possibilities for low-cost, easily accessible home-based interventions as a supplement to typical clinical rehabilitation therapy. In this pilot study, we tested a new interactive adjustable Feedback training system on 14 infants at high risk of cerebral palsy between 2 and 12 months of age to facilitate increased movements. The system consists of four wireless motion sensors placed on the infant’s limbs. Inertial sensors track the infant’s movements which control auditory and visual stimuli that act as motivational feedback. A 15 min usage of the Feedback training system four days a week for approximately six months was aimed for. None of the participants reached the recommended amount of intervention, due to time limitations. Seven of the twelve participating infants (58%) achieved at least 50% of the recommended training amount. Parents found the Feedback training system easy to use with minimal need for technical assistance. Preliminary data suggest that infants engaged more actively during training sessions where their movements actively controlled the presentation of the stimuli. The Feedback training system is promising as a user-friendly add-on to the playful and interactive stimulation of motor and cognitive development in infants with neurodevelopmental disorders. Full article

Background: We explored a new approach for increasing participation in daily life for individuals with severe movement impairments. The core of the approach is an application designed to Translate Intentions into Action (TIA) as a motivational tool for both leisure and clinical training sessions. Methods: The TIA application was developed to enable users to activate motivational feedback, like sounds, music, or videos, through movement measured with an IMU (Inertial Measurement Unit). IMUs were calibrated to user-specific thresholds based on individual movement potential. TIA was tested in two different age groups to assess applicability throughout lifespan and across different motor capacities. Results: The results indicated that TIA can be used for improved participation when positive feedback is provided during the intervention sessions. Observations demonstrated that regardless of age and motor capabilities, increased participation was achieved. TIA demonstrated the far-reaching potential to enhance the engagement and motivation of individuals with different levels of severe disabilities. Conclusions: By providing personalized, positive feedback through movement-activated outputs, TIA can be used by a wide range of people, with or without motor disabilities, to control digital outputs, such as video and audio. These findings suggest that TIA can be a valuable tool in both clinical and leisure settings to promote meaningful participation in activities. Full article

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

Analyzing the dynamics of muscle activation patterns and joint range of motion is essential to understanding human movement during complex tasks such as tooth brushing Activities of Daily Living (ADLs). In individuals with neuromotor impairments, accurate assessment of upper limb motor patterns plays a critical role in rehabilitation, supporting the identification of compensatory strategies and informing clinical interventions. This study presents the validation of a previously developed novel, low-cost, wearable, and portable multimodal prototype that integrates inertial measurement units (IMU) and surface electromyography (sEMG) sensors into a single device. The system enables bilateral monitoring of arm segment kinematics and muscle activation amplitudes from six major agonist muscles during ADLs. Eleven healthy participants performed a functional task, tooth brushing, while wearing the prototype. The recorded data were compared with two established gold-standard systems, Qualisys^® motion capture system and Biosignalsplux^®, for validation of kinematic and electrophysiological measurements, respectively. This study provides technical insights into the device’s architecture. The developed system demonstrates potential for clinical and research applications, particularly for monitoring upper limb function and evaluating rehabilitation outcomes in populations with neurological disorders. Full article

The accurate prediction of human movement direction plays a crucial role in fields such as rehabilitation monitoring, sports science, and intelligent military systems. Based on plantar pressure and inertial sensor data, this study developed a hybrid deep learning model integrating a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network to enable joint spatiotemporal feature learning. Systematic comparative experiments involving four distinct deep learning models—CNN, BiLSTM, CNN-LSTM, and CNN-BiLSTM—were conducted to evaluate their convergence performance and prediction accuracy comprehensively. Results show that the CNN-BiLSTM model outperforms the other three models, achieving the lowest RMSE (0.26) and MAE (0.14) on the test set, with an R² of 0.86, which indicates superior fitting accuracy and generalization ability. The superior performance of the CNN-BiLSTM model is attributed to its ability to effectively capture local spatial features via CNN and model bidirectional temporal dependencies via BiLSTM, thus demonstrating strong adaptability for complex motion scenarios. This work focuses on the optimization and comparison of deep learning algorithms for spatiotemporal feature extraction, providing a reliable framework for real-time human motion prediction and offering potential applications in intelligent gait analysis, wearable monitoring, and adaptive human–machine interaction. Full article

Background: Greater trochanteric pain syndrome (GTPS) is associated with structural tendon alterations and functional impairment. Extracorporeal shockwave therapy (ESWT) is a common treatment, but objective monitoring of tendon remodeling and motor recovery remains limited. Objective: This study aimed to integrate shear-wave elastography (SWE) expressed in m/s and wearable inertial measurement unit (IMU) as biosensing tools for the quantitative assessment of tendon elasticity, morphology, and hip motion after ESWT in GTPS. Methods: In a prospective cohort of adults with chronic GTPS, shear wave elastography (SWE) quantified gluteus medius tendon (GMT) elasticity and thickness, while hip abduction range of motion (ROM) was measured using a triaxial inertial measurement unit. Clinical scores on the Visual Analogue Scale (VAS), Harris Hip Score (HHS), Low Extremity Functional Scale (LEFS), and Roles and Maudsley score (RM) were collected at baseline (T0) and at 6 months (T1). Results: Thirty-five patients completed follow-up. Pain and function improved significantly (VAS, HHS, LEFS, RM; all p < 0.05). SWE values of the affected GMT increased, while tendon thickness decreased yet remained greater than on the contralateral side. Hip abduction ROM increased significantly from T0 to T1 (p < 0.05). Correlation analysis showed a negative association between abduction and pain at T1 (r = −0.424; p = 0.011) and, at baseline, between abduction and VAS (r = −0.428; p = 0.010) and RM (r = −0.346; p = 0.042), and a positive association with LEFS (r = 0.366; p = 0.031). SWE correlated negatively with VAS at T1 (r = −0.600; p < 0.05) and positively with HHS at T1 (r = 0.400; p < 0.05). Conclusions: Integrating elastography with inertial sensor-based motion analysis provides complementary, quantitative insights into tendon remodeling and functional recovery after ESWT in GTPS. These findings support combined imaging and wearable motion measures to monitor treatment response over time. Full article

Gait recognition using wearable sensor data is crucial for healthcare, rehabilitation, and monitoring neurological and musculoskeletal disorders. This study proposes a deep learning framework for gait classification using inertial measurements from four body-mounted IMU sensors (head, lower back, and both feet). The data were collected from a publicly available, clinically annotated dataset comprising 1356 gait trials from 260 individuals with diverse pathologies. The framework, G-MASA-TCN (Gait Multi-Anchor, Space-Aware Temporal Convolutional Network), integrates multi-scale temporal fusion, graph-informed spatial modeling, and residual dilated convolutions to extract discriminative gait signatures. To ensure both high performance and interpretability, Integrated Gradients is incorporated as an explainable AI (XAI) method, providing sensor-level and temporal attributes that reveal the features driving model decisions. The framework is evaluated via repeated cross-validation experiments, reporting detailed metrics with cross-run statistical analysis (mean ± standard deviation) to assess robustness. Results show that G-MASA-TCN achieves 98% classification accuracy for neurological, orthopedic, and healthy cohorts, demonstrating superior stability and resilience compared to baseline architectures, including Gated Recurrent Unit (GRU), Transformer neural networks, and standard TCNs, and 98.4% accuracy in identifying individual subjects based on gait. Furthermore, the model offers clinically meaningful insights into which sensors and gait phases contribute most to its predictions. This work presents an accurate, interpretable, and reliable tool for gait pathology recognition, with potential for translation to real-world clinical settings. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensively examines the landscape of multi-modal wearable devices that simultaneously capture physiological data, such as electrodermal activity (EDA), electrocardiogram (ECG), heart rate variability (HRV), and body temperature, alongside environmental exposures, including air quality, ambient temperature, and atmospheric pressure. We analyze the fundamental sensing technologies, data fusion methodologies, and the critical importance of contextualizing physiological signals within an individual’s environment to disambiguate health states. A detailed survey of existing commercial and research-grade devices highlights a growing, yet still limited, integration of these domains. As a central case study, we present an integrated prototype, which exemplifies this approach by fusing data from inertial, environmental, and physiological sensors to generate intuitive, composite indices for stress, fitness, and comfort, visualized via a polar graph. Finally, we discuss the significant challenges and future directions for this field, including clinical validation, data security, and power management, underscoring the potential of convergent sensing to revolutionize personalized, predictive healthcare. Full article

Background: Benign Paroxysmal Positional Vertigo (BPPV) is the most common vestibular disorder. Although canalith repositioning procedures (CRPs) typically resolve positional vertigo, several patients still report imbalance or residual dizziness, which remain difficult to quantify with standard tests. Wearable inertial sensors now allow high-resolution, objective gait analysis and may detect subtle vestibular-related impairments. Objectives: This study evaluates the clinical usefulness of sensor-based gait metrics, enhanced by the newly developed φ-bonacci index framework to quantify gait changes and residual dizziness in BPPV before and after CRPs. Methods: Fifteen BPPV patients (BPPV-P) and fifteen age-matched controls completed walking tests under eyes-open (EO) and eyes-closed (EC) conditions using wearable inertial measurement units (IMU). φ-bonacci index components—self-similarity (A1), swing symmetry (A2), and double-support consistency (A4)—were calculated to assess gait harmonicity, symmetry and stability. Results: Before treatment, BPPV-P exhibited significantly higher A1 values than healthy controls (p = 0.038 EO; p = 0.011 EC), indicating impaired gait harmonicity. After CRPs, A1 values normalized to control levels, suggesting restored gait self-similarity. Under visual deprivation, both A1 and A4 showed pronounced increases across all groups, reflecting the contribution of vision to balance control. Among post-treatment patients, those reporting residual dizziness demonstrated persistently elevated A4 values—particularly under EC conditions—indicating incomplete sensory reweighting despite clinical recovery. Conclusions: Wearable sensor–derived φ-bonacci metrics offer sensitive, objective markers of gait abnormalities and residual dizziness in BPPV, supporting their use as digital biomarkers for diagnosis, rehabilitation, and follow-up. Full article

Efficient monitoring of lower-limb coordination is important for understanding movement characteristics during off-ice speed-skating training. This study aimed to develop an analytical framework to characterize the kinematic–kinetic coupling of the lower limbs during slideboard skating tasks using wearable sensors. Eight national-level junior speed skaters performed standardized simulated skating movements on a slideboard while wearing sixteen six-axis inertial measurement units (IMUs) and Pedar-X in-shoe plantar-pressure insoles. Joint-angle trajectories and plantar-pressure signals were temporally synchronized and preprocessed using a Kalman-based multimodal state-estimation approach. Third-order polynomial regression models were applied to examine the nonlinear relationships between hip–knee joint angles and plantar loading across four distinct movement phases. The results demonstrated consistent coupling patterns between angular displacement and peak plantar pressure across phases (R² = 0.72–0.84, p < 0.01), indicating coordinated behavior between joint kinematics and plantar kinetics during simulated skating movements. These findings demonstrate the feasibility of a Kalman-based joint analysis framework for fine-grained assessment of lower-limb coordination in slideboard speed-skating training and provide a methodological basis for future investigations using wearable sensor systems. Full article

(1) Background: Navigating surfaces during walking can alter gait patterns. This study aims to develop tools for automatic walking condition classification using inertial measurement unit (IMU) and foot pressure sensors. We compared sensor modalities (IMUs on lower-limbs, IMUs on feet, IMUs on the pelvis, pressure insoles, and IMUs on the feet or pelvis combined with pressure insoles) and evaluated whether gait cycle segmentation improves performance compared to a sliding window. (2) Methods: Twenty participants performed flat, stairs up, stairs down, slope up, and slope down walking trials while fitted with IMUs and pressure insoles. Machine learning (ML; Extreme Gradient Boosting) and deep learning (DL; Convolutional Neural Network + Long Short-Term Memory) models were trained to classify these conditions. (3) Results: Overall, a DL model using lower-limb IMUs processed with gait segmentation performed the best ($F1=0.89$). Models trained with IMUs outperformed those trained on pressure insoles ($p<0.01$). Combining sensor modalities and gait segmentation improved performance for ML models ($p<0.01$). The best minimal model was a DL model trained on IMU pelvis + pressure insole data using sliding window segmentation ($F1=0.83$). (4) Conclusions: IMUs provide the most discriminative features for automatic walking condition classification. Combining sensor modalities may be helpful for some model architectures. DL models perform well without gait segmentation, making them independent of gait event identification algorithms. Full article

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics. Full article

Walking ability is crucial for maintaining independence and healthy aging. Although joint angle measurement is important for detailed gait assessment, it is rarely performed in clinical practice due to the complexity of motion capture systems. This study investigates individual variability and cross-population generalizability of deep learning-based joint angle estimation from a single inertial measurement unit (IMU) attached to the pelvis. Gait data from three distinct populations were collected: 17 young adults, 20 healthy older adults (aged 65+), and 14 pre-operative patients scheduled for hip replacement surgery due to hip osteoarthritis (also aged 65+). A 1D ResNet-based convolutional neural network was trained to estimate bilateral hip, knee, and ankle joint angles from IMU signals. We systematically compared within-population training (trained and tested on the same population) with cross-population training (trained on combined data from all populations) using nested 5-fold cross-validation. Cross-population training showed population-specific effectiveness: older adults demonstrated consistent improvement, while young adults showed minimal change due to already high baseline performance, and pre-operative patients exhibited highly variable responses. These findings suggest that the effectiveness of cross-population learning depends on within-population gait heterogeneity, with important implications for developing clinically applicable gait analysis systems across diverse patient populations. Full article

Population aging has intensified the prevalence of falls among older adults, making automatic Fall Detection Systems (FDS) a key component of telemonitoring and remote care. Among wearable-based approaches, inertial sensors, particularly accelerometers, offer an effective and low-cost alternative for continuous monitoring. However, the impact of the selection of the sampling frequency on model performance remains insufficiently explored. This work seeks to determine the sampling rate that best balances accuracy, stability, and computational efficiency in wearable FDS. Five representative algorithms (CNN-LSTM, CNN, LSTM-BN, k-NN, and SVM) were trained and evaluated using the SisFall dataset at 10, 20, 50, and 100 Hz, followed by a multi-stage validation including the real-fall repositories FARSEEING and Free From Falls, as well as a seven-day continuous monitoring test under real-life conditions. The results show that deep learning architectures consistently outperform traditional classifiers, with the CNN-LSTM model at 20 Hz achieving the best balance of accuracy (98.9%), sensitivity (96.7%), and specificity (99.6%), while maintaining stable performance across all validations. The observed consistency indicates that intermediate frequencies, around 20 Hz and down to 10 Hz, provide sufficient temporal resolution to capture fall dynamics while reducing data volume, which translates into more efficient energy usage compared to higher sampling rates. Overall, these findings establish a solid empirical foundation for designing next-generation wearable fall-detection systems that are more autonomous, robust, and sustainable in long-term IoT-based monitoring environments. Full article