IEEE Transactions on Biomedical Engineering

Instrumented gait assessment in pediatric populations is often constrained by the complexity and lack of portability of traditional motion capture systems. In this article, we propose a deep learning approach utilizing a one-dimensional (1D) U-Net architecture to accurately estimate ankle and knee joint angles in the sagittal plane from surface electromyography (sEMG) signals. We analyzed data from the tibialis anterior and medial gastrocnemius of 25 typically developing children (ages 4-16) to evaluate the models performance and the influence of age-related gait maturation. The proposed 1D U-Net achieved high predictive accuracy for the ankle joint (RMSE: 3.6{degrees}) and the knee joint (RMSE: 4.1{circ}). Experimental results demonstrated that incorporating the toe-off event as a temporal marker significantly enhanced prediction stability during transitional gait phases. Furthermore, Statistical Parametric Mapping (SPM) was employed to identify systematic errors, which were primarily localized during initial contact and pre-swing but remained below clinically relevant thresholds. The findings reveal that prediction accuracy increases with age, reflecting more stable neuromotor patterns. This study demonstrates that a 1D U-Net can reliably decode lower-limb kinematics from sEMG alone, enabling the development of simplified, non-invasive, and portable pediatric gait assessment tools that can be integrated into the control strategies of assistive devices.

Optimizing assistive wearable devices is crucial for their efficacy and user adoption, yet state-of-the-art methods like Human-in-the-Loop Optimization (HILO) and biomechanical modeling face limitations. HILO is time-consuming and often restricted to optimizing control parameters, while inverse dynamics assumes invariant kinematics, which is unreliable for adaptive human-device interaction. Predictive simulation offers a powerful alternative, enabling computational exploration of design spaces. However, existing approaches often lack systematic optimization frameworks and rigorous validation against experimental data. To address this, we developed a Design Optimization Platform that integrates predictive simulations within a two-level optimization structure for personalizing assistive device design. This paper primarily validates the platforms predictive simulations against a publicly available dataset of the passive Biarticular Thigh Exosuit (BATEX), assessing its reliability. Our findings show that the model can sufficiently predict the kinematics and major muscle activations, except for the pelvis tilt and some biarticular muscles. The key finding is that successful identification of personalized optimal BATEX stiffness parameters needs acceptable prediction of metabolic cost trends, not their precise values. Our analysis further reveals that the models accuracy in predicting Vasti muscle activation in the baseline condition is a significant indicator of its success in predicting metabolic cost trends. This demonstrates that accurate prediction of performance trends is more important for effective simulation-based design optimization than perfect biomechanical accuracy, advancing targeted and efficient assistive device development.

As implantable brain-computer interfaces (iBCIs) for communication and movement transition from cutting-edge research to clinical practice, a standardized approach will be required to reliably plan neurosurgeries involving complex microelectrode arrays and other neural sensors. Here, through our BrainGate study experiences, we present a replicable methodology, using open-source tools, to create interactive, personalized, 3-dimensional, virtual and physical, functional mapping models to guide iBCI surgical planning and provide intra-operative imaging displays.

Blood pressure (BP) measurement is crucial for medical care, yet existing BP methods are either invasive, tethered, or suffer from low temporal resolution. Non-invasive continuous BP estimation thus remains a significant challenge. To address these challenges, this work presents a novel, non-invasive, multi-modal sensor designed for continuous blood pressure estimation using multiple biosignal modalities as feature inputs. From these input data, we extract cardiovascular timing intervals (e.g., pulse arrival time), which serve as key features for BP regression models, enabling continuous, non-invasive BP monitoring. We validate our algorithm with 16 healthy subjects using standard blood pressure cuff readings as ground truth. Our wearable, non-invasive multimodal and multinodal sensor array for integrated computation (MOSAIC) demonstrated promising performance and was able to predict systolic and diastolic BP across all study subjects with a MAE of 5.31 {+/-} 7.32 mmHg and 4.27 {+/-} 2.35 mmHg, respectively.

The electrocardiogram (ECG) is a critical tool in the diagnosis and monitoring of cardiovascular disease. Although traditional 12-lead ECGs offer comprehensive in-sights into the electrical activity of the heart, they typically require clinical settings and expert interpretation, which limits their accessibility. In contrast, smartwatch 1-lead ECGs can be recorded at home, allowing more frequent and rapid monitoring. This opens opportunities not only for early detection but also for enhancing patient autonomy. This study investigates whether 1-lead ECGs can provide information beyond heart rhythm, specifically whether they can be used to assess left ventricular function (LVF) using explainable deep learning models. Our findings show that LVF can be accurately predicted from 1-lead ECGs (AUC = 0.883), nearly matching the performance of 12-lead ECGs (AUC = 0.897). These results suggest that 1-lead ECGs, when combined with interpretable AI, could support broader clinical applications and empower patients, particularly in resource-limited or remote settings.

Wearable devices for gravity balancing have high potential for impact across domains, including neuromotor rehabilitation and occupational systems. Devices made from compliant mechanisms, optimized to achieve specific compensation moments at target joints, have proven effective, but thus far have solely been optimized towards gravity compensation and not other wearability criteria. In this work, we propose a multi-objective optimization framework, based on particle swarm optimization, to design a soft, gravity balancing shoulder orthosis, while taking into account wearability constraints such as undesired loading directions and device size. Using this custom framework, we pursued multiple stages of orthosis design and optimization, selecting multiple solutions to be translated to real-world prototypes. These solutions were realized via 3D printing with thermoplastic polyurethane and evaluated for mechanical performance on benchtop and in-vivo. In-vivo testing on 6 healthy individuals demonstrated relative reductions in muscle activity for the anterior deltoid and upper trapezius, by 53 % and 71 % respectively when operating the orthosis for static tasks within functional shoulder ranges of motion. Changes in muscle activation were also were observed across other muscles, including the posterior deltoid, as well as in dynamic tasks at different speeds.

One-third of the worlds 70 million people with epilepsy have seizures that are not controlled by medication; and implantable devices are an exciting option for treatment. These devices improve seizure control and can detect impending attacks, missed medication, and impaired cognition. Unfortunately, they have no way to share this information with their hosts in real-time - a limitation common to most medical devices. This is a missed opportunity for implants and wearables to learn from patients, focus on what matters most to them, and teach them how their behavior affects their health. Here, we present a device platform that converses with patients and learns to co-manage epilepsy. The inpatient pro-totype links scalp and intracranial EEG (electroencephalograms) to secure large language models that communicate freely and bidirectionally with their hosts through a smartphone app. An AI agent ingests biomarkers of sleep, medication level, cognition, and seizure risk extracted from brain activity. It con-verses with patients to inform them of clinical events and physiological trends, records their symptoms, responses, and behaviors, and automatically retrains itself to improve performance. Both patients and the AI agent can initiate conversations to teach each other and personalize interactions. We demon-strate this platform in 13 patients undergoing inpatient video-EEG monitoring for epilepsy and validate its performance. Algorithms for detecting seizures optimized their precision over several days without expert intervention - in contrast to the months of iterative, in-person physician programming currently required. Patients responded positively to messages regarding sleep, cognition, and seizure risk while rating the system as highly usable. The platform includes several safeguards, including a system for further algorithm fine-tuning using efficient expert review, and features that ensure data security and regulate communication content. Further work will link other biosensors to measure behavior, improve performance, and optimize therapeutic stimulation. We propose this system as a scalable platform for medical devices that can rapidly adapt to patient and provider needs; one that is broadly adaptable to improving care for many medical conditions.

Reliable interpretation of electrocardiograms (ECGs) requires precise identification of P, QRS, and T (PQRST) wave boundaries. However, it remains challenging due to noise, signal quality variability, and inherent morphological diversity particularly in recordings from children. This study systematically compares the performance of leading deep neural networks (DNN) and heuristic-based delineation algorithms on ambulatory single-lead ECG signals focusing on temporal accuracy. Experiments were conducted using the publicly available LUDB dataset and a private validation dataset comprising 21,759 annotated single-lead wave segments from 611 children recorded using KardiaMobile ECG sensor. DNN were first trained on the LUDB dataset and subsequently tested on the validation dataset. The delineation performance was assessed using Sensitivity (Se) and positive-predictive-value (P+) metrics. The best-performing heuristic based and DNN models reached Se and P+ of (98.9% vs 97.9%) for P, (99.8% vs 99.2%) for QRS, and (98.7% vs 95.9%) for T wave fiducials, respectively. The lowest standard-deviation (in ms) of wave onset/offset delineation was achieved by attention based 1DU-Net model; {+/-}16.6/{+/-}16.3 for P-wave, {+/-}14.0/{+/-}16.3 for QRS, and {+/-}26.3/{+/-}18.8 for T-wave, respectively. The findings indicate that optimized heuristic models can perform comparably to complex DNN, highlighting their efficiency and suitability for real-time ECG delineation in digital health monitoring applications.

The kinematics of rhythmic, speed-modulated finger and grasp-like movements were analyzed using a reduced biomechanical model of the hand and a marker-based optical motion-capture system. Twenty-one healthy participants performed eight hand motor tasks involving metacarpophalangeal (MCP) joint flexion-extension (F-E) and carpometacarpal (CMC) thumb opposition-reposition (O-R) at two movement frequencies (0.50 and 0.75 Hz). Kinematic analysis quantified the range of movement (RoM), mean speed, and normalized total harmonic distortion (TDHN). Statistical analysis identified task type as the primary factor modulating all three metrics across digits, with large effect sizes [Formula]. Movement frequency significantly influenced mean speed [Formula] and moderately affected TDHN [Formula], while thumb RoM remained statistically unchanged across frequencies (p = 0.063). Participants consistently reproduced the intended sinusoidal trajectories, as indicated by low TDHN values (below 19%). The findings support the analysis of coordinated hand movements across various tasks under controlled time conditions. They also demonstrate that the simplified biomechanical model accurately captured both individual and co-ordinated finger movements. This provides a valuable reference for studies on motor control and for applications in rehabilitation and assistive technology.

After amputation, advanced prosthetic limbs offer a promising means of restoring motor function. However, state-of-the-art prostheses often rely on aggregate electromyogram (EMG) signals to decode motor intention, which limits their ability to replicate natural limb movements. Decomposing EMG signals into individual motor unit components has shown potential for more natural control, but distinguishing between individual units can be challenging when nearby signals overlap. This study demonstrates that muscle target reinnervation surgeries can naturally increase physical separation between motor unit signals, thereby mitigating this overlap. Reinnervation of individual motor units is evaluated in a rodent hindlimb model after direct nerve-to-muscle implantation. Histological and electrophysiological analyses reveal that structural changes following reinnervation surgery result in beneficial motor unit signal changes, particularly improving spatial separation between motor unit signals compared to those in intact muscle. This spatial separation contributed to fewer instances of complex, overlapping signals in reinnervated muscle recordings. Motor unit signals were leveraged to provide a proof-of-concept of precise control of a virtual prosthesis for the first time after direct nerve-to-muscle implantation surgery. These findings highlight the potential of reinnervated muscle targets as key biological interfaces that facilitate motor unit separation, reducing the burden on decomposition algorithms and improving prosthetic control.

Restoring communication for people with dysarthria secondary to pontine stroke remains a critical challenge. Intracortical brain-computer interfaces (iBCIs) have demonstrated great potential for speech restoration in people with amyotrophic lateral sclerosis (ALS), with 1-24% word error rates (WERs) on a 125,000-word vocabulary. In pontine stroke, electrocorticography (ECoG) BCIs achieved 25.5% WERs with a smaller 1,024-word vocabulary. Whether intracortical BCI performance improvements extend to people with pontine stroke-induced dysarthria remains unclear. Here, we show that neural activity from a single 64-channel microelectrode array in orofacial motor cortex can predict attempted speech in a person with pontine stroke more accurately than prior ECoG BCI work and comparably to prior iBCI work. We trained a neural network decoder to predict phoneme probabilities from spiking rates and spike-band power as BrainGate2 participant T16 mimed (mouthed without vocalization) sentences from a large vocabulary. A series of language models converted these probabilities into word sequences. This decoding architecture has remained stable more than two years post-implantation, achieving a median 19.6% WER with a 125,000-word vocabulary and a median 10.0% WER with a 1,024-word vocabulary (a 60.8% reduction over prior ECoG studies). This framework also generalized beyond cue repetition, enabling T16 to communicate spontaneously via the iBCI in a question-and-answer setting with a 35.2% WER. These results demonstrate that brain-to-text decoding from a small patch of cortex can outperform ECoG-based systems in individuals with pontine stroke and is comparable to early speech iBCIs in individuals with ALS.

Vascular waveforms, which measure bulk flow in blood vessels, are widely used to measure vital signs, diagnose conditions, and predict long-term health outcomes. Analyzing vascular waveforms depends on three fundamentally interdependent tasks: signal filtering, pulse timing detection, and pulse shape extraction. We hypothesized that Bayesian pulse deconvolution can achieve improved performance on all three tasks by solving them jointly. This method uses an analytical, generative model of vascular waveforms with priors informed by physical and biological domain knowledge. In simulations, Bayesian pulse deconvolution achieves better performance on all tasks compared with existing algorithms: 90% reduction of median filtering error, 60% reduction in pulse timing error, and 85% reduction in shape extraction error. The advantages in simulations extend to human recordings of photoplethysmography waveforms. Taking real time-synchronized electrocardiogram R-R intervals as a proxy ground truth, Bayesian pulse deconvolution achieves 40% lower pulse interval estimation error (RMSE = 5.1 ms) compared with typical algorithms (RMSE = 8.3 ms, p=1e-10). By extracting more accurate and informative insights from vascular waveforms, Bayesian pulse deconvolution could advance a wide array of health technologies that rely on interpreting signals from blood vessels.

Myocaridum biomechanics are a biomarker for multiple cardiac pathologies. However the rapid and complex heart motion hampers accurate measurements of the tissue stiffness. Current in vivo methods for the evaluation of myocardium mechanical health are either highly invasive or can only provide with a global surrogate of heart function as they suffer from poor spatiotemporal resolution. We propose a new in vivo technique, transient magnetic resonance elastography (tMRE), to assess the dynamic cardiac biomechanics. tMRE is able to quantify local shear wave speed as a proxy for myocardial stiffness at user-defined times within the cardiac cycle. We report proof-of-concept results where we probe the septum of 4 different healthy rat specimens at 3 physiologically distinct cardiac phases. We provide with apparent speed measurements for early systole, mid-late systole and early diastole that match the expected values from the cardiac cycle physiological mechanics. We correct for non-negligible geometrical biases using literature results and report true stiffness values where possible. Finally, we validate tMRE in phantom experiments.

Affordable home-based electroencephalography (EEG) headsets could widen access to EEG assessment, but require rigorous validation before research or clinical use. Here, we evaluated a custom-developed 2-channel sensorimotor headset (PainWaive) intended for remote neuro-feedback and longitudinal monitoring in chronic pain. Eighty participants (47 female; mean age 24.0 years, SD 7.9) completed two resting-state sessions with PainWaive and a research-grade 64-channel EEG system (LiveAmp), under eyes-open (EO) and eyes-closed (EC) conditions. Alpha, beta and theta power and peak alpha frequency (PAF) were derived from homologous sensorimotor channels (C1/C2). Relative reliability was quantified with intraclass correlation coefficients (ICCs), absolute reliability with SEM%, and cross-device consistency with between-device ICCs and Pearson correlations of overall spectral shape. ICCs/correlations were interpreted using pre-specified thresholds: fair 0.20-0.39, moderate 0.40-0.59, good 0.60-0.79, excellent [≥]0.80. PainWaive and LiveAmp showed comparable absolute reliability across metrics (similar SEM%). Under EC, PainWaive reliability was excellent for alpha (0.81), theta (0.85) and PAF (0.94), and good for beta (0.72). Under EO, reliability was excellent for alpha (0.82), good for beta and PAF (0.61-0.72), and moderate for theta (0.59). Spectral-shape correlations between devices were excellent (r>0.90). Cross-device ICCs were good under EC for alpha/theta/PAF (ICC=0.66-0.77) though fair for beta (0.35). Under EO, ICCs were good for alpha (0.62), moderate for PAF (0.53), and fair for beta/theta (0.26-0.32). To assess performance under real-world use, we additionally analysed 2 clinical samples of individuals (total n = 8) with chronic pain who each completed 20 home-based neurofeedback sessions using PainWaive (160 sessions total). Within-session stability was good-to-excellent across metrics (ICCs>0.72). Overall, our findings suggest PainWaive is a reliable tool for the assessment of EEG metrics, supporting its use in research and clinical applications.

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional Variational Autoencoder (cVAE), a class-conditional latent Denoising Diffusion Probabilistic Model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits to augment minority arrhythmia classes in the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over five independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification and motivate further investigation of quantum-classical hybrid methods in cardiac diagnostics.

Chest radiography (CXR) is a primary modality for assessing cardiopulmonary conditions, but its effectiveness is limited by anatomical obstructions (e.g., ribs, clavicles) that hinder accurate pneumothorax segmentation, boundary delineation, and severity estimation. While deep learning-based bone suppression improves soft-tissue visibility, its utility for precise pixel-wise localization remains underexplored. This study investigates the downstream application of bone suppression for pneumothorax segmentation, integrating it as a preprocessing step to mitigate bony obscuration. We evaluate its impact across CNN and Vision Transformer models on two public datasets, where models trained on bone-suppressed CXRs significantly outperform (p < 0.05) non-suppressed counterparts, achieving up to 17% improvement in Mean Average Surface Distance (MASD), 4.9% in Dice Similarity Coefficient (DSC), and 5.9% in Normalized Surface Dice (NSD), alongside a 9.5% gain in Matthews Correlation Coefficient (MCC). These results demonstrate bone suppression as an architecture-independent enhancement for pneumothorax localization, improving the reliability of automated CXR interpretation.

Head rotation is the leading cause of diffuse brain injuries from cycling accidents, with severe, long-term or even fatal consequences. Here, we present a novel helmet safety technology, the Release Layer System (RLS), designed to enhance conventional helmets and reduce the likelihood of such injuries. RLS is located on the outer side of the helmet and thus gets impacted first. The force of the impact activates a rolling mechanism triggering the release of an outer polycarbonate panel, thereby dispersing and transforming a substantial portion of the incident rotational energy. To evaluate the effectiveness of the technology, we conducted oblique impact tests on three popular helmet types, in conventional and RLS-equipped configurations, at three impact locations. RLS-equipped helmets reduced Peak Angular Velocity (PAV) by 57-66%, averaged across impact locations, compared to their conventional counterparts. This corresponds to a 68-86% reduction in the probability of an AIS2+ brain injury, as estimated by the Brain Injury Criterion. The most notable improvement was observed at the pYrot location (front impacts, mid-sagittal plane), with up to 85% PAV reduction. Testing across headforms further demonstrated the effectiveness of the technology in mitigating head rotation irrespective of variations in evaluation setups. This work introduces a novel mechanism for rotational impact mitigation and provides evidence of its potential benefits compared with conventional helmets. As an outer-layer approach, RLS may offer an alternative pathway for managing rotational kinematics in future helmet designs.

Beam shaping of ultra-short pulses is essential for medical ultrasound, where single-cycle excitations are required to achieve high axial resolution and improve frame rate. Conventional methods, such as the Gerchberg-Saxton (GS) algorithm or more recent deep learning approaches, are generally effective for continuous-wave excitation but degrade significantly under single-cycle conditions. In diagnostic imaging, high frame rate is critical for applications demanding rapid scanning. In this context, multi-line transmission (MLT) leverages beam shaping to synthesize multiple simultaneous foci, thereby increasing frame rate. In parallel, structured illumination methods for super-resolution and acoustical holography likewise depend on actively shaping single-cycle pulses to produce controlled patterns, highlighting the need for precise short-pulse beam shaping. To address this challenge, we introduce the spatio-temporal adaptive reconstruction (STAR) algorithm, which performs active beam shaping directly in the time domain by integrating the generalized angular spectrum method (GASM) into an iterative optimization scheme. STAR enforces constraints on both the transducer and focal planes, enabling accurate control of single-cycle excitations. Simulations showed that STAR consistently outperformed GS for multi-focus patterns. For example, in a four-foci configuration, STAR achieved a correlation of 0.80 compared to 0.64 for GS, with significantly improved uniformity across focal peaks. Resolution analysis demonstrated that STAR reduced the minimum distinguishable foci spacing from 1.09 mm with GS to 0.87 mm. Experimental hydrophone measurements confirmed these improvements. Across multi-foci patterns, STAR produced more distinct and balanced foci compared to those observed with GS. These results demonstrate that STAR provides robust and efficient active beam shaping of single-cycle pulses, maintaining accuracy across different depths and frequencies for diagnostic applications.

Accurate diagnosis of cardiac abnormalities from electrocardiogram signals remains a central challenge in automated cardiovascular assessment. This study investigates the efficiency of time-frequency representations and deep learning architectures in classifying 12-lead ECGs into five diagnostic super-classes using the PTB-XL dataset. Continuous Wavelet Transform is applied to generate time- frequency representations, scalograms and phasograms, representing spectral energy and phase distributions, respectively. We experiment with both early and late information fusion strategies using several convolutional and transformer-based networks of a custom Convolutional Neural Network, Hybrid Deep Learning, transfer learning, feature fusion, and ensemble modeling, and weighted loss strategies. An ensemble fusion of models trained on time-frequency representation and time representation achieved the best overall performance of Area Under Curve of 0.9233 surpassing individual modalities. To improve the results further, weighted focal loss is used to improve the low classification rates in some labels due to imbalanced data. The results highlight the potential of multi-representation wavelet fusion for interpretable and generalizable ECG classification.

Freezing of gait (FoG) prediction is clinically meaningful only when warnings arrive sufficiently early for subsequent action. Therefore, we adopt a Freezing Prediction Horizon (FPH) evaluation that reports prediction performance as a function of the warning horizon before onset, making the lead-time versus reliability trade-off explicit. Within this protocol, we develop a Transformer-based predictor with a progressive self-paced learning strategy and evaluate it on a 55-patient clinical dataset and two public datasets. The horizon-performance curves show that Macro-F1 remains stable up to approximately 2.5 seconds before FoG onset in our dataset, after which a gradual decline is observed. This horizon-based characterization replaces single, fixed ahead-of-onset windows with a continuous method that summarizes achievable advanced time at specified accuracy levels. In this way, it offers a principled basis for setting targets in real-time implementationslinking algorithmic early-warning capacity to the lead times that practical systems may require-while remaining compatible with conventional metrics. By centering evaluation on FPH, this study clarifies how far in advance FoG can be predicted with confidence, and it positions horizon-based assessment as a reproducible complement to standard reporting for future work on deployable FoG prediction. Ultimately, quantifying advance warning is a prerequisite for prevention-oriented use, by indicating whether sufficient time can be reserved for cueing prior to onset.