Objective: To determine whether a two-lead ECG (wECG), acquired using a self-applicable, wrist-worn device, provides diagnostic value in acute myocardial infarction (AMI) detection. Methods: Seventy-one patients with AMI, 26 with other cardiovascular diseases (CVDs), and 54 healthy controls were enrolled in the study. To acquire lead I, one electrode of the wrist-worn device was touched with the index finger of the opposite hand, while another lead was acquired by touching the electrode on the strap to a specific part of the body. The diagnostic value of the wECG is evaluated either directly or through synthesis of the standard 12-lead ECG using an echo state network. Results: Using the standard ECG, an AMI detector based on a convolutional neural network yields a sensitivity of 0.86 and a specificity of 0.71; the corresponding figures for blinded cardiologist diagnosis are 0.77/0.88. Using the synthesized ECG, the V3 electrode touch site yields the best performance, with a sensitivity/specificity of 0.84/0.71 for AMI detection and 0.67/0.86 for cardiologist diagnosis. Compared to the synthesized ECG across all touch sites, use of the wECG results in a substantially lower sensitivity, reduced by 0.12–0.13, while the specificity is comparable or slightly higher. Implementation of a two-stage approach, consisting of detector-based screening followed by cardiologist review of positive cases, results in correct decisions in 63% of the AMI cases using either the synthesized ECG or the wECG. Conclusion: The two-lead ECG acquired with a wrist-worn device offers diagnostic value and enables screening for AMI. Clinical and Translational Impact Statement: This work enables AMI detection using a two-lead ECG acquired with a wrist-worn device, either directly or through synthesis of the 12-lead ECG, supporting cardiovascular diagnostics in remote and resource-limited environments. (Category: Clinical Research).
Objective: Accurate oscillometry requires minimizing artifacts, particularly those from cheek vibrations, which introduce a parallel (shunt) impedance pathway that distorts measurements. Standard practice includes using a nose clip and manually supporting the cheeks to reduce this effect by stiffening the tissue. This study evaluated the performance of a novel, equipment-integrated cheek holder designed to stabilize the cheeks and improve measurement accuracy without manual support, by comparing it to the current gold-standard (operator-supported cheeks). Methods: Respiratory resistance (Rrs) and reactance (Xrs) at 5 Hz were measured using a handheld oscillometer (FIRST, Restech srl) with the cheek holder and with manual support, in randomized order. Each test was performed in triplicate. Agreement was assessed using Deming regression. Equivalence was defined as the 95% confidence interval (CI) of the regression line falling within the known short-term repeatability: 17% (Rrs) and 37% (Xrs) for adults; 27% and 40% for children, respectively. Results: 55 subjects (age 5 – 75 years; height 104 – 185 cm; 56% females) were included for a broad representation of facial characteristics, with lip length ranging from 36 to 67 mm and bigonial breadth from 80 to 155 mm. The bias between methods was not clinically significant for Rrs or Xrs: the 95% confidence intervals (CI) from the Deming regression fell within the expected short-term variability for all evaluated parameters, indicating consistency between the two methods. Conclusion: The novel cheek holder produced impedance measurements comparable to manual support, simplifying oscillometry and potentially improving its reproducibility.
Objective: To develop and evaluate a low-cost, skin-safe strain-sensing patch and real-time-capable algorithm for early detection of intravenous (IV) infiltration during continuous monitoring. Methods: A custom strain sensor composed of discrete piezoresistive elements was encased between two transparent medical dressings and mounted adjacent to the IV site. Data were collected during controlled IV infiltrations in juvenile pigs and during inpatient monitoring in pediatric patients without infiltrations. A strictly causal, event-based detection algorithm was implemented using features derived from the strain signal and its first difference. Performance was evaluated using event sensitivity, time-to-detection (TTD), and false-positive alerts per hour (FP/hr). Candidate thresholds were derived from the animal training-set evidence distribution. The operating point was selected using training-data sensitivity, detection time, and control false-alert burden, then fixed before held-out testing. Results: Across 16 animal recordings comprising 35 infiltration events, the algorithm detected 30 events (event sensitivity = 0.86) with a median TTD of 57 seconds at the selected operating threshold. Approximately 86.7% of detected events occurred within two minutes of infiltration onset. On the held-out recording subset, 8 of 9 infiltration events were detected, with a median TTD of 53.5 seconds. Evaluation on 117.8 hours of pediatric inpatient control data demonstrated a threshold-dependent false-positive alert burden, operating at approximately 1.2 alerts per hour at the selected operating threshold. Conclusion: A strain-sensing patch integrated with a real-time-capable, event-based detection algorithm enabled early IV infiltration detection at low infused volumes while maintaining a false-positive alert burden that is acceptable for early-stage clinical evaluation.
Simulation-based surgical training is essential in ophthalmology; however, existing eye models fail to accurately reproduce the anatomy and biomechanics of the posterior segment, thereby limiting their utility for advanced procedures. In this work, we developed Eye-3DP, ta layered multi-material 3D-printed posterior-segment phantom designed to reproduce the structure and the mechanical properties of the sclera, choroid, and retina. Unlike current synthetic or animal models, Eye-3DP provides a reproducible platform for practicing delicate steps crucial in subretinal drug delivery, supporting the formation of subretinal blebs, a delicate step crucial for training subretinal drug delivery. Mechanical testing confirmed a close similarity to biological tissues, and surgical validation with ophthalmic trainees demonstrated successful vitrectomy and subretinal injections. Eye-3DP represents a promising and ethically sustainable training platform for vitreoretinal surgery and subretinal drug delivery, potentially bridging a critical gap in ophthalmic education, although further validation is required to fully demonstrate its educational and clinical relevance.
Objective: Wearable sensing for capturing knee acoustic emissions (KAEs) can enable earlier detection and better management of juvenile idiopathic arthritis (JIA). In this paper, we expand on our previous work by validating the KneeMS wearable for individuals with JIA in a clinical setting against a benchtop system (Dytran). Methods and procedures: Acoustic features were recorded from the medial and lateral sides of the patellar tendon using both Dytran and KneeMS in 36 participants with JIA during flexion/extension (FE). We calculated the Spearman correlation coefficient (ρ) between the acoustic features of both devices Acoustic features were recorded from the medial and lateral sides of the patellar tendon using both Dytran and KneeMS in 36 participants with JIA during flexion/extension (FE). We calculated the Spearman correlation coefficient between the acoustic features of both devices and assessed severity trends and longitudinal tracking of JIA knees from a machine learning (ML) pipeline differentiating active from inactive JIA knees. Results: Of 36 extracted features, six had the highest ρ (medial/lateral): RMS (0.79/0.70), MFCC1 (0.79/0.72), spectral slope (0.78/0.68), energy (0.78/0.67), Hjorth activity (0.75/0.71), and spectral flux (0.74/0.69). Our ML pipeline demonstrated an area under the receiver operating characteristic curve (AUC-ROC) mean of 0.77 (p<0.05) and showed a significant (p<0.05) upward trend in active knee prediction probabilities P(A) with clinical severity using KneeMS. For follow-ups, KneeMS P(A) moved in the clinically expected direction for 4 of 5 status-changing knees (versus 1 of 5 for Dytran); the clinical JADAS (cJADAS) score, available for most knees, was consistent with this finding. Conclusion: The high correlation between the acoustic features of Dytran and KneeMS, a significant AUC-ROC, significant JIA severity trend, and prospective longitudinal tracking make KneeMS a viable alternative to Dytran. Clinical impact: This validation establishes the foundation of home-based longitudinal joint assessment for JIA. Clinical and Translational Impact Statement: Wearable acoustic monitoring could enable at-home tracking of JIA disease activity, supporting timely treatment.
Objective: This study presents MeRKeL, a portable wireless system designed for quantitative, reference-calibrated assessment of pinch and grip strength, and evaluates its technical validity and human feasibility under standardized laboratory conditions. Methods: The system consists of two compact and functionally aligned modules (pinch and grip dynamometer) sharing a unified electronic architecture and data acquisition pipeline. Both employ task-specific mechanical designs tailored to the distinct loading characteristics of pinch and grip actions. Reference-based calibration and bench validation were performed across the full operating ranges and across multiple units. Accuracy, agreement, and within-session repeatability were evaluated using root mean squared error (RMSE), Bland–Altman analysis, and intraclass correlation coefficient (ICC). Feasibility testing in 196 healthy adults examined associations between maximal strength and demographic, behavioral, and health-related variables. Results: Bench validation showed high agreement with the calibrated reference instrument (pinch: RMSE = 0.95 N, bias = −0.56 N; grip: RMSE = 1.86 N, bias = −1.10 N), and within-session repeatability was excellent (MDC95 pinch = 0.86 N; grip = 1.43 N). Experimental testing reproduced expected associations with key demographic and behavioral factors. Conclusion: The MeRKeL system enables accurate and repeatable, reference-calibrated wireless measurement of pinch and grip strength, establishing a robust technical foundation for subsequent clinical evaluation and future home-based neuromuscular monitoring studies.
Objective: Arteriovenous fistula (AVF) dysfunction is a major cause of morbidity in hemodialysis (HD) patients. Although conventional monitoring tools are clinically effective, they are often limited by cost, invasiveness, and operator dependence. This study proposes a cycle-aware, auscultation-based deep learning framework for objective and scalable assessment of AVF blood flow status. Methods and Procedures: We developed CALM, a physiologically informed learning framework that integrates Hilbert-based cycle segmentation with pretrained audio foundation model (AFM) representations. A clinical auscultation dataset (CHGH-AVF) from 188 HD patients at three standardized vascular access sites was collected and labeled using concurrent blood flow measurements. The proposed approach was evaluated on both three-class flow-severity classification (Low, Medium, and High) and binary flow-quality screening (Ideal vs. Unideal). Model variants incorporating domain-specific low-level descriptors and different feature fusion strategies were also investigated. Results: Across all auscultation sites, CALM consistently outperformed conventional handcrafted-feature baselines, deep learning baselines, and classical machine learning-based classifiers in patient-level flow-severity classification. Ablation studies demonstrated that physiologic cycle segmentation substantially improves discriminability, while integrating complementary low-level descriptors (LLDs) further enhanced performance across anatomically different recording locations. Although classification performance remained highest at the proximal sites, reduced performance at the venous site highlighted the influence of anatomical location and signal quality. Strong performance was also observed for binary flow-quality monitoring, particularly at the proximal auscultation sites. Conclusion: The proposed framework demonstrates the feasibility of AFM–based auscultation for non-invasive AVF flow assessment. By combining physiologic signal modeling with foundation model representations, CALM provides a robust and objective framework for automated assessment of vascular access function. Clinical Impact: This work introduces a multi-site clinical AVF auscultation dataset and establishes automated auscultation analysis as a low-cost and scalable adjunct to existing vascular access monitoring practices in HD care. Clinical and Translational Impact Statement: AFM-based auscultation has the potential to support objective screening of AVF dysfunction and may facilitate future integration into bedside or home monitoring systems, enabling more accessible and scalable vascular access monitoring.
Subdiaphragmatic vagus nerve stimulation (VNS) is being explored as a device-based option for obesity, but translation is limited by the lack of standardized implantation workflows and cuff electrodes that remain stable at the cuff–nerve interface in a moist in vivo environment. Here, we aimed to establish a bench-to–in vivo feasibility platform that integrates a microfabrication-compatible perfluoroalkoxy alkane (PFA) film cuff with a reproducible rabbit surgical procedure and sham-controlled evaluation. The cuff electrode was fabricated using MEMS-based processes and characterized on the bench (impedance/charge metrics), then implanted on the subdiaphragmatic vagus nerve in diet-induced obese rabbits assigned to sham or intermittent VNS at graded current levels (total n = 8; n = 2/group). Body weight was tracked as an exploratory outcome under variable delivered exposure. During the stimulation window, weight trajectories showed heterogeneous, non-monotonic patterns across current levels, with partial rebound after stimulation cessation. Gross inspection and hematoxylin and eosin (H&E) histology indicated preserved fascicular organization with a thin peri-neural fibrotic capsule, assessed qualitatively in this pilot cohort. Overall, these results support feasibility of reproducible subdiaphragmatic implantation and cuff–nerve interface operation and motivate powered studies with exposure-matched delivery logging and expanded metabolic and longitudinal endpoints.
Objective: To develop and validate a deployment-centric ECG arrhythmia classification framework for low-power wearable edge devices, with emphasis on efficient on-device screening and reduced communication burden in remote cardiac monitoring workflows, optimized for seamless integration into clinical remote monitoring workflows using resource-constrained wearable IoT edge devices.Methods and Procedures: We design a clinically-oriented wearable-to-phone workflow in which the edge device performs on-device ECG inference and transmits actionable anomaly summaries to a smartphone via Bluetooth Low Energy (BLE). This approach reduces latency, energy cost, and privacy risks compared to continuous raw-signal transmission. A NAS process tailored to arrhythmia detection automatically explores candidate architectures under diverse compute and memory constraints to identify feasible models for embedded platforms. Multiple deployment-ready variants are generated via quantization and pruning, and implemented on an ARM Cortex-M33-based system. System efficiency is quantified through end-to-end measurements of energy consumption and data throughput, ensuring its readiness for long-term ambulatory monitoring.Results: The deployed models achieve real-time inference on an ARM Cortex-M33 with as little as 21.7 kB RAM and 265k MACs. After optimization, energy consumption is reduced to 5.51 mJ per inference. Compared with the original model, RAM usage is reduced by 53.6%, with only a 0.5% absolute decrease in accuracy (97.9% to 97.4%). Hardware-in-the-loop power profiling confirms significant efficiency gains, validating the clinical feasibility of the proposed selective offloading strategy.Conclusion: A NAS-guided, deployment-centric pipeline-spanning architecture search, compression/mapping, and embedded power profiling enables accurate, real-time ECG classification within stringent wearable constraints. The selective-offloading strategy bridges the gap between laboratory algorithms and practical cardiac surveillance by providing a high-fidelity, low-power, and clinically-aware solution for continuous arrhythmia screening.
Objective: Parkinson’s disease (PD) is characterized by motor and non-motor symptoms. Among non-motor symptoms, neuropsychiatric symptoms (NPS), including depression, anxiety, cognitive decline and psychosis, are common and affect quality of life. The present study investigates gait patterns in PD patients with and without NPS, focusing on kinematic, kinetic, and spatio-temporal variables extracted from gait analysis. Notably, the assessments were carried out in a real-world outpatient clinical environment, which strengthens the translational value of the findings. Method: Overall, 104 PD patients were assessed using an optoelectronic system to record spatio-temporal parameters, kinematic and kinetic signals during a single task and two different dual tasks. First, a preliminary statistical analysis was carried out and then, several machine learning algorithms were applied to classify patients with and without NPS based on gait data. Results: Statistical analyses revealed that PD patients with NPS had slower gait, greater instability and reduced movement control. Decision Tree and Support Vector Machine classifiers showed promising results, particularly for kinematic and kinetic features (with an accuracy of 91.3% and 80.3%, respectively). The most recurrent features on which the classifiers were trained almost entirely matched the significance found by statistical analyses. Conclusion: The present findings further expand the relationship between motor and non-motor symptoms in PD highlighting that the relationship between gait and mental symptoms may extend beyond cognitive status. Clinical Impact—The study highlights the value of gait analysis as a promising, non-invasive surrogate biomarker for the early identification of a distinct clinical phenotype characterized by an increased burden of neuropsychiatric symptoms in Parkinson’s disease. The high accuracy achieved by machine learning models trained on gait features for distinguishing Parkinsonian patients with and without neuropsychiatric symptoms supports the development of assistive diagnostic tools that can enhance clinical decision-making, reduce subjectivity in patient assessments, and enable personalized monitoring of disease progression. Since the analyses were carried out in a real-world outpatient setting, the proposed approach naturally lends itself to integration within multidisciplinary clinical workflows, fostering synergistic collaboration among neurologists, physiotherapists and biomedical engineers, and contributing to a more personalized, precise, and proactive model of care.
Objective: During surgery it is common to measure the arterial blood pressure. One important reason is to monitor for hypotension, a too low blood pressure, which is known to be harmful. Since post-surgical complications correlate with hypotension, it is common to calculate the number of hypotensive events, their cumulative length, or their cumulative area under threshold. To make the data collection process manageable, parameter data is often stored only once every 15 seconds and then used to derive the amount of hypotension.Methods and procedures: In this study, we calculate the errors introduced in quantizing hypotension based on different amount of downsampling from 419 surgical patients at the open abdomen surgery unit at Karolinska University Hospital. Experiments with error mitigation are also tested. Results are compared with using the continuous blood pressure waveform data.Results: For a downsampling to once every 15 seconds, the relative bias was 27.0% for the number of hypotensive events, 30.0% for the cumulative hypotensive length, and 10.8% for the cumulative area under threshold. Error mitigation approaches often do not improve the errors.Conclusion: These results highlight the necessity of using high frequency data and that it is important to be aware of the errors when interpreting older studies involving quantification of hypotension and based on downsampled data.
Wearable consumer electronics such as smart watches, rings and hearables are increasingly used to collect real-time physiological and activity data for health monitoring and disease prevention. As researchers and practitioners at the forefront of wearable technologies in health and medicine, the Young Professionals of EMBS organized a Roundtable Discussion on the 17th of July 2025 at the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’25) to identify key challenges and recommendations for the field and we would like to highlight some key recommendations and insights from our discussions for the broader community.
Objective:Accurate pre-procedural identification of atrial flutter (AFL) mechanisms can streamline mapping and indirectly inform ablation strategy, yet surface-electrocardiogram (ECG) criteria remain unreliable and circuit definition is typically confirmed invasively.Methods and procedures:We analyzed 97 consecutive patients undergoing electrophysiological (EP) study with simultaneous 12-lead ECG and EP-verified AFL subtype; adenosine-induced atrioventricular AV block enabled extraction of clean atrial segments. We reconstructed atrial vectorcardiograms (VCGs) and engineered interpretable descriptors of loop morphology and kinematics, including archetype cosine correlation, geometric complexity, and velocity-based slow-occupancy indices, then fused these with clinical variables in an explainable tree-ensemble model evaluated with nested cross-validation.Results:VCG loops exhibited subtype-specific archetypes (within-class correlation: $0.832\pm 0.129$ CCCW, $0.874\pm 0.154$ CCW, $0.647\pm 0.127$ PMCCW, $0.667\pm 0.159$ PMCW; C: common; PM: perimitral; CW: clockwise; CCW: counter-CW). On the test set, the multimodal Random-Forest improved discrimination over VCG-only and clinical-only baselines, achieving AUROC of 0.870 (CCCW), 0.900 (CCW), 0.840 (PMCCW), and 0.790 (PMCW), with high sensitivity for common AFL (0.833 and 0.929) and very high specificity for PMCW (0.988).Conclusion:This interpretable framework provides a practical route to non-invasive, mechanism-oriented AFL stratification to support targeted mapping and more efficient ablation planning. Future work will focus on multicenter prospective validation and robust atrial-signal extraction without adenosine to broaden routine applicability.
The IEEE Journal of Translational Engineering in Health and Medicine (JTEHM) exists at the intersection of biomedical engineering and clinical practice. Published articles go beyond laboratory proof-of-concept to provide tangible, real-world evidence of translation into clinical settings. This editorial provides the rationale for manuscripts submitted to IEEE JTEHM to demonstrate evidence of clinical translation. It also provides examples of acceptable forms of evidence and offers guidance to authors on how to meet this expectation. Clinical and Impact—By requiring demonstrated clinical translational evidence IEEE JTEHM endeavours to publish high-quality research with scientific novelty and practical clinical impact. This expectation strengthens the journal’s aim to accelerate the adoption of innovative solutions into healthcare systems and ultimately deliver quantifiable benefits to patients.
OBJECTIVE:This study evaluates the performance of a fully wireless multi-node wearable platform equipped with a sub-microsecond synchronization engine in a clinically relevant scenario. The prototype system is used in a cuff-less blood pressure monitoring application, based on pulse arrival time derived from synchronized ECG and PPG signals. METHODS:The system integrates a custom 2.4 GHz synchronization protocol and Bluetooth Low Energy for data transmission. Nineteen healthy subjects completed a treadmill protocol designed to induce transient hemodynamic perturbations representative of daily-life physical activities. PAT values extracted from ECG and PPG signals acquired before and after exercise were compared with reference systolic blood pressure (SBP) measurements, intermittently sampled using a validated oscillometric device. The experimental protocol and analysis were designed to reflect realistic home monitoring scenarios, including limited user interaction during measurements. RESULTS:Immediately after exercise, significant deviations from baseline were observed in computed PAT ([Formula: see text]) and SBP ([Formula: see text]). PAT recovery trend was accurately modeled by a mono-exponential function ([Formula: see text]). Recovery indices derived from PAT and SBP were strongly correlated (Pearson's [Formula: see text], [Formula: see text]) with high concordance ([Formula: see text]), although Bland-Altman analysis revealed subject-specific variability (LoA: -34.36% / 37.53%). CONCLUSION:The proposed platform enables continuous synchronized multi-signal acquisition for extracting PAT dynamics and tracking blood pressure fluctuations under realistic home-monitoring conditions, with minimal additional user interaction. These results support the operational feasibility of wirelessly synchronized architectures for cardiovascular monitoring in daily-life scenarios, promoting integration into remote health assessment workflows beyond traditional intermittent cuff-based measurements.
Objective: Robotic-assisted sacrocolpopexy is the gold standard for treating advanced pelvic organ prolapse, but its most critical step, presacral dissection, carries a significant risk of vascular and ureteral injury. Traditional training relies heavily on intraoperative exposure, yet no simulator currently provides an anatomically realistic, reusable, and cost-effective platform for practicing this high-risk procedure. Method: To address this gap, we developed a modular phantom designed for training in laparoscopic and robotic sacrocolpopexy procedures. A hierarchical task analysis was conducted to identify the critical steps involved in presacral dissection, informing the design of the simulator. The phantom incorporates a reusable pelvic base and a stratified sacral area pad that reproduces the vertebrae, anterior longitudinal ligament, vascular structures, ureters, visceral fat, and peritoneum. Fabrication employed 3D printing, silicone molding, and layered composite materials to strike a balance between anatomical fidelity and durability. Results: Five expert gynecologic surgeons validated the simulator using structured questionnaires, hierarchical task analysis-based task assessment. The model demonstrated excellent content validity (S-CVI = 0.877), strong inter-rater agreement, and high internal consistency (Cronbach’s alpha = 0.925). Ten of thirteen critical surgical tasks were completed by all participants, including realistic dissection and ligament exposure. Face validity ratings confirmed the realism of anatomy and haptic feedback, though ureteral identification was less consistent. Conclusion: The developed phantom enables realistic, structured training in presacral dissection and mesh fixation, supporting skill acquisition outside the operating room. Clinical Impact—By providing a cost-effective, reusable, and anatomically faithful simulator, this work contributes to safer training in gynecology and pelvic floor surgery, with potential integration into competency-based curricula and dry-lab facilities.
Objective: To develop an explainable, knowledge-guided framework for automated detection of contrast media extravasation from sequential computed tomography (CT) images and to evaluate its potential to accelerate time-critical trauma triage while maintaining clinically acceptable sensitivity.Methods: A mathematical framework was formulated to explicitly encode three expert-derived diagnostic rules: 1) progressive increase of contrast outside anatomically plausible vessels, 2) appearance of contrast in non-vascular regions, and 3) localized irregularity of vessel caliber. Sequential two-dimensional CT slices were analyzed using a 2.5D formulation integrating temporal intensity evolution, anatomical plausibility, vessel morphology, and inter-slice continuity. The model outputs a confidence score and a binary alert. Model parameters and decision thresholds were initialized using a single representative clinical case guided by expert interpretation. Performance was evaluated against senior emergency surgeon assessment, emphasizing sensitivity and time-to-decision.Results: The proposed framework achieved clinically acceptable sensitivity for detection of contrast extravasation while substantially reducing time-to-decision relative to manual review. Early-trigger analysis demonstrated that positive cases were identified within the initial portion of the CT volume, supporting rapid screening and prioritization in emergency workflows.Conclusion: This study demonstrates the feasibility of translating expert clinical reasoning into an interpretable computational model for time-critical imaging tasks. The knowledge-guided design enables rapid automated screening while preserving transparency and clinician oversight. The framework shows promise as a decision-support tool for accelerating trauma triage, with future work focused on prospective validation and broader multi-center evaluation. Clinical Impact: The proposed knowledge-guided algorithm enables rapid extravasation alerts on trauma CT, supporting earlier triage and prioritization for angiography or surgery within existing emergency imaging workflows.
Objective: Diagnosis of lower urinary tract symptoms (LUTS) often relies on subjective questionnaires, conventional uroflowmetry or invasive urodynamic study, each with inherent limitations.Methods and procedures: This work presents EasyVoid, a novel handheld device that employs the Coandă effect to enable non-invasive and quantitative measurement of urinary flow. EasyVoid uses a passive fluidic mechanism in which the urinary stream adheres to a curved surface and transfers angular momentum to a helical rotor, allowing direct estimation of flow rate and volume without pressure sensors or weighing systems.Results: EasyVoid simultaneously records key voiding parameters, including flow rate, flow pattern, voiding time, and voided volume, with high temporal precision. In controlled bench-top trials using simulated urine events, measurements obtained from EasyVoid showed strong agreement with standard uroflowmetry across multiple flow patterns.Conclusion: These results demonstrate the potential of EasyVoid for portable and quantitative assessment of urinary flow dynamics, offering a promising solution for remote urinary health monitoring.
Objective: Wearable exoskeletons can improve walking in children with movement disorders by delivering precisely timed and scaled torques based on discrete gait phase. Most single-joint systems segment the gait cycle using underfoot force-sensitive resistors (FSRs), but FSRs are suboptimal for exoskeleton use in pathological gait due to fragile hardware and inconsistent foot loading/unloading. Thigh kinematics may provide more robust gait event detection (GED), yet this approach has not been evaluated in children with crouch gait, a key target population for pediatric exoskeleton therapy. This study aimed to: 1) assess feasibility of GED using thigh kinematics from ground-truth motion capture, 2) evaluate GED using a thigh-mounted inertial measurement unit (IMU), and 3) validate this method with a wearable knee exoskeleton in children with crouch gait.Methods and procedures: A novel thigh segment kinematics algorithm (TSKA) was developed to detect initial contact (IC) and terminal contact (TC) during overground walking. Algorithm performance was assessed using IMU-derived gait events compared to motion capture ground truth, then evaluated with a wearable knee exoskeleton.Results: Mean IMU-based timing errors for IC and TC were 38 and 84 ms, respectively. IC predictions occurred earlier than ground truth, whereas TC predictions varied. During exoskeleton walking, IMU-based GED significantly improved timing accuracy compared to FSR-based detection.Conclusion: The thigh-based kinematic algorithm, tuned for each individual with six or fewer strides, accurately detected gait events in children with crouch gait and outperformed FSR-based GED in an exoskeleton. These findings support further development for real-time exoskeleton control and gait assessment in individuals with pathological gait.
Objective: Pediatric airway management presents unique challenges, particularly in patients with congenital conditions that increase the risk of difficult intubation. Simulation offers a safe environment for clinicians to practice, yet current pediatric mannequins often lack pathological realism. This study aims to develop and validate a high-fidelity, patient-specific pediatric airway mannequin simulating difficult intubation associated with Crouzon syndrome. Technology or Method: A modular training mannequin was developed through CT-based modeling and additive manufacturing at the T3Ddy Laboratory, a collaboration between AOU Meyer Children's Hospital-IRCCS and the University of Florence. Anatomical structures were segmented from CT and MRI scans of a 19-month-old patient with Crouzon syndrome. These 3-D models guided the design of a realistic airway, including nasal and oral access, a flexible tongue, and a movable jaw. Rigid parts were 3-D-printed; soft tissues were cast in silicones of varying hardness. Design choices were validated through iterative testing and refinement. Results: The simulator was evaluated during a pediatric bronchoscopy training course. Physicians assessed realism, tactile feedback, and usability via a Likert-scale questionnaire. Results indicated strong agreement on the mannequin's effectiveness in replicating challenging intubation scenarios, particularly those requiring fiberoptic-guided techniques. Conclusion: This modular, patient-specific pediatric airway simulator provides a realistic, high-fidelity platform for training clinicians in difficult intubation techniques. Its anatomically accurate design enhances procedural confidence and skill acquisition, particularly for managing complex cases like those associated with Crouzon syndrome.
