FetalBrainAwareNet: Bridging GANs with anatomical insight for fetal ultrasound brain plane synthesis.

Over the past decade, deep-learning (DL) algorithms have become a promising tool to aid clinicians in identifying fetal head standard planes (FHSPs) during ultrasound (US) examination. However, the adoption of these algorithms in clinical settings is still hindered by the lack of large annotated datasets. To overcome this barrier, we introduce FetalBrainAwareNet, an innovative framework designed to synthesize anatomically accurate images of FHSPs. FetalBrainAwareNet introduces a cutting-edge approach that utilizes class activation maps as a prior in its conditional adversarial training process. This approach fosters the presence of the specific anatomical landmarks in the synthesized images. Additionally, we investigate specialized regularization terms within the adversarial training loss function to control the morphology of the fetal skull and foster the differentiation between the standard planes, ensuring that the synthetic images faithfully represent real US scans in both structure and overall appearance. The versatility of our FetalBrainAwareNet framework is highlighted by its ability to generate high-quality images of three predominant FHSPs using a singular, integrated framework. Quantitative (Fréchet inception distance of 88.52) and qualitative (t-SNE) results suggest that our framework generates US images with greater variability compared to state-of-the-art methods. By using the synthetic images generated with our framework, we increase the accuracy of FHSP classifiers by 3.2% compared to training the same classifiers solely with real acquisitions. These achievements suggest that using our synthetic images to increase the training set could provide benefits to enhance the performance of DL algorithms for FHSPs classification that could be integrated in real clinical scenarios.

Immersive VR for upper-extremity rehabilitation in patients with neurological disorders: a scoping review.

BACKGROUND: Neurological disorders, such as stroke and chronic pain syndromes, profoundly impact independence and quality of life, especially when affecting upper extremity (UE) function. While conventional physical therapy has shown effectiveness in providing some neural recovery in affected individuals, there remains a need for improved interventions. Virtual reality (VR) has emerged as a promising technology-based approach for neurorehabilitation to make the patient's experience more enjoyable. Among VR-based rehabilitation paradigms, those based on fully immersive systems with headsets have gained significant attention due to their potential to enhance patient's engagement. METHODS: This scoping review aims to investigate the current state of research on the use of immersive VR for UE rehabilitation in individuals with neurological diseases, highlighting benefits and limitations. We identified thirteen relevant studies through comprehensive searches in Scopus, PubMed, and IEEE Xplore databases. Eligible studies incorporated immersive VR for UE rehabilitation in patients with neurological disorders and evaluated participants' neurological and motor functions before and after the intervention using clinical assessments. RESULTS: Most of the included studies reported improvements in the participants rehabilitation outcomes, suggesting that immersive VR represents a valuable tool for UE rehabilitation in individuals with neurological disorders. In addition, immersive VR-based interventions hold the potential for personalized and intensive training within a telerehabilitation framework. However, further studies with better design are needed for true comparison with traditional therapy. Also, the potential side effects associated with VR head-mounted displays, such as dizziness and nausea, warrant careful consideration in the development and implementation of VR-based rehabilitation programs. CONCLUSION: This review provides valuable insights into the application of immersive VR in UE rehabilitation, offering the foundation for future research and clinical practice. By leveraging immersive VR's potential, researchers and rehabilitation specialists can design more tailored and patient-centric rehabilitation strategies, ultimately improving the functional outcome and enhancing the quality of life of individuals with neurological diseases.

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients.

BACKGROUND: Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed. AIM: We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol. DESIGN: Prospective cohort study. SETTING: Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy. POPULATION: Sub-acute and chronic stroke survivors. METHODS: Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery. RESULTS: Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2. CONCLUSIONS: The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns. CLINICAL REHABILITATION IMPACT: This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.

Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records.

Amyloidosis refers to a range of medical conditions in which misshapen proteins accumulate in various organs and tissues, forming insoluble fibrils. Cardiac amyloidosis is frequently linked to the buildup of misfolded transthyretin (TTR) or immunoglobulin light chains (AL). Delayed diagnosis, due to lack of disease awareness, results in a poor prognosis, especially in patients with AL amyloidosis. Early identification is therefore a key factor to improve patient outcomes. This study investigates the use of supervised machine-learning algorithms to support clinicians in classifying amyloidosis and control subjects. The aim of this work is to foster model interpretability reporting the most important risk factors in predicting the presence of cardiac amyloidosis. We analyzed electronic health records (EHRs) of 418 participants acquired in a time window of 12 years as part of a case-control study conducted in Fondazione Toscana Gabriele Monasterio (Italy) clinical practice. This work paves the way for the creation of digital health solutions that can aid in amyloidosis screening. The effective handling, analysis, and interpretation of these solutions can have a transformative effect on modern healthcare, offering new opportunities for improved patient care.

Exploiting class activation mappings as prior to generate fetal brain ultrasound images with GANs.

The identification of fetal-head standard planes (FHSPs) from ultrasound (US) images is of fundamental importance to visualize cerebral structures and diagnose neural anomalies during gestation in a standardized way. To support the activity of healthcare operators, deep-learning algorithms have been proposed to classify these planes. To date, the translation of such algorithms in clinical practice is hampered by several factors, including the lack of large annotated datasets to train robust and generalizable algorithms. This paper proposes an approach to generate synthetic FHSP images with conditional generative adversarial network (cGAN), using class activation maps (CAMs) obtained from FHSP classification algorithms as cGAN conditional prior. Using the largest publicly available FHSP dataset, we generated realistic images of the three common FHSPs: trans-cerebellum, trans-thalamic and trans-ventricular. The evaluation through t-SNE shows the potential of the proposed approach to attenuate the problem of limited availability of annotated FHSP images.

A preliminary study on self-care telemonitoring of dysarthria in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a rare neuromuscular disease which may cause impairments in oro-facial musculature. Most of the individuals with SMA present bulbar signs such as flaccid dysarthria which mines their abilities to speak and, as consequence, their psychic balance. To support clinicians, recent work has demonstrated the feasibility of video-based techniques for assessing the oro-facial functions in patients with neurological disorders such as amyotrophic lateral sclerosis. However, no work has so far focused on automatic and quantitative monitoring of dysarthria in SMA. To overcome limitations this work's aim is to propose a cloud-based store-and-forward telemonitoring system for automatic and quantitative evaluation of oro-facial muscles in individuals with SMA. The system integrates a convolutional neural network (CNN) aimed at identifying the position of facial landmarks from video recordings acquired via a web application by an SMA patient.Clinical relevance- The proposed work is in the preliminary stage, but it represents the first step towards a better understanding of the bulbar-functions' evolution in patients with SMA.

Intelligibility of speech in Parkinson's disease relies on anatomically segregated subthalamic beta oscillations.

BACKGROUND: Speech impairment is commonly reported in Parkinson's disease and is not consistently improved by available therapies - including deep brain stimulation of the subthalamic nucleus (STN-DBS), which can worsen communication performance in some patients. Improving the outcome of STN-DBS on speech is difficult due to our incomplete understanding of the contribution of the STN to fluent speaking. OBJECTIVE: To assess the relationship between subthalamic neural activity and speech production and intelligibility. METHODS: We investigated bilateral STN local field potentials (LFPs) in nine parkinsonian patients chronically implanted with DBS during overt reading. LFP spectral features were correlated with clinical scores and measures of speech intelligibility. RESULTS: Overt reading was associated with increased beta-low ([1220) Hz) power in the left STN, whereas speech intelligibility correlated positively with beta-high ([2030) Hz) power in the right STN. CONCLUSION: We identified separate contributions from frequency and brain lateralization of the STN in the execution of an overt reading motor task and its intelligibility. This subcortical organization could be exploited for new adaptive stimulation strategies capable of identifying the occurrence of speaking behavior and facilitating its functional execution.

Grasp Analysis in the Home Environment as a Measure of Hand Function After Cervical Spinal Cord Injury.

BACKGROUND: Following a spinal cord injury, regaining hand function is a top priority. Current hand assessments are conducted in clinics, which may not fully represent real-world hand function. Grasp strategies used in the home environment are an important consideration when examining the impact of rehabilitation interventions. OBJECTIVE: The main objective of this study is to investigate the relationship between grasp use at home and clinical scores. METHOD: We used a previously collected dataset in which 21 individuals with spinal cord injuries (SCI) recorded egocentric video while performing activities of daily living in their homes. We manually annotated 4432 hand-object interactions into power, precision, intermediate, and non-prehensile grasps. We examined the distributions of grasp types used and their relationships with clinical assessments. RESULTS: Moderate to strong correlations were obtained between reliance on power grasp and the Spinal Cord Independence Measure III (SCIM; P < .05), the upper extremity motor score (UEMS; P < .01), and the Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP) Prehension (P < .01) and Strength (P < .01). Negative correlations were observed between the proportion of non-prehensile grasping and SCIM (P < .05), UEMS (P < .05), and GRASSP Prehension (P < .01) and Strength (P < .01). CONCLUSION: The types of grasp types used in naturalistic activities at home are related to upper limb impairment after cervical SCI. This study provides the first direct demonstration of the importance of hand grasp analysis in the home environment.

Left Atrioventricular Transvalvular Pressure Gradients Derived from Intraoperative and Postoperative Echocardiograms following Atrioventricular Septal Defect Repair.

BACKGROUND: Left atrioventricular valve (LAVV) stenosis following an atrioventricular septal defect (AVSD) repair is a rare but potentially life-threatening complication. While echocardiographic quantification of diastolic transvalvular pressure gradients is paramount in the evaluation of a newly corrected valve function, it is hypothesized that these measured gradients are overestimated immediately following a cardiopulmonary bypass (CPB) due to the altered hemodynamics when compared to postoperative valve assessments using awake transthoracic echocardiography (TTE) upon recovery after surgery. METHODS: Out of the 72 patients screened for inclusion at a tertiary center, 39 patients undergoing an AVSD repair with both intraoperative transesophageal echocardiograms (TEE, performed immediately after a CPB) and an awake TTE (performed prior to hospital discharge) were retrospectively selected. The mean (MPGs) and peak pressure gradients (PPGs) were quantified using a Doppler echocardiography and other measures of interest were recorded (e.g., a non-invasive surrogate of the cardiac output and index (CI), left ventricular ejection fraction, blood pressures and airway pressures). The variables were analyzed using the paired Student's t-tests and Spearman's correlation coefficients. RESULTS: The MPGs were significantly higher in the intraoperative measurements when compared to the awake TTE (3.0 ± 1.2 vs. 2.3 ± 1.1 mmHg; p < 0.01); however, the PPGs did not significantly differ (6.6 ± 2.7 vs. 5.7 ± 2.8 mmHg; p = 0.06). Although the assessed intraoperative heart rates (HRs) were also higher (132 ± 17 vs. 114 ± 21 bpm; p < 0.001), there was no correlation found between the MPG and the HR, or any other parameter of interest, at either time-point. In a further analysis, a moderate to strong correlation was observed in the linear relationship between the CI and the MPG (r = 0.60; p < 0.001). During the in-hospital follow-up period, no patients died or required an intervention due to LAVV stenosis. CONCLUSIONS: The Doppler-based quantification of diastolic transvalvular LAVV mean pressure gradients using intraoperative transesophageal echocardiography seems to be prone to overestimation due to altered hemodynamics immediately after an AVSD repair. Thus, the current hemodynamic state should be taken into consideration during the intraoperative interpretation of these gradients.

Analysis of the Hands in Egocentric Vision: A Survey.

Egocentric vision (a.k.a. first-person vision-FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.

Designing an Egocentric Video-Based Dashboard to Report Hand Performance Measures for Outpatient Rehabilitation of Cervical Spinal Cord Injury.

Background: Functional use of the upper extremities (UEs) is a top recovery priority for individuals with cervical spinal cord injury (cSCI), but the inability to monitor recovery at home and limitations in hand function outcome measures impede optimal recovery. Objectives: We developed a framework using wearable cameras to monitor hand use at home and aimed to identify the best way to report information to clinicians. Methods: A dashboard was iteratively developed with clinician (n = 7) input through focus groups and interviews, creating low-fidelity prototypes based on recurring feedback until no new information emerged. Affinity diagramming was used to identify themes and subthemes from interview data. User stories were developed and mapped to specific features to create a high-fidelity prototype. Results: Useful elements identified for a dashboard reporting hand performance included summaries to interpret graphs, a breakdown of hand posture and activity to provide context, video snippets to qualitatively view hand use at home, patient notes to understand patient satisfaction or struggles, and time series graphing of metrics to measure trends over time. Conclusion: Involving end-users in the design process and breaking down user requirements into user stories helped identify necessary interface elements for reporting hand performance metrics to clinicians. Clinicians recognized the dashboard's potential to monitor rehabilitation progress, provide feedback on hand use, and track progress over time. Concerns were raised about the implementation into clinical practice, therefore further inquiry is needed to determine the tool's feasibility and usefulness in clinical practice for individuals with UE impairments.

Bolus Texture Testing as a Clinical Method for Evaluating Food Oral Processing and Choking Risk: A Pilot Study.

PURPOSE: Choking on food is a leading cause of accidental death in several populations, including children, people with intellectual/developmental disability, and older adults in residential care facilities. One contributor to choking risk is incomplete oral processing and failure to convert food to a cohesive, nonsticky bolus with a maximum particle size that will not block the airway. Clinical tests of mastication do not evaluate properties of chewed food boluses. We characterized expectorated boluses, after oral processing, using methods developed by the International Dysphagia Diet Standardisation Initiative (IDDSI). METHOD: Seventeen adults without dysphagia (seven women and 10 men), aged 23-55 years, chewed samples of a cracker, a raw baby carrot, and a circular, dome-shaped gummy candy. Chewing metrics were obtained up to the point when the person indicated that they were ready to swallow. The bolus was then either expectorated or swallowed; IDDSI tests were used to characterize the expectorated boluses. RESULTS: Measures of chewing did not differ between spit and swallow conditions. Expectorated cracker and carrot boluses had maximum particle size consistent with IDDSI Level SB6 Soft & Bite-Sized foods or lower. The gummy candy samples remained at IDDSI Level RG7 Regular food consistency. CONCLUSIONS: This study suggests that expectorated ready-to-swallow boluses are representative of boluses that are swallowed and that oral processing in adults without dysphagia typically results in boluses at IDDSI's Level SB6 or lower. IDDSI's testing methods provide a practical method for evaluating oral processing by characterizing expectorated ready-to-swallow boluses and may guide food texture recommendations for persons who have increased risk of choking.

Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?

BACKGROUND AND OBJECTIVE: The videofluoroscopic swallowing study (VFSS) is a gold-standard imaging technique for assessing swallowing, but analysis and rating of VFSS recordings is time consuming and requires specialized training and expertise. Researchers have recently demonstrated that it is possible to automatically detect the pharyngeal phase of swallowing and to localize the bolus in VFSS recordings via computer vision approaches, fostering the development of novel techniques for automatic VFSS analysis. However, training of algorithms to perform these tasks requires large amounts of annotated data that are seldom available. In this paper, we demonstrate that the challenges of pharyngeal phase detection and bolus localization can be solved together using a single approach. METHODS: We propose a deep-learning framework that jointly tackles pharyngeal phase detection and bolus localization in a weakly-supervised manner, requiring only the initial and final frames of the pharyngeal phase as ground truth annotations for the training. Our approach stems from the observation that bolus presence in the pharynx is the most prominent visual feature upon which to infer whether individual VFSS frames belong to the pharyngeal phase. We conducted extensive experiments with multiple convolutional neural networks (CNNs) on a dataset of 1245 bolus-level clips from 59 healthy subjects. RESULTS: We demonstrated that the pharyngeal phase can be detected with an F1-score higher than 0.9. Moreover, by processing the class activation maps of the CNNs, we were able to localize the bolus with promising results, obtaining correlations with ground truth trajectories higher than 0.9, without any manual annotations of bolus location used for training purposes. CONCLUSIONS: Once validated on a larger sample of participants with swallowing disorders, our framework will pave the way for the development of intelligent tools for VFSS analysis to support clinicians in swallowing assessment.

Measuring Hand Use in the Home after Cervical Spinal Cord Injury Using Egocentric Video.

Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. The aim of this study was to develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 h of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc); the average duration of individual interactions (Dur); and the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension [GRASSP], Upper Extremity Motor Score [UEMS], and Spinal Cord Independent Measure [SCIM]). Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use.

Protective continuous ventilation strategy during cardiopulmonary bypass in children undergoing surgery for congenital heart disease: a prospective study.

OBJECTIVES: The aim of this study was to evaluate if a 'protective' (low-tidal/low-frequency) ventilation strategy can shorten the postoperative ventilation time and minimize acute lung injury in children with congenital heart disease (CHD) undergoing repair with cardiopulmonary bypass (CPB). METHODS: This is a single-centre prospective, interventional study, including children with CHD under the age of 5 years, undergoing open-heart surgery with a CPB >60 min, in hypothermia, haemodynamically stable, and without evident genetic abnormalities. Assist-control ventilation (tidal volume of 4 ml/kg, 10 breaths/min, positive end-expiratory pressure 5 cmH2O and FiO2 0.21) was applied in a cohort of patients during CPB. We compared clinical outcomes and in fully ventilated versus non-ventilated (control) patients. Propensity score was used to weigh ventilated and control groups to correct for the effect of other confounding clinical variables. Clinical and ventilation parameters and lung inflammatory biomarkers in tracheal aspirates were measured. The primary outcome was the postoperative intubation time of more or less than 48 h. RESULTS: We included 140 children (53 ventilated, 87 non-ventilated) with different CHD. There were no deaths or adverse events in ventilated patients. Using a weighted generalized linear model, we found no sufficient evidence for an effect of intraoperative ventilation on postoperative intubation time [estimate 0.13 (95% confidence interval, -0.08; 0.35), P = 0.22]. CONCLUSIONS: Continuous low-tidal/low-frequency mechanical ventilation during CPB is safe and harmless. However, no significant advantages were found when compared to non-ventilated patients in terms of postoperative ventilation time.

Post-Stroke Hemiplegic Rodent Evaluation: A Framework for Assessing Forelimb Movement Quality Using Kinematics.

Kinematics is the gold-standard method for measuring detailed joint motions. Recent research demonstrates that post-stroke kinematic analysis in rats reveals reaching abnormalities similar to those seen in humans after stroke. Nonetheless, behavioral neuroscientists have failed to incorporate kinematic methods for assessing movement quality in stroke models. The availability of a user-friendly method to assess multi-segment forelimb kinematics models should greatly increase uptake of this approach. Here, we present a framework for multi-segment forelimb analysis in rodents after stroke. This method greatly enhances the understanding of post-stroke forelimb motor recovery by including several movement quality metrics often used in human clinical work, such as upper-limb linear and angular kinematics, movement smoothness and kinetics, abnormal synergies, and compensations. These metrics may constitute a preclinical surrogate for the Fugl-Meyer assessment of hemiplegic patients. The data obtained using this method are 83 outputs of linear and angular kinematics and kinetics. The outputs also include 24 time series of continuous data, which afford a graphical representation of the kinematics and kinetics of the reaching cycle. We show that post-stroke rodents displayed many features resembling those seen in humans after stroke that are evident only when multi-segment kinematics models are considered. This method expands the knowledge derived from methods constrained to paw movements to a multi-segment forelimb movement quality framework. Moreover, it highlights the need for preclinical work to consider more sensitive measures of sensorimotor impairment and recovery as a means to enhance the interpretation of true recovery and compensation. © 2022 Wiley Periodicals LLC. Basic Protocol: Recording and data analysis of rodents performing the Montoya staircase task.

The effect of time on the automated detection of the pharyngeal phase in videofluoroscopic swallowing studies.

Convolutional Neural Networks (CNNs) have recently been proposed to automatically detect the pharyngeal phase in videofluoroscopic swallowing studies (VFSS). However, there is a lack of consensus regarding the best algorithmic strategy to adopt for segmenting this important yet rapid phase of the swallow. Moreover, additional information is needed to understand how small the detection error should be, in view of translating this approach for use in clinical practice. In this manuscript we compare multiple CNN-based algorithms for detecting the pharyngeal phase in VFSS bolus-level clips, specifically looking at 2DCNN and 3DCNN approaches with different temporal windows as input. Our results showed that a 2DCNN analysis on 3-frame windows outperformed both frame-by-frame approaches and 3DCNNs. We also demonstrated that the detection accuracy of the pharyngeal phase is very close to the clinical gold standard (i.e., trained clinical raters). These results demonstrate the feasibility of deep learning-based algorithms for developing intelligent approaches to automatically support clinicians in the analysis of VFSS data.Clinical relevance- Accurate and reliable segmentation of the pharyngeal phase will support clinicians by reducing the time needed for rating VFSS data. Moreover, automatic detection of this phase can be seen as a foundation for building novel and intelligent approaches to detect clinical features of interest in VFSS, such as the presence of penetration-aspiration.

Perspectives and recommendations of individuals with tetraplegia regarding wearable cameras for monitoring hand function at home: Insights from a community-based study.

CONTEXT: Wearable cameras have great potential for producing novel outcome measures of upper limb (UL) function and guiding care in individuals with cervical spinal cord injury (cSCI) living in the community. However, little is known about the perspectives of individuals with cSCI on the potential adoption of this technology. OBJECTIVE: To analyze feedback from individuals with cSCI regarding the use of wearable cameras to record daily activities at home, in order to define guidelines for improving the design of this technology and fostering its implementation to optimize UL rehabilitation. DESIGN: Mixed-methods study. PARTICIPANTS: Thirteen adults with cSCI C3-C8 AIS A-D impairment. MEASURES: Interview including survey and semi-structured questions. RESULTS: Participants felt that this technology can provide naturalistic information regarding hand use to clinicians and researchers, which in turn can lead to better assessments of UL function and optimized therapies. Participants described the technology as easy-to-use but often reported discomfort that prevented them from conducting long recordings of fully natural activities. Privacy concerns included the possibility to capture household members and personal information displayed on objects (e.g. smartphones). CONCLUSION: We provide the first set of guidelines to help researchers and therapists understand which steps need to be taken to translate wearable cameras into outpatient care and community-based research for UL rehabilitation. These guidelines include miniaturized and easy-to-wear cameras, as well as multiple measures for preventing privacy concerns such as avoiding public spaces and providing control over the recordings (e.g. start and stop the recordings at any time, keep or delete a recording).

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders.

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

Stress Assessment by Combining Neurophysiological Signals and Radio Communications of Air Traffic Controllers.

Air Traffic Control (ATC) has been classified as the fourth most stressful job. In this regard, sixteen controllers were asked to perform ecological ATC simulation during which behavioral (Radio Communications with pilots - RCs), subjective (stress perception) and neurophysiological signals (brain activity and skin conductance - SC) were collected. All the considered parameters reported significant changes under high stress conditions. In particular, the theta, alpha, and beta brain rhythms increased significantly (all p<0.05) all over the brain areas, and both the SC components exhibited higher values (p<0.01). Additionally, the number of speech under high stress decreased significantly (p<10-4) while both the mean and median value of the F0 component of the RC increased (p<0.01). The results can be employed to objectively measure and track the controller's stress level while dealing with ATC activities to better tailoring the workshift and maintaining high safety levels.