Multi-modal analysis of infant cry types characterization: Acoustics, body language and brain signals

BACKGROUND

Infant crying is the first attempt babies use to communicate during their initial months of life. A misunderstanding of the cry message can compromise infant care and future neurodevelopmental process.

METHODS

An exploratory study collecting multimodal data (i.e., crying, electroencephalography (EEG), near-infrared spectroscopy (NIRS), facial expressions, and body movements) from 38 healthy full-term newborns was conducted. Cry types were defined based on different conditions (i.e., hunger, sleepiness, fussiness, need to burp, and distress). Statistical analysis, Machine Learning (ML), and Deep Learning (DL) techniques were used to identify relevant features for cry type classification and to evaluate a robust DL algorithm named Acoustic MultiStage Interpreter (AMSI).

RESULTS

Significant differences were found across cry types based on acoustics, EEG, NIRS, facial expressions, and body movements. Acoustics and body language were identified as the most relevant ML features to support the cause of crying. The DL AMSI algorithm achieved an accuracy rate of 92%.

CONCLUSIONS

This study set a precedent for cry analysis research by highlighting the complexity of newborn cry expression and strengthening the potential use of infant cry analysis as an objective, reliable, accessible, and non-invasive tool for cry interpretation, improving the infant-parent relationship and ensuring family well-being.

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol

INTRODUCTION

Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors.

METHODS AND ANALYSIS

We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods.

ETHICS AND DISSEMINATION

The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation.

TRIAL REGISTRATION NUMBER

NCT05747040.

How can cry acoustics associate newborns' distress levels with neurophysiological and behavioral signals?

Introduction

Even though infant crying is a common phenomenon in humans' early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns.

Methods

Multimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants.

Results

We found correlations between most of the features extracted from the signals depending on the infant's arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy.

Conclusion

Our findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.

Evaluating Dual Task Neurological Costs with Functional Near-Infrared Spectroscopy: A Preliminary Report in Healthy Athletes

BACKGROUND

Dual task assessments, which simultaneously challenge and assess cognitive and motor performance, have been used to improve the assessment of athletes with sports-related concussions (SRC). Our lab created a Dual Task Screen (DTS) to evaluate athletes with SRCs, and we have established that it is a valid behavioral measure, as it consistently elicits poorer behavioral performance under dual, compared to single, task conditions. Here, we used a Neuroimaging-Compatible (NC) version of the DTS, named the NC-DTS, which uses portable functional near-infrared spectroscopy (fNIRS) to assess behavioral performance and neural recruitment during single and dual tasks. Our study objective was to evaluate healthy athletes and establish whether the NC-DTS is a valid dual task neurological assessment that can elicit different patterns of neural recruitment during dual versus single task conditions.

METHODS

Twenty-five healthy collegiate athletes completed the NC-DTS in a single laboratory visit. The NC-DTS includes a lower and upper extremity subtask; both include single motor, single cognitive, and dual task conditions. The NC-DTS was administered in a block design, where conditions (i.e., single motor, single cognitive, and dual task) were repeated five times to generate average behavioral performance and task-dependent neural recruitment in superficial cortical regions including: prefrontal cortex, bilateral primary motor and sensory cortices, and posterior parietal cortex. Neural recruitment was measured with fNIRS and quantified using oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) metrics. A single-tailed, within subject t-test was used to compare average dual task behavioral performance to average single task behavioral performance. Pairwise comparisons, that were family-wise-error (FWE) corrected, were used to compare localized neural recruitment during dual versus single task conditions.

RESULTS

As observed in previous studies, the NC-DTS elicited significantly poorer behavioral performance under dual, compared to single, task conditions. Additionally, dual task conditions of the NC-DTS elicited significantly greater neural recruitment in regions of the brain associated with attention allocation and task-specific demands in three of four comparisons.

CONCLUSIONS

These preliminary results suggest that the NC-DTS is a valid dual task neurological assessment which warrants future work using the NC-DTS to evaluate athletes with SRCs.

Characterizing Counterion-Dependent Aggregation of Rhodamine B by Classical Molecular Dynamics Simulations

The aggregation in a solution of charged dyes such as Rhodamine B (RB) is significantly affected by the type of counterion, which can determine the self-assembled structure that in turn modulates the optical properties. RB aggregation can be boosted by hydrophobic and bulky fluorinated tetraphenylborate counterions, such as F5TPB, with the formation of nanoparticles whose fluorescence quantum yield (FQY) is affected by the degree of fluorination. Here, we developed a classical force field (FF) based on the standard generalized Amber parameters that allows modeling the self-assembling process of RB/F5TPB systems in water, consistent with experimental evidence. Namely, the classical MD simulations employing the re-parametrized FF reproduce the formation of nanoparticles in the RB/F5TPB system, while in the presence of iodide counterions, only RB dimeric species can be formed. Within the large, self-assembled RB/F5TPB aggregates, the occurrence of an H-type RB-RB dimer can be observed, a species that is expected to quench RB fluorescence, in agreement with the experimental data of FQY. The outcome provides atomistic details on the role of the bulky F5TPB counterion as a spacer, with the developed classical FF representing a step towards reliable modeling of dye aggregation in RB-based materials.

Electroencephalographic Interbrain Synchronization in Children with Disabilities, their Parents, and Neurologic Music Therapists

As with typically developing children, children with cerebral palsy (CP) and autism spectrum disorder (ASD) develop important socio-emotional rapport with their parents and healthcare providers. However, the neural mechanisms underlying these relationships have less studied. By simultaneously measuring the brain activity of multiple individuals, interbrain synchronization could serve as a neurophysiological marker of social-emotional responses. Music evokes emotional and physiological responses and enhances social cohesion. These characteristics of music have fostered its deployment as a therapeutic medium in clinical settings. Therefore, this study investigated two aspects of interbrain synchronization, namely its phase and directionality, in child-parent (CP) and child-therapist (CT) dyads during music and storytelling session (as a comparison). A total of 17 participants (7 CP or ASD children [aged 12-18 years], their parents, and 3 neurologic music therapists) completed this study, comprising 7 CP and 7 CT dyads. Each music therapist worked with two or three children. We found that session type, dyadic relationship, frequency-band, and brain-region were significantly related to the degree of interbrain synchronization and its directionality. Particularly, music sessions and CP dyads were associated with higher interbrain synchronization and stronger directionality. Delta (0.5-4 Hz) range showed the highest PLV in both CP and CT dyads in frontal brain regions. It appears that synchronization is directed predominantly from parent to child, i.e. parents and music therapists' brain activity tended to influence a child's. Our findings encourage further research into neural synchrony in children with disabilities, especially in musical contexts, and its implications for social and emotional development.

Untangling the influence of depression on clinical risks: role of leukocytes and somatic symptoms

Background

Depression has been associated with increased hospitalization and mortality risk, especially for cardiovascular causes. We previously found a composite circulating inflammation score (INFLA-score) to explain part of this link, although the role of its component and of depressive symptoms domains in this relationship is unexplored.

Methods

In an Italian population cohort (N = 13,191; age≥35 years; 51.7 % women; 4,856 hospitalizations and 471 deaths, median follow-up 7.28/8.24 years), we estimated the proportion of association explained by C-reactive protein levels (CRP), platelet count, granulocyte-to-lymphocyte ratio (GLR) and white blood cell counts (WBC), in multivariable Cox regressions modelling first hospitalization/mortality for all and cardiovascular (CVD), ischemic heart (IHD) and cerebrovascular disease (CeVD) causes vs depression severity based on an alternative validated version of PHQ-9. We also estimated the proportion of association explained by INFLA-score in the associations of polychoric factors tagging somatic and cognitive depressive symptoms with clinical risks.

Results

In models adjusted for age, sex and education, significant proportions of the positive influence of depression on clinical risks were explained by CRP (4.8% on IHD hospitalizations), GLR (11% on all-cause mortality) and WBC (24% on IHD/CeVD hospitalizations). Stable associations of somatic but not of cognitive depressive symptoms were observed with increased hospitalization risk (+16% for all causes, +14% for CVD causes), with INFLA-score again explaining small but significant proportions of these associations (2.5% for all causes, 8.6% for IHD causes).

Conclusions

These findings suggest a prominent explanatory role of leukocytes in the link between depression and clinical (especially CVD) risks, and highlight the importance of inflammation in the influence of somatic depressive symptoms. Therefore, acting on these factors may reduce clinical risks associated with depression.

Key messages

A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence

Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8–14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

Does music induce interbrain synchronization between a non-speaking youth with cerebral palsy (CP), a parent, and a neurologic music therapist? A brief report

Shared emotional experiences during musical activities among musicians can be coupled with brainwave synchronization. For non-speaking individuals with CP, verbal communication may be limited in expressing mutual empathy. Therefore, this case study explored interbrain synchronization among a non-speaking CP (female, 18 yrs), her parent, and a music therapist by measuring their brainwaves simultaneously during four music and four storytelling sessions. In only the youth-parent dyad, we observed a significantly higher level of interbrain synchronization during music rather than story-telling condition. However, in both the youth-parent and youth-therapist dyad, regardless of condition type, significant interbrain synchronization emerged in frontal and temporal lobes in the low-frequency bands, which are associated with socio-emotional responses. Although interbrain synchronization may have been induced by multiple factors (e.g., external stimuli, shared empathetic experiences, and internal physiological rhythms), the music activity setting deserves further study as a potential facilitator of neurophysiological synchrony between youth with CP and caregivers/healthcare providers.

Automatic pain assessment on cancer patients using physiological signals recorded in real-world contexts

Pain assessment represents the first fundamental stage for proper pain management, but currently, methods applied in clinical practice often lack in providing a satisfying characterization of the pain experience. Automatic methods based on the analysis of physiological signals (e.g., photoplethysmography, electrodermal activity) promise to overcome these limitations, also providing the possibility to record these signals through wearable devices, thus capturing the physiological response in everyday life. After applying preprocessing, feature extraction and feature selection methods, we tested several machine learning algorithms to develop an automatic classifier fed with physiological signals recorded in real-world contexts and pain ratings from 21 cancer patients. The best algorithm achieved up to 72% accuracy. Although performance can be improved by enlarging the dataset, preliminary results proved the feasibility of assessing pain by using physiological signals recorded in real-world contexts.

A comparison and classification of oscillatory characteristics in speech perception and covert speech

Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes were conducted to determine statistical differences in frequency and time (t-CWT). Features were also extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within and between tasks. All binary classifications produced accuracies significantly greater (80-90%) than chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception task dynamically invoked all frequencies with more prominent θ and α activity, the covert task favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, supports the notion that the γ- and θ-bands subserve, respectively, shared and unique encoding processes across tasks.

Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review

Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis

AIM

To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants.

METHOD

We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models.

RESULTS

Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68-0.77) and 0.70 (95% CI 0.65-0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74-100) and 91% (95% CI 83-93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80-0.93).

INTERPRETATION

Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

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.

Customized Access Technology for Children using Head Movement Recognition

Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.6% in predicting head movements.

Surface-Confined Macrocyclization via Dynamic Covalent Chemistry

Surface-confined synthesis is a promising approach to build complex molecular nanostructures including macrocycles. However, despite the recent advances in on-surface macrocyclization under ultrahigh vacuum, selective synthesis of monodisperse and multicomponent macrocycles remains a challenge. Here, we report on an on-surface formation of [6 + 6] Schiff-base macrocycles via dynamic covalent chemistry. The macrocycles form two-dimensional crystalline domains on the micrometer scale, enabled by dynamic conversion of open-chain oligomers into well-defined ∼3.0 nm hexagonal macrocycles. We further show that by tailoring the length of the alkyl substituents, it is possible to control which of three possible products-oligomers, macrocycles, or polymers-will form at the surface. In situ scanning tunneling microscopy imaging combined with density functional theory calculations and molecular dynamics simulations unravel the synergistic effect of surface confinement and solvent in leading to preferential on-surface macrocyclization.

Exploring the capabilities of optical pump X-ray probe NEXAFS spectroscopy to track photo-induced dynamics mediated by conical intersections

In the present contribution we introduce an accurate theoretical approach for the simulation of NEXAFS spectra of organic molecules, employing azobenzene as a test case.

Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis

The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.

Molecular organizations of conical mesogenic fullerenes

We have studied liquid crystal phases formed by fullerenes functionalized with mesogenic groups yielding a cone-shaped molecular structure. We have modelled these shuttlecock-like molecules with a set of Gay-Berne particles grafted with flexible springs to a spherical core and we have studied, using Monte Carlo simulations, their phase organization, also with a view to examining their possible use as candidate organic photovoltaic materials. We have found that, upon cooling from the isotropic phase, the system forms a columnar phase, like in the experimental work of Kato and coworkers [T. Kato et al., Nature, 2002, 419, 702]. However the phase is made of polar stacks extending not more than about ten molecules, which could limit their usefulness in enhancing and directing charge transport for possible photovoltaic applications.

A novel approach to automatically quantify the level of coincident activity between EMG and MMG signals

Although previous studies have highlighted both similarities and differences between the timing of electromyography (EMG) and mechanomyography (MMG) activities of muscles, there is no method to systematically quantify the temporal alignment between corresponding EMG and MMG signals. We proposed a novel method to determine the level of coincident activity in quasi-periodic MMG and EMG signals. The method optimizes 3 muscle-specific parameters: amplitude threshold, window size and minimum percent of EMG and MMG overlap using a particle swarm optimization algorithm to maximize the agreement (balanced accuracy) between electrical and mechanical muscle activity. The method was applied to bilaterally recorded EMG and MMG signals from 4 lower limb muscles per side of 25 pediatric participants during self-paced gait. Mean balanced accuracy exceeded 75% for all muscles except the lateral gastrocnemius, where EMG and MMG misalignment was notable (56% balanced accuracy). The proposed method can be applied to the criterion-driven comparison of simultaneously recorded myographic signals from two different measurement modalities during a motor task.

Phonetic analysis during treatment with rapid maxillary expander

OBJECTIVES

To investigate possible changes and/or device-related impairments in phonetic habits produced by rapid maxillary expansion (RME).

MATERIALS AND METHODS

Thirty-five patients scheduled for RME were divided into two groups: Group A (banded two-arm Hyrax) and Group B (banded four-arm Hyrax). Speech samples were collected at six time points, before, during and after RME removal. Acoustical analysis was performed using PRAAT and BioVoice analysis tools. Ten volunteers completed a questionnaire on the acceptability of patient's speech. Maxillary dimensions and palatal volume were measured on dental casts before and after expansion using a digital gauge.

RESULTS

Voice analysis showed an increase in the peak frequency of fricative consonants (/s/,/ʃ/) after expansion, whereas there was no change of formant frequencies of palatal consonants (/ɲ/,/ʎ/). Vowel /i/ displayed a lowering of the first formant frequency, and an increase in the second and third formant frequencies. After bonding, Group B showed both a greater reduction in the peak frequency of fricatives and a greater increase in the formant frequencies of palatal consonants than Group A.

CONCLUSION

Rapid maxillary expansion causes a slight phonetic change in the acoustical parameters of both consonants and vowels. The two-arm Hyrax caused less speech impairment than the four-arm Hyrax during the treatment.

Doping liquid crystals with nanoparticles. A computer simulation of the effects of nanoparticle shape

Molecular-scale Monte Carlo simulations of liquid crystal-nanoparticle dispersions show the effect on the orientational order and on the clearing temperature of shape and concentration of the dopant nanoparticles.

Markerless Analysis of Articulatory Movements in Patients With Parkinson's Disease

OBJECTIVES

A large percentage of patients with Parkinson's disease have hypokinetic dysarthria, exhibiting reduced peak velocities of jaw and lips during speech. This limitation implies a reduction of speech intelligibility for such patients. This work aims at testing a cost-effective markerless approach for assessing kinematic parameters of hypokinetic dysarthria.

STUDY DESIGN

Kinematic parameters of the lips are calculated during a syllable repetition task from 14 Parkinsonian patients and 14 age-matched control subjects.

METHODS

Combining color and depth frames provided by a depth sensor (Microsoft Kinect), we computed the three-dimensional coordinates of main facial points. The peak velocities and accelerations of the lower lip during a syllable repetition task are considered to compare the two groups.

RESULTS

Results show that Parkinsonian patients exhibit reduced peak velocities of the lower lip, both during the opening and the closing phase of the mouth. In addition, peak values of acceleration are reduced in Parkinsonian patients, although with significant differences only in the opening phase with respect to healthy control subjects.

CONCLUSIONS

The novel contribution of this work is the implementation of an entirely markerless technique capable to detect signs of hypokinetic dysarthria for the analysis of articulatory movements during speech. Although a large number of Parkinsonian patients have hypokinetic dysarthria, only a small percentage of them undergoes speech therapy to increase their articulatory movements. The system proposed here could be easily implemented in a home environment, thus, increasing the percentage of patients who can perform speech rehabilitation at home.

Automatic Assessment of Acoustic Parameters of the Singing Voice: Application to Professional Western Operatic and Jazz Singers

INTRODUCTION

The obvious perceptual differences between various singing styles like Western operatic and jazz rely on specific dissimilarities in vocal technique. The present study focuses on differences in vibrato acoustics and in singer's formant as analyzed by a novel software tool, named BioVoice, based on robust high-resolution and adaptive techniques that have proven its validity on synthetic voice signals.

MATERIAL AND METHODS

A total of 48 professional singers were investigated (29 females; 19 males; 29 Western operatic; and 19 jazz). They were asked to sing "a cappella," but with artistic expression, a well-known musical phrase from Gershwin's Porgy and Bess, in their own style: either operatic or jazz. A specific sustained note was extracted for detailed vibrato analysis. Beside rate (s(-1)) and extent (cents), duration (seconds) and regularity were computed. Two new concepts are introduced: vibrato jitter and vibrato shimmer, by analogy with the traditional jitter and shimmer of voice signals. For the singer's formant, on the same sustained tone, the ratio of the acoustic energy in formants 1-2 to the energy in formants 3, 4, and 5 was automatically computed, providing a quality ratio (QR).

RESULTS

Vibrato rates did not differ among groups. Extent was significantly larger in operatic singers, particularly females. Vibrato jitter and vibrato shimmer were significantly smaller in operatic singers. Duration of vibrato was also significantly longer in operatic singers. QR was significantly lower in male operatic singers.

CONCLUSIONS

Some vibrato characteristics (extent, regularity, and duration) very clearly differentiate the Western operatic singing style from the jazz singing style. The singer's formant is typical of male operatic singers. The new software tool is well suited to provide useful feedback in a pedagogical context.

Automated tracking of quantitative parameters from single line scanning of vocal folds: a case study of the 'messa di voce' exercise

This article presents a novel application of the 'single line scanning' of the vocal fold vibrations (kymography) in singing pedagogy, particularly in a specific technical voice exercise: the 'messa di voce'. It aims at giving the singer relevant and valid short-term feedback. A user-friendly automatic analysis program makes possible a precise, immediate quantification of the essential physiological parameters characterizing the changes in glottal impedance, concomitant with the progressive increase and decrease of the lung pressure. The data provided by the program show a strong correlation with the hand-made measurements. Additional measurements such as subglottic pressure and flow glottography by inverse filtering can be meaningfully correlated with the data obtained from the kymographic images.

Automatic newborn cry analysis: a non-invasive tool to help autism early diagnosis

Autism Spectrum Disorders (ASD) are complex developmental disabilities that cause problems with social interaction and communication. ASD are associated with motor development problems, more or less blurred, and with perceptual and sensory brain areas. Crying is the infant's earliest form of communication and recent studies connect this original "language" form with autism disturbs. Being completely non-invasive, cry analysis is an appealing approach for early ASD diagnosis to improve rehabilitation. To this aim, we have developed an automatic system to record newborn cry and movements, during the first six months of life with a specific recording protocol. In this work we present first results of acoustic cry analysis in newborns classified as high-risk subjects being siblings of children already diagnosed as autistics. The work aims at finding possible early ASD signs in high-risk subjects as compared to a group of control subjects based on the fundamental frequency and the vocal tract resonance frequencies. Also, voiced and unvoiced parts of signal and cry-episodes duration are analyzed.

High-resolution cry analysis in preterm newborn infants

Infant monitoring is a common procedure in clinical practice in neonatal critical care units. A number of vital functions are monitored, such as heart beat, breathing, blood flow, etc. Specifically, preterm and/or low-birth-weight infants often present respiratory problems that require monitoring. These may range from insufficient ventilation to apnoea. One of the most common events that may affect the respiratory flow is crying, a physiological action made by the infant to communicate and draw attention, but, for a preterm infant, this action requires great effort, which may cause distress and even may have an adverse impact on blood oxygenation. Acoustic analysis of newborn infant cry is thus of importance, since it is related to other basic neuro-physiological parameters. Being easy to perform, cheap and completely non-invasive, it can be successfully applied in many circumstances. The newborn infant cry is characterised by very high fundamental frequency (F(0)) and resonance frequency (RFs) values, with abrupt changes and voiced/unvoiced features of very short duration in a single utterance. To deal with such signals, a new user-friendly software tool has been developed, that allows robust tracking of main acoustic parameters on very short and time-varying signal frames. The software developed provides the user with a high-resolution picture of the cry signal characteristics.

Computer simulations of biaxial nematics

Biaxial nematic (N(b)) liquid crystals are a fascinating condensed matter phase that has baffled, for more than thirty years, scientists engaged in the challenge of demonstrating its actual existence, and which has only recently been experimentally found. During this period computer simulations of model N(b) have played an important role, both in providing the basic physical properties to be expected from these systems, and in giving clues about the molecular features essential for the thermodynamic stability of N(b) phases. However, simulation studies are expected to be even more crucial in the future for unravelling the structural features of biaxial mesogens at the molecular level, and for helping in the design and optimization of devices towards the technological deployment of N(b) materials. This review article gives an overview of the simulation work performed so far, and relying on the recent experimental findings, focuses on the still unanswered questions which will determine the future challenges in the field.

Non-invasive distress evaluation in preterm newborn infants

With the increased survival of very preterm infants, there is a growing concern for their developmental outcomes. Infant cry characteristics reflect the development and possibly the integrity of the central nervous system. In this paper, relationships between fundamental frequency (F(0)) and vocal tract resonance frequencies (F(1)-F(3)) are investigated for a set of preterm newborns, by means of a multi-purpose voice analysis tool (BioVoice), characterised by high-resolution and tracking capabilities. Also, first results about possible distress occurring during cry in preterm newborn infants, as related to the decrease of central blood oxygenation, are presented. To this aim, a recording system (Newborn Recorder) has been developed, that allows synchronised, non-invasive monitoring of blood oxygenation and audio recordings of newborn infant's cry. The method has been applied to preterm newborns at the Intensive Care Unit, A.Meyer Children Hospital, Firenze, Italy.