Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and intra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no: MLINI/2015/1

pyphysio: A physiological signal processing library for data science approaches in physiology

The lack of open-source tools for physiological signal processing hinders the development of standardized pipelines in physiology. Researchers usually must rely on commercial software that, by implementing black-box algorithms, undermines the control on the analysis and prevents the comparison of the results, ultimately affecting the scientific reproducibility. We introduce pyphysio as a step towards a data science approach oriented to compute physiological indicators, in particular of the Autonomic Nervous System activity. pyphysio serves as a basis for machine learning modules and it implements a suite of combinable algorithms for processing of signals from either by wearable or medical-grade quality devices. Keywords: Physiological signal processing, Psychophysiology, Autonomic indicators, Data science, Pytho

Clarifying the relationship between alexithymia and subjective interoception

The long-standing hypothesis that emotions rely on bodily states is back in the spotlight. This has led some researchers to suggest that alexithymia, a personality construct characterized by altered emotional awareness, reflects a general deficit in interoception. However, tests of this hypothesis have relied on heterogeneous assessment methods, leading to inconsistent results. To shed some light on this issue, we administered a battery of selfreport questionnaires of interoception and alexithymia to three samples from Italy, the U.S., and Singapore (N = 814). Correlation and machine learning analyses showed that alexithymia was associated with deficits in both subjective interoceptive accuracy and attention. Alexithymics’ interoceptive deficits were primarily related to difficulty identifying and describing feelings. Interoception showed a weaker association with externally-oriented thinking as operationalized by the Toronto Alexithymia Scale (TAS-20) and no association with the affective dimension of alexithymia later introduced by the Bermond-Vorst Alexithymia Questionnaire (BVAQ). We discuss our results with reference to the theoretical and psychometric differences between these two measures of alexithymia and their shortcomings. Overall, our results support the view that interoceptive deficits are a core component of alexithymia, although the latter cannot be reduced to the former

I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece.

OBJECTIVES: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health. STUDY DESIGN: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. METHODS: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. RESULTS: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. CONCLUSIONS: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation

Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual's health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from Wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalisable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). To do so, data from Wave 2 of the UK lockdown (n = 263) was used to conduct a graphical inspection of the week-by-week distribution of self-perceived loneliness scores. In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between weeks 3 and 7 of Wave 1 of the UK national lockdown. Furthermore, although the sample size by week in Wave 2 was too small to have a meaningful statistical insight, a graphical U-shaped distribution between weeks 3 and 9 of lockdown was observed. Consistent with past studies, these preliminary results suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions

Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach

Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers

A data analytics framework for physiological signals from wearable devices

Wearable devices have emerged as the most innovative opportunity to enable acquisition and quantification of physiological signals in real-world indoor or outdoor contexts. However, their use in research should be based on a reproducible analytics process, ensuring that all the critical steps in data collection and processing are managed in a reliable experimental setup. The aim of this thesis is to investigate the actual value and technical limitations of wearable devices for their use in a research context, such as physiological monitoring of sleep and crying states in infants, of parenting of typical or atypical children, synchrony in educational contexts, and of fatigue patterns in outdoor sport activity, e.g. skiing. The thesis describes an approach and solutions that aim to compensate the effects of such technical limits. Besides providing a set of appropriate signal processing algorithms, a real-life sensing architecture is designed and implemented enabling synchronized acquisition from multiple subjects and multiple sensors, including cardiac signals, electrodermal activity and inertial data streams. The signal processing pipeline and the real-life sensing architecture are merged in a unique data analytics framework (Physiolitix). The framework is validated on a fairly wide range of sensors, including medical quality multi-sensor smartwatches and smart textile garments applied in diverse research contexts. In particular, a calibration dataset is developed to compare wearable and clinical devices in an affective computing task. We found that wearables can be employed as a valid substitute for medical quality devices with the help of adequate signal processing and machine learning solutions

Acquisition and Processing of Brain Signals

We live within a context of unprecedented opportunities for brain research, with a flourishing of novel sensing technologies and methodological approaches [...

Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach

Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers

Caratterizzazione meccanica di tessuti fasciali: analisi sperimentale e modellazione costitutiva

In questo lavoro di tesi è stata data una caratterizzazione meccanica ai tessuti della fascia plantare e crurale. Lo studio ha evidenziato che la risposta meccanica in fase di allungamento è non lineare, in funzione dell'allungamento delle fibre di collagene; i test di rilassamento hanno permesso di ipotizzare una viscosità di tipo lineare. Per il tessuto della fascia plantare è stato formulato un modello costitutivo visco-iperelastico, con somma dei contributi della matrice, delle fibre di collagene e delle tensioni rilassate in tre rami viscosi, e ne sono stati identificati i parametr