Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs

Cytogenetic and Molecular Predictors of Outcome in Acute Lymphocytic Leukemia: Recent Developments

During the last decade a tremendous technologic progress based on genome-wide profiling of genetic aberrations, structural DNA alterations, and sequence variations has allowed a better understanding of the molecular basis of pediatric and adult B/T- acute lymphoblastic leukemia (ALL), contributing to a better recognition of the biological heterogeneity of ALL and to a more precise definition of risk factors. Importantly, these advances identified novel potential targets for therapeutic intervention. This review will be focused on the cytogenetic/molecular advances in pediatric and adult ALL based on recently published articles

Data Collection Platforms for Mental Health Research

The modern society exposes the individual to a lifestyle often frantic. This turn in a persistent harmful state of stress for the body. It hasn’t evolved to be subjected at persistent stressful situations. For this reason, in recent years there has been an increase in diseases due to stress. An example are cadiovascular pathologies. The aim of my PhD was to validate and provide tools to assess (coming from physiologic signals) the person’s Level of Stress during everyday life. This information if acquired noninvasively (without annoy users) is fundamental in treatment, thanks to Cognitive Behavioural Therapy (CBT) of patients with pathological stress situations. It’s also very important in prevention, giving to users real-time feedback on their own situation, and through special techniques of concentration and relaxation. It’s demostrated how this treatments help to normalize parameters that indicate a high level of mental stress. To accomplish this result various steps needed. The first was to find out together with experts, State-of-the-art method that could be used to collect preliminary data. The literature suggested to look at Experience Sampling Method, a method which gave a better ability to contextualize Psychological data during the ordinary life. I chose to go beyond the State of the art, by implementing the first application for Smartphone capable to gathering Psychological and releted Physiological informations (ECG-Accelerometer). Developed application called PsychLog. Meanwhile PsychLog was realized, it was necessary to choose most suitable sensor for data acquisition. Moreover it could be able connect Smartphone. The chosen sensor was Shimmer2 ®. The reasons were, its dimensions, reduced weight and quality of the signal. Next step was the validation of whole Smartphone + Wearable Sensor System. ECG data were collected at 250 Hz, a lower sample-rate than 1000 Hz (frequency of a clinical Holter). It was therefore necessary to find out important differences between tachograms obtained by both devices. It’s well known from literature that fatures that better describe autonomic system (and therefore more stress-related) are from tachogram signal (differences between ECG RR spikes). First of all it was necessary to evaluate the QRS detection algorithm performance, implemented inside the Smartphone. Two subjects dressed both Holter and Sensor simultaneously. Sensitivity and Specificity were calculated (of detected beats), achieving very high values for both (Sens = 99.97% and Spec = 99.94%). Furthermore Relative Error Percentage was evaluated according to CEI 2-47 ISO60601-2-47. Error has been always lower than the 10% (maximum admitted). It was also evaluated the absence of any Bias between Holter and sensor data. This first described stage has been useful to evaluate the QRS detection algorithm (fundamental to calculate the most important signal where to extract stress-related features). I did also an evaluation of power Spectral Density (PSD) from both Holter and Sensor Tachogram. The measured error presents the following statistic: Subj1 (m = 0.0839% σ = 0.3143%) and Subj2 (m = 0.3457% σ = 0.1435%). The PSD from sensor has to be as close as possible to Holter’s PSD, because tachogram is used as source of features both in the time and frequency domain. These analyses demostrated that the Wearable + Smartphone System was suitable to address a bigger measure campaing. A bigger data collection campaign was realized in order to evaluate physiological and psychological data correlation. In particular if the Psychological data could be used as Label for Physiological. Considered people belonged at two professional categories (Nurses and Teachers). Emotional state information is gained through questionnaires presented by the Smartphone. It was important to look at the possibility to reduce number of questions, and use new scores for stress level’s assessment, without losing information of gold standard scores (all proposed questionnaires was deliveded by expert clinicians). Six subjects’ data was collected for a week each. Auto-Perceived Stress and Gold Standard Indexes showed a positive correlation (R = 0.348) that accoding to clinitians opinion is a good result. Moreover I did a deeper analysis for a particular subject, This showed as the ratio (LF/HF) had an inversely proportional trend respect level of stress, as reported in literature. The next step was to create a large measure campaign. The sessions considered for subsequent evaluations were 726. With collected data I wanted to achieve an automatic decision model for Stress Level’s evaluation from physiological data (avoiding wasted time to fill questionnaires). The literature proves that most important features were 15 (9 in the time domain and 6 in frequency domain). Not all that features could be used as inputs for automatic decision model. It was therefore necessary to select the most important ones. In First step I chose to apply the ReliefF method (choosen for its higher robustness against noise in data). In this way I got a ranking of features’ importance about classification. The next step was to choose the correct number of features. The Davies-Bauldin Index was calculated iterating number of features (each step added a new one), having trend of this parameter. Davies-Bauldin Index has not been used as an index of classification’s quality, but as a discriminator of most important features within the data set. Lower value was obtained with number of features amounting 6. The features are selected and the final step was to define the model that best categorize collected data (DSS). The features were used as input to different classifiers. The number of defined classes was 4 (No Stress, Low, Medium, High Stress). These features have been normalized in linear way. By Leave One Subject Out Cross Validation the K-nearest got a misclassification of 17.31%. To improve the classification, I decided to use a different approach at the standardization of the incoming data. Variables were fuzzyfied. For each feature, I defined 3 Membership Functions, under guidance of medical staff and looking at literature, obtaining, de facto 3 new features for each parameter. I again tested each classifier. The only one that showed an improvement was the SOM (Kohonen Self Organizing Map), obtaining an average classification error equal to 11.83%, with capability to discriminate between the 4 defined classes, close to 90%. The last step was to implement a Server Side application able to connect to Database through WSDL functions. At this point I created a prediction model for Stress Level within a hardware/software architecture, and customized for each user. The functioning is divided into 2 parts. The Training stage is one week long, and mobile device acquires both physiological and psychological data. These data are saved in the DB through WSDL functions. Meantime DSS system reads the data and trains the SOM. Then there is the Test phase. For each session measurement,the system automatically calculates Level of Stress from physiological data, and notifies the user his Level of Stress The system, validated and developed, can be used for many purposes. One example is aggregated data collector. This work for the first time combines behavioral and autonomic system information. If used as a Black Box, the system can convey a therapy aimed to lowering stress. Moreover this work represents the starting point for a research thread in the field of Correlations between Mind and Autonomic System, thanks to the large amount of data that has been recorded and analyzed. The architecture is easily extensible and a challenge for future is the introduction of new features gained with less invasive systems in order to realize more precise knowledge based model

Dispositivi pervasivi wireless per la diagnosi e prevenzione cardiovascolare e terapie personalizzate

Nel presente lavoro di tesi mi sono occupato di progettare, sviluppare e testare un sistema pervasivo personale che consentisse il monitoraggio continuo di pazienti cardiopatici in collaborazione con i ricercatori dell’Istituto di Fisiologia Clinica del CNR di Pisa. Il sistema si compone di una suite di applicazioni, una realizzata per un dispositivo Mobile, e l’altra per una stazione base rappresentata da un Tablet o da un comune PC. I segnali ECG e accelerometrico sono campionati con due dispositivi General Pourpose (al torace acquisisce sia ECG che accelerazioni, e alla gamba solamente le accelerazioni) e inviati tramite standard Bluetooth ad un sistema fisso (Stazione di Monitoraggio/PC) oppure ad uno mobile (Sistema di Acquisizione Portatile). Avendo realizzato il sistema di acquisizione dei segnali, si è passati alla fase di post processing dei dati acquisiti. È stato realizzato un Data Set di 14 sani e 19 Infartuati. Il sistema in automatico restituisce il Tacogramma di ogni acquisizione e si è cercato di discriminare le due classi di soggetti a partire da esso

Decision Support Processing Architecture

This report presents the design and im-plementation of the INTERSTRESS Deci-sion Support System (DSS). The goal of the DSS is to assess the psychological state of each patient by analyzing the previously acquired knowledge, such as patient\u27s physiological and behavioural profile, and current sensory data. Starting from such information, the DSS then infers physiological and behavioural markers of stress

Personal Health System architecture for stress monitoring and support to clinical decisions

Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in Personal Health Systems. Pervasive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, personalized and cost efficient pervasive architecture for the evaluation of the stress state of individual subjects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic modeling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions

A decision support system for real-time stress detection during virtual reality exposure

Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface

An Event-Driven Psychophysiological Assessment for Health Care

Computerized experience-sampling method comprising a mobile based system that collects psychophysiological data appears to be a very promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://sourceforge.net/projects/psychlog/) a free opensource mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Our innovative approach offers to researchers and clinicians new effective opportunities to assess and treat psychological stress in daily-life environments