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.

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.

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.

Background: In the absence of specific protection, invasive cardiologists receive a high lifetime head exposure, with organ doses in the 1 to 3 Sievert range (equivalent to the risk of 50,000 to 150,000 chest x-rays). Radiation exposure in this dose range can reduce neurogenesis in adult brain and negatively impact on olfactory function, whose impairment is a recognized early biomarker of neurodegenerative disease. Aim: To assess psychological testing results in exposed subjects (ES) working as staff in the Cath Lab compared with non-exposed matched controls (NES). Methods: A complete olfactory test battery (Sniffin' Sticks Extended test) was performed in 130 Cath Lab ES (60 males; age=44.9±8.7 years; 58 interventional cardiologists, 60 nurses, 12 technicians). A control group of age- and gender-matched non-exposed subjects (NES) was also studied. Results: ES showed lower scores in the Odor Threshold test (p=.006), Discrimination test (p=.039), Identification test (p=.005) and TDI (the sum of the previous 3 tests, p<.001), as reported in Figure. Subnormal scores of at least 1 of 3 odor subtests were present in 18/130 ES and 7/130 NES (13.8 vs 5.4%, p<.001). Significant correlations were found between Identification Score and years at Cath Lab (r=-.18, p=.012), this last parameter being also correlated with the overall TDI Score (r=-.16, p=.048). Conclusions: In ES, olfactory testing showed mild impairment of identification (cortical), discrimination (bulb) and threshold (peripheral) function, somewhat correlated with radiation exposure in the Cath Lab. While further evidence is needed at this point, in the meantime head protection should be a mandatory good practice of safety in every Cath Lab.

The incredible advancement in the ICT sector has challenged technology developers, designers, and psychologists to reflect on how to develop technologies to promote mental health.Computerized experience-sampling method appears to be a 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://psychlog.com) a free open-source 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.Within the paradigm of Positive Technology, our innovative approach offers for researchers and clinicians new effective opportunities for the assessment and treatment of the psychological stress in daily situations.

Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user’s experience in real time. In this paper, we describe the design and development of Psychlog, a mobile phone platform designed to collect users’ psychological, physiological, and activity information for mental health research. The tool allows administering self-report questionnaires at specific times or randomly within a day. The system also permits to collect heart rate and activity information from a wireless electrocardiogram equipped with a three-axial accelerometer. By combining self-reports with heart rate and activity data, the application makes it possible to investigate the relationship between psychological, physiological, and behavioral variables, as well as to monitor their fluctuations over time. The software runs on Windows mobile operative system and is available as open source ( http://sourceforge.net/projects/psychlog/ ).

Computerized experience-sampling method comprising a mobilebased 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.

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone's memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).

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.

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system's architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.