Ethical Considerations for Movement Mapping to Identify Disease Transmission Hotspots.

Traditional public health methods for detecting infectious disease transmission, such as contact tracing and molecular epidemiology, are time-consuming and costly. Information and communication technologies, such as global positioning systems, smartphones, and mobile phones, offer opportunities for novel approaches to identifying transmission hotspots. However, mapping the movements of potentially infected persons comes with ethical challenges. During an interdisciplinary meeting of researchers, ethicists, data security specialists, information and communication technology experts, epidemiologists, microbiologists, and others, we arrived at suggestions to mitigate the ethical concerns of movement mapping. These suggestions include a template Data Protection Impact Assessment that follows European Union General Data Protection Regulations.

Gelotophobia and the challenges of implementing laughter into virtual agents interactions.

This study investigated which features of AVATAR laughter are perceived threatening for individuals with a fear of being laughed at (gelotophobia), and individuals with no gelotophobia. Laughter samples were systematically varied (e.g., intensity, laughter pitch, and energy for the voice, intensity of facial actions of the face) in three modalities: animated facial expressions, synthesized auditory laughter vocalizations, and motion capture generated puppets displaying laughter body movements. In the online study 123 adults completed, the GELOPH <15 > (Ruch and Proyer, 2008a,b) and rated randomly presented videos of the three modalities for how malicious, how friendly, how real the laughter was (0 not at all to 8 extremely). Additionally, an open question asked which markers led to the perception of friendliness/maliciousness. The current study identified features in all modalities of laughter stimuli that were perceived as malicious in general, and some that were gelotophobia specific. For facial expressions of AVATARS, medium intensity laughs triggered highest maliciousness in the gelotophobes. In the auditory stimuli, the fundamental frequency modulations and the variation in intensity were indicative of maliciousness. In the body, backwards and forward movements and rocking vs. jerking movements distinguished the most malicious from the least malicious laugh. From the open answers, the shape and appearance of the lips curling induced feelings that the expression was malicious for non-gelotophobes and that the movement round the eyes, elicited the face to appear as friendly. This was opposite for gelotophobes. Gelotophobia savvy AVATARS should be of high intensity, containing lip and eye movements and be fast, non-repetitive voiced vocalization, variable and of short duration. It should not contain any features that indicate a down-regulation in the voice or body, or indicate voluntary/cognitive modulation.

Objective study of sensor relevance for automatic cough detection.

The development of a system for the automatic, objective, and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently reported solutions achieving this task with a relative success, there is still no standardization about the method to adopt or the sensors to use. The goal of this paper is to study objectively the performance of several sensors for cough detection: ECG, thermistor, chest belt, accelerometer, contact, and audio microphones. Experiments are carried out on a database of 32 healthy subjects producing, in a confined room and in three situations, voluntary cough at various volumes as well as other event categories which can possibly lead to some detection errors: background noise, forced expiration, throat clearing, speech, and laugh. The relevance of each sensor is evaluated at three stages: mutual information conveyed by the features, ability to discriminate at the frame level cough from these latter other sources of ambiguity, and ability to detect cough events. In this latter experiment, with both an averaged sensitivity and specificity of about 94.5%, the proposed approach is shown to clearly outperform the commercial Karmelsonix system which achieved a specificity of 95.3% and a sensitivity of 64.9%.

Algorithms for sleep-wake identification using actigraphy: a comparative study and new results.

The aim of this study was to investigate two new scoring algorithms employing artificial neural networks and decision trees for distinguishing sleep and wake states in infants using actigraphy and to validate and compare the performance of the proposed algorithms with known actigraphy scoring algorithms. The study employed previously recorded longitudinal physiological infant data set from the Collaborative Home Infant Monitoring Evaluation (CHIME) study conducted between 1994 and 1998 [http://dccwww.bumc.bu.edu/ChimeNisp/Main_Chime.asp; Sleep26 (1997) 553] at five clinical sites around the USA. The original CHIME data set contains recordings of 1079 infants <1 year old. In our study, we used the overnight polysomnography scored data and ankle actimeter (Alice 3) raw data for 354 infants from this data set. The participants were heterogeneous and grouped into four categories: healthy term, preterm, siblings of SIDS and infants with apparent life-threatening events (apnea of infancy). The selection of the most discriminant actigraphy features was carried out using Fisher's discriminant analysis. Approximately 80% of all the epochs were used to train the artificial neural network and decision tree models. The models were then validated on the remaining 20% of the epochs. The use of artificial neural networks and decision trees was able to capture potentially nonlinear classification characteristics, when compared to the previously reported linear combination methods and hence showed improved performance. The quality of sleep-wake scoring was further improved by including more wake epochs in the training phase and by employing rescoring rules to remove artifacts. The large size of the database (approximately 337,000 epochs for 354 patients) provided a solid basis for determining the efficacy of actigraphy in sleep scoring. The study also suggested that artificial neural networks and decision trees could be much more routinely utilized in the context of clinical sleep search.