The effect of load characteristics on multi-machine electric power system dynamic stability

Imperial Users onl

Stress detection using wearable physiological and sociometric sensors

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection

Using mobile devices and apps to support reflective learning about older people with dementia

There has been little research to develop computing technologies to support the care of people with dementia, in spite of the growing challenges that the condition poses for society. To design such technologies, an existing model of computer-support reflective learning was instantiated with findings from a pre-design study in one residential home. The result was a mobile device running an adapted enterprise social media app to support person-centred care. Evaluations of the device and app in two residential homes revealed that use of the app both motivated and increased different styles of care note recording, but little reflective learning was identified or reported. The results suggest the need for more comprehensive and flexible computer-based support for reflective learning about residents in their care – and new designs of this more comprehensive support are also introduced

The relationship of bottle feeding and other sucking behaviors with speech disorder in Patagonian preschoolers

<p>Abstract</p> <p>Background</p> <p>Previous studies have shown that children's nonnutritive sucking habits may lead to delayed development of their oral anatomy and functioning. However, these findings were inconsistent. We investigated associations between use of bottles, pacifiers, and other sucking behaviors with speech disorders in children attending three preschools in Punta Arenas (Patagonia), Chile.</p> <p>Methods</p> <p>Information on infant feeding and sucking behaviors, age starting and stopping breast- and bottle-feeding, pacifier use, and other sucking behaviors, was collected from self-administered questionnaires completed by parents. Evaluation of speech problems was conducted at preschools with subsequent scoring by a licensed speech pathologist using age-normative standards.</p> <p>Results</p> <p>A total of 128 three- to five-year olds were assessed, 46% girls and 54% boys. Children were breastfed for an average of 25.2 (SD 9.6) months and used a bottle 24.4 (SD 15.2) months. Fifty-three children (41.7%) had or currently used a pacifier for an average of 11.4 (SD 17.3) months; 23 children (18.3%) were reported to have sucked their fingers. Delayed use of a bottle until after 9 months appeared to be protective for subsequent speech disorders. There was less than a one-third lower relative odds of subsequent speech disorders for children with a delayed use of a bottle compared to children without a delayed use of a bottle (OR: 0.32, 95% CI: 0.10-0.98). A three-fold increase in relative odds of speech disorder was found for finger-sucking behavior (OR: 2.99, 95% CI: 1.10-8.00) and for use of a pacifier for 3 or more years (OR: 3.42, 95% CI: 1.08-10.81).</p> <p>Conclusion</p> <p>The results suggest extended use of sucking outside of breastfeeding may have detrimental effects on speech development in young children.</p

Mobile Phones and Social Signal Processing for Analysis and Understanding of Dyadic Conversations

Social Signal Processing is the domain aimed at bridging the social intelligence gap between humans and machines via modeling, analysis and synthesis of nonverbal behavior in social interactions. One of the main challenges of the domain is to sense unobtrusively the behavior of social interaction participants, one of the key conditions to preserve the spontaneity and naturalness of the interactions under exam. In this respect, mobile devices offer a major opportunity because they are equipped with a wide array of sensors that, while capturing the behavior of their users with an unprecedented depth, are still invisible. This is particularly important because mobile devices are part of the everyday life of a large number of individuals and, hence, they can be used to investigate and sense natural and spontaneous scenarios