Increases in energy, protein and fat intake following the addition of sauce to an older person's meal

Energy intake in 15-20% of the UK older population is currently thought to be inadequate for health. Based on the suggestion that increases in food pleasantness and familiarity can increase intake, this study investigated the impact of the addition of sauce to an older person's meal on subsequent intake. Twenty-eight older people consumed two meals with sauce and the same two meals without sauce on different occasions, and amount consumed in terms of weight, energy and energy consumed from carbohydrate, fat and protein were compared. Pre-meal hunger and desire to eat, post-meal pleasantness and familiarity and participants' expectations of the effects of sauces were also measured. Compared to meals without sauce, meals with sauce were found to result in greater intakes of energy, energy consumed from protein and energy consumed from fat (smallest t(27)=2.13, p=0.04). No differences between conditions were found in measures of pre-meal hunger and desire to eat, or post-meal pleasantness and familiarity (largest t(27) = 1.47, p = 0.15). Similar effects were also found when participant expectations were taken into account, and no differences between participants who expected sauces to affect intake vs. those who did not expect sauces to affect intake were found (largest F(1, 26) = 1.70, p=0.20). These findings suggest that the addition of sauce to an older person's meal can result in increases in intake and may be beneficial for preventing or treating under-nutrition in these individuals, although the mechanisms by which sauces can increase intake are unlikely to be related to pleasantness and familiarity. (c) 2008 Elsevier Ltd. All rights reserved.

The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent

SEMAINE has created a large audiovisual database as a part of an iterative approach to building Sensitive Artificial Listener (SAL) agents that can engage a person in a sustained, emotionally colored conversation. Data used to build the agents came from interactions between users and an operator simulating a SAL agent, in different configurations: Solid SAL (designed so that operators displayed an appropriate nonverbal behavior) and Semi-automatic SAL (designed so that users' experience approximated interacting with a machine). We then recorded user interactions with the developed system, Automatic SAL, comparing the most communicatively competent version to versions with reduced nonverbal skills. High quality recording was provided by five high-resolution, high-framerate cameras, and four microphones, recorded synchronously. Recordings total 150 participants, for a total of 959 conversations with individual SAL characters, lasting approximately 5 minutes each. Solid SAL recordings are transcribed and extensively annotated: 6-8 raters per clip traced five affective dimensions and 27 associated categories. Other scenarios are labeled on the same pattern, but less fully. Additional information includes FACS annotation on selected extracts, identification of laughs, nods, and shakes, and measures of user engagement with the automatic system. The material is available through a web-accessible database. © 2010-2012 IEEE.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.

Device-Free Person Detection and Ranging in UWB Networks

We present a novel device-free stationary person detection and ranging method, that is applicable to ultra-wide bandwidth (UWB) networks. The method utilizes a fixed UWB infrastructure and does not require a training database of template waveforms. Instead, the method capitalizes on the fact that a human presence induces small low-frequency variations that stand out against the background signal, which is mainly affected by wideband noise. We analyze the detection probability, and validate our findings with numerical simulations and experiments with off-the-shelf UWB transceivers in an indoor environment. © 2007-2012 IEEE.

An experimental study of UWB device-free person detection and ranging

Passive person detection and localization is an emerging area in UWB localization systems, whereby people are not required to carry any UWB ranging device. Based on experimental data, we propose a novel method to detect static persons in the absence of template waveforms, and to compute distances to these persons. Our method makes very little assumptions on the environment and can achieve ranging performances on the order of 50 cm, using off-the-shelf UWB devices. © 2013 IEEE.