Presenting the networked home: a content analysis of promotion material of Ambient Intelligence applications

Ambient Intelligence (AmI) for the home uses information and communication technologies to make users’ everyday life more comfortable. AmI is still in its developmental phase and is headed towards the first stages of diffusion. \ud Characteristics of AmI design can be observed, among others, in the promotion material of initial producers. A literature study revealed that AmI originally envisioned a central role for the user, convenience that AmI offers them and that attention should be paid to critical policy issues such as privacy and a potential loss of freedom. A content analysis of current promotion material of several high-tech companies revealed that these original ideas are not all reflected in the material. Attributes which were used most in the promotion material were ‘connectedness’, ‘control’, ‘easiness’ and ‘personalization’. An analysis of the pictures in the promotion material showed that almost half of the pictures contained no humans but appliances. These results only partly correspond to the original vision on AmI, since the emphasis is now on technology. The results represent a serious problem, since both users, as well as critical policy issues are underexposed in the current promotion material

Human-Machine Co-Creativity with Older Adults -- A Learning Community to Study Explainable Dialogues

This position paper is part of a long-term research project on human-machine co-creativity with older adults. The goal is to investigate how robots and AI-generated content can contribute to older adults' creative experiences, with a focus on collaborative drawing and painting. The research has recently started, and current activities are centred around literature studies, interviews with seniors and artists, and developing initial prototypes. In addition, a course "Drawing with Robots", is being developed to establish collaboration between human and machine learners: older adults, artists, students, researchers, and artificial agents. We present this course as a learning community and as an opportunity for studying how explainable AI and creative dialogues can be intertwined in human-machine co-creativity with older adults

Out-of-home activity analysis using a low-resolution visual sensor

Loneliness and social isolation are probably the most prevalent psychosocial problems related to aging. One critical component in assessing social isolation in an unobtrusive manner is to measure the out-of-home activity levels, as social isolation often goes along with decreased physical activity, decreased motoric functioning, and a decline in activities of daily living, all of which may lead to a reduction in the amount of time spent out-of-home. In this work, we propose to use a single visual sensor for detecting out-of-home activity. The visual sensor has a very low spatial resolution (900 pixels), which is a key feature to ensure a cheap technology and to maintain the user’s privacy. Firstly, the visual sensor is installed in a top view setup at the door entrance. Secondly, a correlation-based foreground detection method is used to extract the foreground. Thirdly, an Extra Trees Classifier (ETC) is trained to classify the directionality of the person (in/out) based on the motion of the foreground pixels. Due to the nature of variability of the out-of-home activity, the relative frequency of the directionality (in/out) is measured over a window of 3 seconds to determine the final result. We installed our system in 9 different service flats in the UK, Belgium and France where the same ETC model is used. We evaluate our method on video sequences captured in real-life environments from the different setups, where the persons’ out-of-home routines are recorded. The results show that our approach of detecting out-of-home activity achieves an accuracy of 91.30%

Adherence to Blended or Face-to-Face Smoking Cessation Treatment and Predictors of Adherence:Randomized Controlled Trial

Background: Blended face-to-face and web-based treatment is a promising way to deliver smoking cessation treatment. Since adherence has been shown to be an indicator of treatment acceptability and a determinant for effectiveness, we explored and compared adherence and predictors of adherence to blended and face-to-face alone smoking cessation treatments with similar content and intensity. Objective: The objectives of this study were (1) to compare adherence to a blended smoking cessation treatment with adherence to a face-to-face treatment; (2) to compare adherence within the blended treatment to its face-to-face mode and web mode; and (3) to determine baseline predictors of adherence to both treatments as well as (4) the predictors to both modes of the blended treatment. Methods: We calculated the total duration of treatment exposure for patients (N=292) of a Dutch outpatient smoking cessation clinic who were randomly assigned either to the blended smoking cessation treatment (n=130) or to a face-to-face treatment with identical components (n=162). For both treatments (blended and face-to-face) and for the two modes of delivery within the blended treatment (face-to-face vs web mode), adherence levels (ie, treatment time) were compared and the predictors of adherence were identified within 33 demographic, smoking-related, and health-related patient characteristics. Results: We found no significant difference in adherence between the blended and the face-to-face treatments. Participants in the blended treatment group spent an average of 246 minutes in treatment (median 106.7% of intended treatment time, IQR 150%-355%) and participants in the face-to-face group spent 238 minutes (median 103.3% of intended treatment time, IQR 150%-330%). Within the blended group, adherence to the face-to-face mode was twice as high as that to the web mode. Participants in the blended group spent an average of 198 minutes (SD 120) in face-to-face mode (152% of the intended treatment time) and 75 minutes (SD 53) in web mode (75% of the intended treatment time). Higher age was the only characteristic consistently found to uniquely predict higher adherence in both the blended and face-to-face groups. For the face-to-face group, more social support for smoking cessation was also predictive of higher adherence. The variability in adherence explained by these predictors was rather low (blended R-2 =0.049; face-to-face R-2 =0.076). Within the blended group, living without children predicted higher adherence to the face-to-face mode (R-2 =0.034), independent of age. Higher adherence to the web mode of the blended treatment was predicted by a combination of an extrinsic motivation to quit, a less negative attitude toward quitting, and less health complaints (R-2 =0.164). Conclusions: This study represents one of the first attempts to thoroughly compare adherence and predictors of adherence of a blended smoking cessation treatment to an equivalent face-to-face treatment. Interestingly, although the overall adherence to both treatments appeared to be high, adherence within the blended treatment was much higher for the face-to-face mode than for the web mode. This supports the idea that in blended treatment, one mode of delivery can compensate for the weaknesses of the other. Higher age was found to be a common predictor of adherence to the treatments. The low variance in adherence predicted by the characteristics examined in this study suggests that other variables such as provider-related health system factors and time-varying patient characteristics should be explored in future research

Speech Emotion Recognition using Deep Convolutional Neural Networks improved by the fast Continuous Wavelet Transform

The fast Continuous Wavelet Transform (fCWT) is used to improve Deep Convolutional Neural Networks (DCNN)’s Speech Emotion Recognition (SER). While being computationally efficient, the fCWT’s time-frequency analysis overcomes traditional methods’ resolution limitations (e.g., Short-Term Fourier Transform). fCWT-induced DCNNs are compared to state-of-the-art DCNN SER systems. Comparing different wavelet parameters, we also provide an empirical strategy for balancing temporal and spectral features in speech signals. We suggest that this strategy is of generic interest for non-stationary signal processing where large amounts of data are available. fCWT’s potential for improving SER accuracy in real-time applications is confirmed. In parallel, the variance in the cross-validation folds confirmed deep learning’s vulnerability on non-big data sets

Shaping digital life:technology that cares

Constante vernieuwingen op het gebied van digitale technologie in zowel prive- als publieke omge­vingen kenmerken de huidige samenlevingen en benadrukken de alsmaar belangrijker wordende rol daarin voor socio-technische systemen. De uitdaging is om voorafgaand, tijdens en na de ontwikkeling van deze systemen de mens en zijn digitale, sociale en fysieke omgeving centraal te blijven stellen. Dit vraagt om een duidelijk inzicht in de behoeften, wensen en eisen van mensen, zodat deze vertaald kunnen worden naar digitale technologie die een positieve bijdrage levert aan gezondheid, welzijn en participatie. Toegepast onderzoek naar het ontwerp- en appropriatieproces van digitale technologie voor maatschappelijk welbevinden is nodig om succesvol gebruik, implementatie, evaluatie en opschaling te bevorderen. Een goede samenwerking en beter begrip van elkaars doelen, motie­ven en werkwijzen, tussen zowel ontwerpers en gebruikers als tussen onderzoek, onderwijs en het werkveld, zijn hiervoor absolute vereisten. Lector Digital Life dr. Somaya Ben Allouch pleit in haar rede voor meer aandacht voor een mensgerichte ontwerpaanpak van digitale technologie. Het onderzoek van het lectoraat richt zich op innovatieve, digitale technologie op het gebied van mens-systeem interactie, sensoren en hun data en creatieve methodes voor gezondheid, welzijn en participatie. In de rede zal nader worden ingegaan op hoe het onderzoek van het lectoraat Digital Life bijdraagt aan het (inter)nationale netwerk van onderzoek, onderwijs en praktijk op het snijvlak van technologie, gezondheid en welzijn

Listening to the ones who care: exploring the perceptions of informal caregivers towards ambient assisted living applications

Ambient assisted living (AAL) technologies have received increased attention from government, industry and research. Informal caregivers will be directly affected by the use of these technologies and are likely to be key influencers in the adoption decision of older adults. However, so far the informal caregivers’ perceptions, concerns and needs have been mostly overlooked in AAL research. To address these shortcomings, two studies were conducted. Study I consisted of 20 in-depth interviews with informal caregivers to investigate their perception of various AAL applications. In Study II these findings were validated with regard to our own prototype application called SONOPA. The second study included couples of informal caregivers and care receivers to compare both user groups. Although informal caregivers had a more positive attitude than care receivers and appreciated the opportunities of AAL technologies (e.g., increased safety, peace of mind); they also had several concern such as invading the care receiver’s privacy, the lack of human touch, and the care receiver’s technology experience. To address these concerns, informal caregivers should be more involved when developing AAL applications

Always connected: a longitudinal field study of mobile communication

Twenty-five novice users of a new mobile communication device were closely tracked for a period of three months. The results of this longitudinal field study show that people’s motivations for using mobile communication technology are initially influenced more strongly by their perceptions about the expected use, which is more task-oriented. Over time, due to the quick habituation of the new mobile communication device important, initial gratifications, like permanent access and social interaction, appear to be less manifest reasons for using the mobile communication device and become more latent, while gratifications like fashion/status and entertainment appear to become more dominant. Moreover, the boundary between work and personal life slowly disappears as people can easily use mobile communication technology simultaneously for personal and business purposes in both social and work-related contexts