Development of Early Social Interactions in Infants Exposed to Artificial Intelligence from Birth

Research suggests that technology density in a home may change interactions parents and infants in the earliest months of life. This study explored how the use of smart baby technology influenced parental perceptions of development and early social interactions. A qualitative, case methodology was used. The participants in this study were one family with newborn twins. Data was collected over a six month period using journals, field notes, and observations. Thematic coding of these materials was used to answer the questions of the study. Results suggest that use of smart technology supported the emerging parenting skills and allowed the parents to confidently establish care interactions

Learning when out and about

[Introduction] Mobile digital devices such as tablet computers and smartphones (mobile phones that can run apps and access the Internet), enable learners to access learning materials while out and about during their daily lives. This enables networked digital learning to move from beyond the classroom and to become part of everyday routines. Smartphones and tablets are increasingly likely to be the first devices a wide range of people will turn to for technology enhanced learning, incorporated into their everyday activities and carried with them. Learning becomes ubiquitous, making use of ‘domesticated’ technologies that serve a variety of purposes in daily life. In this chapter, we consider how smartphones can trigger location specific learning resources to support adults learning languages when out and about, and consider two projects, MASELTOV and SALSA. There has been increased interest in exploring the potential of ‘smart cities’ – urban environments with high-tech infrastructures – to support learning. We look at an example, the SALSA project, where a combination of smartphones, location-based technologies and learning resources has been used to prompt language learners, and to understand whether this motivates them to extend their learning

Discovering human activities from binary data in smart homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods

Pervasive Technologies and Support for Independent Living

A broad range of pervasive technologies are used in many domains, including healthcare: however, there appears to be little work examining the role of such technologies in the home, or the different wants and needs of elderly users. Additionally, there exist ethical issues surrounding the use of highly personal healthcare-related data, and interface issues centred on the novelty of the technologies and the disabilities experienced by the users. This report examines these areas, before considering the ways in which they might come together to help support independent-living users with disabilities which may be age-related

Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine

Analysis of the office workers’ activities of daily working in an intelligent office environment can be used to optimize energy consumption and also office workers’ comfort. To achieve this end, it is essential to recognise office workers’ activities including short breaks, meetings and non-computer activities to allow an optimum control strategy to be implemented. In this paper, fuzzy finite state machines are used to model an office worker’s behaviour. The model will incorporate sensory data collected from the environment as the input and some pre-defined fuzzy states are used to develop the model. Experimental results are presented to illustrate the effectiveness of this approach. The activity models of different individual workers as inferred from the sensory devices can be distinguished. However, further investigation is required to create a more complete model

‘Look, I have my ears open’: Resilience and early school experiences among children in an economically deprived suburban area in Ireland

Children from economically disadvantaged communities frequently lack the socio- emotional, cognitive and behavioural skills needed for successful early school adjustment. Assessments of early school experience often rely on parent and teacher perspectives, yet children’s views are essential to design effective, resilience-promoting school ecologies. This mixed methods study explored children’s appraisals of potential stressors in the first school year with 25 children from a disadvantaged suburban community in Ireland. School scenarios were presented pictorially (Pictorial Measure of School Stress and Wellbeing, or PMSSW), to elicit children’s perspectives on social ecological factors that enable or constrain resilience. Salient positive factors included resource provision, such as food, toys and books; school activities and routines, including play; and relationships with teachers. Negative factors included bullying; difficulties engaging with peers; and using the toilet. Drawing on these factors, we indicate how school psychologists can develop resilience-fostering educational environments for children in vulnerable communitie

Discovering activity patterns in office environment using a network of low-resolution visual sensors

Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events

MakeSense: An IoT Testbed for Social Research of Indoor Activities

There has been increasing interest in deploying IoT devices to study human behaviour in locations such as homes and offices. Such devices can be deployed in a laboratory or `in the wild' in natural environments. The latter allows one to collect behavioural data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants' daily routines which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this paper, we present MakeSense, an IoT testbed that enables real-world experimentation for large scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed, one in homes and one in offices.Comment: 20 pages, 11 figure