Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
Background and purpose The aim of this study is to investigate changes in movement behaviors, sedentary behavior and physical activity, and to identify potential movement behavior trajectory subgroups within the first two months after discharge from the hospital to the home setting in first-time stroke patients. Methods A total of 140 participants were included. Within three weeks after discharge, participants received an accelerometer, which they wore continuously for five weeks to objectively measure movement behavior outcomes. The movement behavior outcomes of interest were the mean time spent in sedentary behavior (SB), light physical activity (LPA) and moderate to vigorous physical activity (MVPA); the mean time spent in MVPA bouts ≥ 10 minutes; and the weighted median sedentary bout. Generalized estimation equation analyses were performed to investigate overall changes in movement behavior outcomes. Latent class growth analyses were performed to identify patient subgroups of movement behavior outcome trajectories. Results In the first week, the participants spent an average, of 9.22 hours (67.03%) per day in SB, 3.87 hours (27.95%) per day in LPA and 0.70 hours (5.02%) per day in MVPA. Within the entire sample, a small but significant decrease in SB and increase in LPA were found in the first weeks in the home setting. For each movement behavior outcome variable, two or three distinctive subgroup trajectories were found. Although subgroup trajectories for each movement behavior outcome were identified, no relevant changes over time were found. Conclusion Overall, the majority of stroke survivors are highly sedentary and a substantial part is inactive in the period immediately after discharge from hospital care. Movement behavior outcomes remain fairly stable during this period, although distinctive subgroup trajectories were found for each movement behavior outcome. Future research should investigate whether movement behavior outcomes cluster in patterns.
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Movement behaviors, that is, both physical activity and sedentary behavior, are independently associated with health risks. Although both behaviors have been investigated separately in people after stroke, little is known about the combined movement behavior patterns, differences in these patterns between individuals, or the factors associated with these patterns. Therefore, the objectives of this study are (1) to identify movement behavior patterns in people with first-ever stroke discharged to the home setting and (2) to explore factors associated with the identified patterns.
E-cycling intelligence is a research project directly connected to the PhD-research of Joost de Kruijf at the Utrecht University. Within the program the effects of the introduction of e-bikes in daily commuting are being investigated. Using a large-scale incentive program targeting on behavioral change among car-oriented commuters the next four specific components are being :- Modal shift to e-cycling- Well-being and travel satisfaction of e-bikes vs. car- Weather circumstances and e-cycling- Behavioral intention to e-bike vs. actual behavior Using a combination of three surveys (baseline, one month and half a year) and continuous GPS-measurement on the behavior of more than 800 participants makes this research unique. In collaboration with the TU/e the GPS-dataset is being translated into relevant information on modal shift on different trip purposes offering a new range of possibilities to analyses behavioral change. Knowledge on every of the four topics in the project is translated scientific paper. The expected end of the project is July 2021.With the research not new insights are being gained, the Breda University of Applied Sciences also develops a scientific network of cycling related researchers together with a network of cycling engaged road authorities.
Smart office buildings are expected to incorporate user preferences, business objectives, and sustainability goals simultaneously in building operations. Furthermore, they are expected to provide comfortable, flexible and energy efficient working environment to its owners and users. Smart working environments with various implemented technologies affect the work style and user behavior. It changes how people use the office environment compared to traditional office environments. The success of a new smart technology largely depends on the user satisfaction of the office workers, which has not been studied until so far mostly due to the lack of sufficient data. Accordingly, the main objective of this research is to reveal what the significant aspects are for a successful adaptation of smart technologies in the office environment. Therefore, the case of "Stadhuistoren" which is one of the newest smart office building of the Municipality of Eindhoven is studied for which both user satisfaction data, as well as smart system control data, are collected. The smart system is a typical example of an innovative induvial climate control system, newly implemented in the Stadhuistoren. Finally, in this research, it is expected to reveal how user satisfaction is affected by smart technologies in offices for the future. The research method and the findings can be used as input for the implementation of other smart technologies in future smart offices.
