Ubiquitous computing and natural interfaces for environmental information

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe next computing revolution‘s objective is to embed every street, building, room and object with computational power. Ubiquitous computing (ubicomp) will allow every object to receive and transmit information, sense its surroundings and act accordingly, be located from anywhere in the world, connect every person. Everyone will have the possibility to access information, despite their age, computer knowledge, literacy or physical impairment. It will impact the world in a profound way, empowering mankind, improving the environment, but will also create new challenges that our society, economy, health and global environment will have to overcome. Negative impacts have to be identified and dealt with in advance. Despite these concerns, environmental studies have been mostly absent from discussions on the new paradigm. This thesis seeks to examine ubiquitous computing, its technological emergence, raise awareness towards future impacts and explore the design of new interfaces and rich interaction modes. Environmental information is approached as an area which may greatly benefit from ubicomp as a way to gather, treat and disseminate it, simultaneously complying with the Aarhus convention. In an educational context, new media are poised to revolutionize the way we perceive, learn and interact with environmental information. cUbiq is presented as a natural interface to access that information

Methods for monitoring the human circadian rhythm in free-living

Our internal clock, the circadian clock, determines at which time we have our best cognitive abilities, are physically strongest, and when we are tired. Circadian clock phase is influenced primarily through exposure to light. A direct pathway from the eyes to the suprachiasmatic nucleus, where the circadian clock resides, is used to synchronise the circadian clock to external light-dark cycles. In modern society, with the ability to work anywhere at anytime and a full social agenda, many struggle to keep internal and external clocks synchronised. Living against our circadian clock makes us less efficient and poses serious health impact, especially when exercised over a long period of time, e.g. in shift workers. Assessing circadian clock phase is a cumbersome and uncomfortable task. A common method, dim light melatonin onset testing, requires a series of eight saliva samples taken in hourly intervals while the subject stays in dim light condition from 5 hours before until 2 hours past their habitual bedtime. At the same time, sensor-rich smartphones have become widely available and wearable computing is on the rise. The hypothesis of this thesis is that smartphones and wearables can be used to record sensor data to monitor human circadian rhythms in free-living. To test this hypothesis, we conducted research on specialised wearable hardware and smartphones to record relevant data, and developed algorithms to monitor circadian clock phase in free-living. We first introduce our smart eyeglasses concept, which can be personalised to the wearers head and 3D-printed. Furthermore, hardware was integrated into the eyewear to recognise typical activities of daily living (ADLs). A light sensor integrated into the eyeglasses bridge was used to detect screen use. In addition to wearables, we also investigate if sleep-wake patterns can be revealed from smartphone context information. We introduce novel methods to detect sleep opportunity, which incorporate expert knowledge to filter and fuse classifier outputs. Furthermore, we estimate light exposure from smartphone sensor and weather in- formation. We applied the Kronauer model to compare the phase shift resulting from head light measurements, wrist measurements, and smartphone estimations. We found it was possible to monitor circadian phase shift from light estimation based on smartphone sensor and weather information with a weekly error of 32±17min, which outperformed wrist measurements in 11 out of 12 participants. Sleep could be detected from smartphone use with an onset error of 40±48 min and wake error of 42±57 min. Screen use could be detected smart eyeglasses with 0.9 ROC AUC for ambient light intensities below 200lux. Nine clusters of ADLs were distinguished using Gaussian mixture models with an average accuracy of 77%. In conclusion, a combination of the proposed smartphones and smart eyeglasses applications could support users in synchronising their circadian clock to the external clocks, thus living a healthier lifestyle

App Usage as Feedback for Mobile Energy-Awareness Apps

Energy plays a central role in mobile computing, especially energy-intensive activities such as watching videos or playing games on mobile devices have increased in popularity. These activities accelerate energy usage in the device, as a result, the question of economizing the energy consumption on mobile devices becomes relevant. Some research efforts have focused on energy management applications to prolong battery life by detecting energy-hungry applications and recommending users to close those applications. However, the recommended applications could be uniquely important to users’ mobile experience and usage might continue even if it means decreased battery life. Except increase battery life by economizing mobile behavior, it is relevant for the design of energy-saving applications to know how users behave when receiving both helpful and redundant recommendations. We conduct a study on mobile application user behavior when there is a mobile energy-aware application (Carat) present on the devices. This thesis provides an approach by using application usage as implicit feedback to study if user behavior changes when recommendations on energy-hungry applications are given over the study period. Firstly, the thesis describes procedures for pre-processing and cleaning the study datasets, such as running applications in sample dataset and energy-hungry applications recommended by Carat in bug dataset and hog dataset. Secondly, this thesis provides statistical analysis methods for analyzing mobile data in different aspects. For example, applications are divided into system and installable applications. We found that users have more common system applications on their devices while less overlapped installable applications. We also separately study bugs and hogs which are the two types of energy-hungry applications. In general, there are more unique energy-hungry applications detected as hogs than bugs. For an average user, system applications are slightly more often bugs than installable applications while installable applications are more often hogs when compared with system applications. Thirdly, this thesis utilizes point biserial correlation to study application usage and Carat recommendations. We found there is no relationship between application usage and recommended energy-hungry applications. We also found that Carat users previously collected information to make recommendations. In addition, we found applications might needed by users. Based on our findings, we suggest that Carat and other energy-hungry applications recommend actions based on recent data only, and do not recommend actions against user’s needs. ACM Computing Classification System (CCS): General and reference → Cross-computing tools and techniques → Empirical studies Probability and statistics → Statistical paradigms → Exploratory data analysis Human-centered computing → Human computer interaction → Empirical studies in HC