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The advancement of battery technology is falling behind the progress of mobile processing capabilities and storage technology. This presents an issue which needs to be addressed, given the ever-increasing energy consumption by mobile applications. To reduce these energy demands we can make use of approximate computing techniques, which act as a countermeasure to overcome this problem by reducing computation accuracy to achieve higher energy efficiency, without having a negative impact on the end-user experience. Since the most frequently used applications on mobile devices are ones from the multimedia domain, we show how these techniques can be applied to video playback with the use of context-aware video quality adaptation. We conduct a supervised user study to inspect how the context in which a video is played, its content and the user's personality affect the video quality requirements of the user, and discover that the user's physical activity, the spatial and temporal properties of a video, and the user's personality traits all play a role in influencing the minimal acceptable playback resolution. Napredek tehnologije baterij zaostaja za napredkom računske sposobnosti mobilnih naprav in tehnologije shranjevanja. To predstavlja težavo, ki jo je potrebno obravnavati glede na vedno naraščajočo porabo energije s strani mobilnih aplikacijah. Eden od načinov za izboljšanje energetske učinkovitosti mobilnih naprav je z uporabo tehnik približnega računanja, ki dosežejo boljšo učinkovitost z zmanjšanjem natančnosti izračuna, ne da bi to negativno vplivalo na uporabniško izkušnjo. Ker so najpogosteje uporabljene aplikacije na mobilnih napravah tiste iz multimedijske domene, v tem delu pokažemo, kako je mogoče te tehnike uporabiti pri predvajanju videa z uporabo kontekstno odvisne prilagoditve kakovosti videa. Izvedemo nadzorovano študijo uporabnikov, da preverimo, kako kontekst v katerem se predvaja videoposnetek, njegova vsebina in uporabnikova osebnost, vplivajo na zahteve uporabnika glede kakovosti videa in odkrijemo, da fizična aktivnost, prostorske in časovne lastnosti videoposnetka ter osebnost uporabnika igrajo vlogo pri pri določanju minimalne sprejemljive kakovosti videa.

The Experience Sampling Method (ESM) is gaining ground for collecting self-reported data from human participants during daily routines. An important methodological challenge is to sustain sufficient response rates, especially when studies last longer than a few days. An obvious strategy is to deliver the experiential questions on a device that study participants can access easily at different times and contexts (e.g., a smartwatch). However, responses may still be hampered if the prompts are delivered at an inconvenient moment. Advances in context sensing create new opportunities for improving the timing of ESM prompts. Specifically, we explore how physiological sensing on commodity-level smartwatches can be utilized in triggering ESM prompts. We have created Experiencer, a novel ESM smartwatch platform that allows studying different prompting strategies. We ran a controlled experiment (N=71) on Experiencer to study the strengths and weaknesses of two sampling regimes. One group (N=34) received incoming notifications while resting (e.g., sedentary), and another group (N=37) received similar notifications while being active (e.g., running). We hypothesized that response rates would be higher when experiential questions are delivered during lower levels of physical activity. Contrary to our hypothesis, the response rates were found significantly higher in the active group, which demonstrates the relevance of studying dynamic forms of experience sampling that leverage better context-sensitive sampling regimes. Future research will seek to identify more refined strategies for context-sensitive ESM using smartwatches and further develop mechanisms that will enable researchers to easily adapt their prompting strategy to different contextual factors.

In this work, we explore a new sensing technique for smart eyewear equipped with Electrooculography (EOG) sensors. We repurpose the EOG sensors embedded in a JINS MEME smart eyewear, originally designed to detect eye movement, to detect midair hand gestures. We also explore the potential of sensing human proximity, rubbing action and to differentiate materials and objects using this sensor. This new found sensing capabilities enable a various types of novel input and interaction scenarios for such wearable eyewear device, whether it is worn on body or resting on a desk.

The virtual bot is one of the hot topics in artificial intelligence, where most of the current studies focus on chatbots. Nevertheless, the context-sensitive virtual bot, especially with rich human-like interactions (e.g., appearance change, context-aware narration/recommendation) regarding the ambient changes (e.g., location, focused scene) through various built-in sensors, would have broader application. Towards this direction, we propose MateBot, a human-like, context-sensitive virtual bot, which supports harmonious human-computer interaction on smartphones. The design of MateBot consists of three parts. First, a context sensing network is used to recognize the input background information and face information, and modify the appearance of the virtual bot through the conversion of the encoding network. Second, a human-like bot appearance generation network can generate a virtual bot image with a human-like appearance through the GAN network and modify the appearance of the virtual bot with context-sensitive information. Third, a personalized conversation network is devised to communicate with human users. Furthermore, we apply MateBot to the intelligent travel scenario to justify its practicality, and the experiment results show that the bot can better increase the user’s sense of substitution and improve the communication efficiency between human users and virtual bots.

Data from networked sensors, such as those in our phones, are increasingly being explored and used to identify behaviors related to health and mental health. While computer scientists have referred to this field as context sensing, personal sensing, or mobile sensing, medicine has more recently adopted the term digital phenotyping. This paper discusses the implications of these labels in light of privacy concerns, arguing language that is transparent and meaningful to the people whose data we are acquiring.

In-vehicle context sensing can detect many aspects of driver behavior and the environment, such as drivers changing lanes, potholes, road grade, and stop signs, and these features can be used to improve driver safety and comfort, and engine efficiency. In general, detecting these features can use either onboard sensors on the vehicle ( car sensors ) or sensors built into mobile devices ( phone sensors ) carried by one or more occupants, or both. Furthermore, traces of sensor readings from different cars, when crowd-sourced , can provide increased spatial coverage as well as disambiguation. In this paper, we explore, by designing novel detection algorithms for the four different features discussed above, three related questions: How is the accuracy of detection related to the choice of phone versus car sensors? To what extent, and in what ways, does crowd-sourcing contribute to detection accuracy? How is accuracy affected by phone position? We have collected hundreds of miles of vehicle traces with annotated groundtruth, and demonstrated through evaluation that our detection algorithms can achieve high accuracy for each task (e.g., $>$ 90% for lane change determinations) and that crowd-sensing plays an indispensable role in improving the detection performance (e.g., improving recall by 35% for lane change determinations on curves). Our results can give car manufacturers insight into how to augment their internal sensing capabilities with phone sensors, or give mobile app developers insight into what car sensors to use in order to complement mobile device sensing capabilities.

The goal of active aging is to promote changes in the elderly community so as to maintain an active, independent and socially-engaged lifestyle. Technological advancements currently provide the necessary tools to foster and monitor such processes. This paper reports on mid-term achievements of the European H2020 EMPATHIC project, which aims to research, innovate, explore and validate new interaction paradigms and platforms for future generations of personalized virtual coaches to assist the elderly and their carers to reach the active aging goal, in the vicinity of their home. The project focuses on evidence-based, user-validated research and integration of intelligent technology, and context sensing methods through automatic voice, eye and facial analysis, integrated with visual and spoken dialogue system capabilities. In this paper, we describe the current status of the system, with a special emphasis on its components and their integration, the creation of a Wizard of Oz platform, and findings gained from user interaction studies conducted throughout the first 18 months of the project., Comment: 12 pages

The rapid development in mobile computing has brought context sensing and information available for music recommendation as well. We reviewed 19 experimental contextual music systems and found that while the context factors of location, time, activity, and identity are adopted in a wide variety of ways, the systems mainly rely on common UI solutions for interacting with these factors. Specifically, context factors could be employed to offer explanations, transparency, and visualizations of music recommendations in more explorative ways, providing novel user experiences. Based on our review, we provide implications for design and research on future media discovery systems, which we believe can realize the great potential of context-aware content services.

تعد بیئات الواقع المعزز من التکنولوجیات الحدیثة فی التعلیم، وهی تکنولوجیا تدمج بین الواقع الحقیقی والواقع الافتراضی، حیث یمکنها تعزیز الواقع الحقیقی بمعلومات إضافیة وصور ولقطات فیدیو من خلال تکنولوجیا الواقع الافتراضی. تعتمد تکنولوجیا الواقع المعزز على استخدام التکنولوجیات والاجهزة المحمولة، وخاصة الکمبیوتر اللوحی والهواتف الذکیة، حیث تقوم هذه الاجهزة باستشعار السیاق الحقیقی، وعرض المعلومات المناسبة له. ومن ثم فإن عنصر استشعار السیاق الحقیقی یعد من أهم مکونات التعلم المعزز، لانه هو الذی یربط بین الواقع الحقیقی والواقع الافتراضی. وتوجد أنماط عدیدة لاستشعار الواقع الحقیقی، لکل منها إمکانیاته وحدوده واستخدماته، ولکن البحوث والدراسات السابقة لم تقارن بین فاعلیة استخدام هذه المستشعرات، لتحدید الانسب منها. لذلک یهدف البحث الحالی إلى المقارنة بین أثر استخدام نمطین لاستشعار السیاق البیئی الحقیقی. هما نمط استشعار السیاق القائم على العلامة Marker، ونمط استشعار السیاق بدون العلامة Markerless. ومن أجل هذا قامت الباحثة بتصمیم وتطویر نسختین من بیئة الواقع المعزز، استخدمت فی الأولى نمط الاستشعار القائم على "العلامة"، وفی الثانیة نمط الاستشعار "بدون العلامة"، وطبقتهما على طالبات الفرقة الرابعة شعبة تکنولوجیا التعلیم والمعلومات بکلیة البنات، جامعة عین شمس للعام الدراسی 2015/2016، لمعرفة تأثیرهما على تنمیة بعض مهارات صیانة الکمبیوتر والتفکیر البصری، وقد کشفت النتائج عن تساوی الکسب فی تحصیل الجانب المعرفی من مهارات صیانة الکمبیوتر للطالبات فی کل من نمطی استشعار السیاق (القائم على العلامة Marker، بدون العلامة Markerless) ببیئة الواقع المعزز، ووصولهن لدرجة التمکن 90% من الدرجة الکلیة للاختبار التحصیلی ککل، أما بالنسبة للجانب الأدائی من مهارات صیانة الکمبیوتر فقد وجد فرق دال بین متوسطى الکسب فی الجانب الأدائی من مهارات صیانة الکمبیوتر للطالبات لصالح متوسط درجات الطالبات فی نمط إدراک السیاق بدون العلامة Markerless، کما وصلت الطالبات فی کل من نمطی الاستشعار لدرجة التمکن 90% من الدرجة الکلیة لبطاقة الملاحظة ککل، کما کشفت النتائج عن تساوی الکسب فی مهارات التفکیر البصری للطالبات فی کل من نمطی استشعار السیاق (القائم على العلامة Marker، بدون العلامة Markerless) ببیئة الواقع المعزز، ووصولهن لدرجة التمکن 90% من الدرجة الکلیة لاختبار التفکیر البصری ککل. وفی ضوء ذلک قدمت الباحثة التوصیات والمقترحات المناسبة. Augmented reality environment is considered one of the new educational technologies that can combine both reality and virtual reality in one environment and enhance reality with additional information, images, and video clips through the augmented reality technology. The augmented reality technology,basically, depends on the usage of technology and the portable devices, especially the laptops and smartphones, where these devices can recognize the real context and present the appropriate information. Consequently, the factor of recognizing the real context is considered one of the most important components of the augmented reality as it combine both reality and virtual reality. Although there are various modesof recognizing reality, each pattern has its abilities, limitations, and usages and the available related literature did not provide clear comparison between these modes to identify the most appropriate one. With this in mind, the current research aims at comparing between the impact of using two modes of recognizing the environmental reality context. These two modes, namely, are the marker-based recognition mode and the markerless-based recognition mode. To fulfill the purpose of the current study, the researcher designed and developed two versions of the augmented reality environment. In the first version, the marker-based recognition modewas adopted and in the second version markerless-based recognition mode was adopted.These two modeswere administered to the fourth year Educational Technology and Information department students, Faculty of Girls, Ain Shams University, during the academic year 2015-2016 A.D. in order to probe the impact of the tow modes on developing some computer maintenance and the visual thinking skills. The results of the study revealed that the gaining rate of the cognitive achievement of the computer maintenance skills among the students in both of the recognition based on augmented reality environment (marker, markerless) was equal in relation to the two groups and the students reach 90% mastery level according to the achievement test. Furthermore,in relation to the practical domain of the computer maintenance skills among the students, there was a statistically significant difference among the gaining means in the practical part of the computer maintenance skills among the students in favour of the markerless-based recognition mode. Moreover, the students reached 90 % mastery level according to the observation checklist. The results of the study also revealed that the visual thinking skills among the students in both of the context recognition mode (marker and markerless) within the augmented reality environment was equal. The students reached 90% mastery level of the total degree of visual thinking test. The recommendations and suggestions for further researches were included.

Following current trends in language learning applications, a context-based language generating application was developed to aid learners in effective language acquisition. In an effort to not only match the user’s situation with a relevant sentence, but also combine context information to create heterogeneous sentences, a new data model was devised. This paper describes the structure of this model based on a sensing data classifier as well as the corresponding language database. It also depicts a usage scenario with a procedural description of the underlying processes.

This chapter discusses the concept of human context sensing, the definitions of the four main types of human contexts, and the current technological sensing mechanisms. The types of human context sensing are physiological sensing, emotive sensing, functional sensing, and location sensing. Together, these facets capture the mental states, physical body conditions, lifestyles, and location of individuals. The goals and applications for each category unify in improving the quality of life of an individual by monitoring different aspects of life that can help the smart city provide an individual with the right level of assistance and facilities. the chapter also discusses the impact of the four main technological thrusts in each category of human context sensing: video and audio, wearables, smartphones, and environmental sensing. Each type of technology has a unique set of sensing abilities as well as constraints. Additionally, each has practical uses, costs, and privacy implications for use in a smart city.

Item does not contain fulltext This research motivates and evaluates the notion of similarity awareness as a means to enhance connectedness between remote family members. Similarity awareness refers to notifying connected individuals when they are engaged in similar activities. This idea is illustrated with the design and evaluation of MatchMaker an application targeting the needs of young adults who have recently left home and their parents, the so called 'empty nesters'. The potential affective benefits of similarity awareness are evaluated in a laboratory experiment involving 23 pairs of a parent and a child, showing strong indications that similarity awareness can enhance social connectedness. A proof of concept of MatchMaker was implemented where it is shown that combined audio and video scene analysis can identify reliably six activities typical for domestic life. 17 p.

BackgroundA major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. ObjectiveWe sought to (1) assess smokers’ compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. MethodsAn explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. ResultsOut of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app’s identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. ConclusionsUser-initiated self-report is feasible for training a cessation app about an individual’s smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.

The authors were invited to present a reprise of a recently-published paper on Sensing Techniques for Tablet \(+\) Stylus Interaction at the WIPTTE 2015 Workshop. The talk took the original contribution as a point of departure, because for the WIPTTE venue we felt that the most important role of the work was to illuminate and help the audience understand more deeply the interaction modalities of pen and touch—as well as their use in tandem. And in the process the authors felt like they came to understand the topic more deeply as well, hence the paper that follows.

The clinical assessment of severity of depressive symptoms is commonly performed with standardized self-report questionnaires, most notably the patient health questionnaire (PHQ-9), which are usually administered in a clinic. These questionnaires evaluate symptoms that are stable over time. Ecological momentary assessment (EMA) methods, on the other hand, acquire patient ratings of symptoms in the context of their lives. Today's smartphones allow us to also obtain objective contextual information, such as the GPS location, that may also be related to depression. Considering clinical PHQ-9 scores as ground truth, an interesting question is to what extent the EMA ratings and contextual sensor data can be used as potential predictors of depression. To answer this question, we obtained PHQ-9 scores from 18 participants with a variety of depressive symptoms in our lab, and then collected their EMA and GPS sensor data using their smartphones over a period of two weeks. We analyzed the relationship between GPS sensor features, EMA ratings, and the PHQ-9 scores. While we found a strong correlation between a number of sensor features extracted from the two-week period and the PHQ-9 scores, the other relationships remained non-significant. Our results suggest that depression is better evaluated using long-term sensor-based measurements than the momentary ratings of mental state or short-term sensor information.

We propose context sensing as features for improved accuracy in classifications in our ongoing research. In many applications, features extracted from purposed sensors may not be enough for classification tasks accurately. Context features can help to discriminate classes in such cases. To address the problem, we first present how context can be used as features in classifications. Further, we present a case study on energy appliance identification from aggregated power usage for a household using context sensing and features.

Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra. The unprecedented success of Web 2.0, and with it, social media services, has resulted in massive amounts of user-generated data. Traditional techniques are no longer adequate to deal with this sheer amount of information. In an attempt to address this problem, new techniques that can be applied to big data, are being proposed in an increasingly frequent way. In this dissertation, the concept of parsimonious sensing and some of its applications are presented. Parsimonious sensing attempts to select the most relevant information from a large dataset, thus reducing the cost of its analysis. To do this, it employs different techniques such as active learning, also know as optimal experimental design in the field of statistics. We also explore some innovative methods of identifying relevant anomalies from a large dataset to be subsequently explored. This dissertation studies the application of parsimonious sensing on three unique datasets. The first main experience studies the employment of active learning in an environmental sensing network system with air quality parameters. The second experience depicts an attempt to predict the number of hits for a certain query related to events happening in Singapore, thus decreasing the number of required queries. The third and last experiment makes use of a dataset provided by a major taxi company in Singapore and tries to identify traffic anomalies and later, synthesize queries that are run through a search engine in order to identify the context of the anomalies. We found the application of parsimonious sensing to be successful when implemented in the context of environmental sensing. We have further developed a system capable of identifying traffic anomalies and returning a number of links that can potentially explain why they happened. The fully automated system has been shown to be better than a hybrid system, composed of information retrieved both automatically and manually. The findings from this dissertation can hopefully shed some light on the possible applications of parsimonious sensing to diverse contexts.

Health effects attributed to air pollution, especially ambient fine particulate matter (PM2.5), become a global issue. The central environment monitoring networks provide limited spatial coverage and no contextual information. However, there is no solution to take contextual information, such as environmental and user behavioral factors, into account, which is highly associated to the variability of air quality level and the complex relationship between air quality and human activities. In this paper, we design, implement, and evaluate a new context-sensing device for personal air quality monitoring, namely AirSense. AirSense is a portable and cost-effective platform, which is equipped with a dust sensor, a global position system (GPS) sensor, a temperature and humidity sensor, and an accelerometer sensor. The development of such a user-centered and geographical-information integrated platform enables us to collect fine-grained air quality along with contextual information. We evaluate the platform across a set of focused settings, such as the indoor vs outdoor, walking vs in-vehicle, moving vs stationary, and an environment with various levels of dust. Meanwhile, a user study is conducted to verify that AirSense is capatable of performing the ambient air quality monitoring in daily life. We also discuss several other applications with the new context-sensing platform.

Developers work in the IDE, but search online resources in the web browser. The separation of the working and search context often cause the ignorance of the working context during online search. Several tools have been proposed to integrate the web browser into the IDE so that developers can search and use online resources directly in the IDE. These tools enable only the shallow integration of the web browser and the IDE. Some tools allow the developer to augment search queries with program entities in the current snapshot of the code. In this paper, we present an in-IDE ambient search agent to bridge the separation of the developer's working context and search context. Our approach considers the developers' working context in the IDE as a time-series stream of programming event observed from the developer's interaction with the IDE over time. It supports the deeper integration of the working context in the entire search process from query formulation, custom search, to search results refinement and representation. We have implemented our ambient search agent and integrate it into the Eclipse IDE. We conducted a user study to evaluate our approach and the tool support. Our evaluation shows that our ambient search agent can better aid developers in searching and using online programming resources while working in the IDE.

[ENGLISH] This thesis goes into context aware systems, specifically studies the AWARE framework and the information stored by this tool. The initial objective is the understanding of the term context awareness and the different types of context. A second objective is the study of the AWARE framework and the parts that compose it, which includes the client application (configuration and sensor information) and the AWARE server (database and web services). This study doesn't include any further programming of the sensors or the creation of new plug-ins. And as a final objective will be to test whether or not the AWARE framework is able to provide context aware information that allows the user to monitor, study and reconstruct a given activity. For this, a test was performed using three different users to recreate an ongoing activity in Barcelona. Also a test varying the location sensor settings was performed, to help improve the accuracy of the information offered by this sensor.

A primary concern with modern smartphones is battery consumption. With so many different hardware components in modern smartphones, there are situations where certain components may be powered down or reduced in functionality without disrupting the user experience. We propose a transparent correlation-based scheme for energy efficient context sensing and fusion under Android systems. We experiment with the idea of disabling hardware functionality based on context. Our scheme focuses on inferring a user's location and subsequently disabling the GPS, which is considered to be one of the most energy-expensive components included in a smartphone. For example, when a user has connected to a Wi-Fi network with a known location, we disable GPS navigation and deliver the known location in its place. Based on our experiments, we conclude that this approach can significantly improve a device's battery life.

In this paper, we highlight the challenge of continuously sensing context data from mobile phones. In particular, we show that the energy cost of this type of continuous sensing is extremely high if a) accuracy is desired, and b) power optimisations do not work well if multiple tasks are sensing concurrently. Our results are derived from our experience in building the LiveLabs context sensing platform. We present results for different types of sensing tasks; ranging from simple sensing using just one sensor all the way to multi-sensor sensing performed by concurrent high-level tasks. We end with a discussion of the challenges of supporting multi-task sensing across heterogeneous devices and operating systems.

Heading information becomes widely used in ubiquitous computing applications for mobile devices. Digital magnetometers, also known as geomagnetic field sensors, provide absolute device headings relative to the earth's magnetic north. However, magnetometer readings are prone to significant errors in indoor environments due to the existence of magnetic interferences, such as from printers, walls, or metallic shelves. These errors adversely affect the performance and quality of user experience of the applications requiring device headings. In this paper, we propose Headio, a novel approach to provide reliable device headings in indoor environments. Headio achieves this by aggregating ceiling images of an indoor environment, and by using computer vision-based pattern detection techniques to provide directional references. To achieve zero-configured and energy-efficient heading sensing, Headio also utilizes multimodal sensing techniques to dynamically schedule sensing tasks. To fully evaluate the system, we implemented Headio on both Android and iOS mobile platforms, and performed comprehensive experiments in both small-scale controlled and large-scale public indoor environments. Evaluation results show that Headio constantly provides accurate heading detection performance in diverse situations, achieving better than 1 degree average heading accuracy, up to 33X improvement over existing techniques.

Rank-based policies represent a promising approach for designing message forwarding algorithms that meet the needs of opportunistic networks. In fact, they combine low computation and communication costs with good performance in terms of both latency and delivery rates. Nonetheless, they highly depend on the mobility scenario relevant to the user, and a forwarding policy with good performances in heterogeneous settings has yet to be designed. In this paper, we propose to provide each mobile device with novel autonomic observation and reasoning components according to the following objectives: enable the device (i) to achieve awareness about the behavior of the mobility scenario it is moving in, and (ii) to identify the role played by the device within the set of other moving devices. These components are combined into a self-configuring forwarding algorithm that uses them to locally install both the utility function and the relevant settings suitable for the sensed configuration. Through of extensive simulations, this paper shows that by properly discriminating between roles it is possible to derive a self-configuring forwarding mechanism that constantly performs well in different mobility settings.

Among artists and designers, the pen-and-tablet combination is widely used for creating digital drawings, as digital pens outperform other input devices in replicating the experience of physical drawing tools. In this paper, we explore how contextual information such as the relationship between the hand, the pen, and the tablet can be leveraged in the digital drawing experience to further enhance its naturalness. By embedding sensors in the pen and the tablet to sense and interpret these contexts, we demonstrate how several physical drawing practices can be reflected and assisted in digital interaction scenarios.

In usability evaluations, experiments are often conducted in closed laboratory situations to avoid disturbing influences. Due to non-realistic usage assumptions, this approach has important shortcomings when mobile Human Computer Interactions (m-HCI) have to be evaluated. Field studies allow to perform experiments close to real-world conditions, but potentially introduce influences caused by the environment.In this paper, we aim at distinguishing application shortcomings from environmental disturbances which both potentially cause decreased user performance. Our approach is based on monitoring environmental conditions during the usability experiment, such as light, acceleration, sound, temperature, and humidity, and relating them to user actions. Therefore, a mobile context-framework has been developed based on a small Wireless Sensor Network (WSN). First results are presented that point at increased delays and error rates of user tasks under induced environmental disturbances. Additionally, we demonstrate the potential of environmental monitoring for understanding user performance.

Context sensitivity is an important application of wearable computers. The article describes research on using the Twiddler one-handed keyboard for sensing motion-associated context. Two possible methods for detecting walking are described, and other types of movement context that it might be possible to sense are discussed. Results of early experiments with a preliminary Twiddler driver modification are discussed.

The immanent and pervasive use of mobile devices, especially in wireless environments, raises issues about the context awareness and sensitivity of applications. As the use of embedded mobile devices grows in vast quantity, the need for the efficient gathering, representation and delivery of so called ‘context information’ evolves. With regard to this lack of context oriented computing methods, this work describes issues related to context sensing, representation and delivery, and proposes a new approach for context based computing: Time and event triggered context sensing for mobile devices and an abstract (application and platform independent) representation of context information is introduced. The paper presents different showcases of time and event triggered context sensing in wireless environments.

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. To realise this prediction, next-generation computing should develop anticipatory user interfaces that are human-centred, built for humans and based on naturally occurring multimodal human communication. These interfaces should transcend the traditional keyboard and mouse and have the capacity to understand and emulate human communicative intentions as expressed through behavioural cues, such as affective and social signals. This article discusses how far we are to the goal of human-centred computing and Human-Centred Intelligent Human-Computer Interaction (HCI²) that can understand and respond to multimodal human communication.