User experience (UX) summarizes user perceptions and responses resulting from the interaction with a product, system, or service. The User Experience Questionnaire (UEQ) is one standardized instrument for measuring UX. With six scales, it identifies areas in which product improvements will have the highest impact. In this paper, we evaluate the reliability and validity of this questionnaire. The data of N = 1, 121 participants who interacted with one of 23 products indicated an acceptable to good reliability of all scales. The results show, however, that the scales were not independent of each other. Combining perspicuity, efficiency, and dependability to pragmatic aspects as well as novelty and stimulation to hedonic aspects of UX improved the model fit significantly. The systematic variations of product properties and correlations with the System Usability Scale (SUS) in a second experiment with N=499 participants supported the validity of these two factors. Practical implications of the results are discussed.

We explore how discreet input can be provided using the tensor tympani - a small muscle in the middle ear that some people can voluntarily contract to induce a dull rumbling sound. We investigate the prevalence and ability to control the muscle through an online questionnaire (N=192) in which 43.2% of respondents reported the ability to “ear rumble”. Data collected from participants (N=16) shows how in-ear barometry can be used to detect voluntary tensor tympani contraction in the sealed ear canal. This data was used to train a classifier based on three simple ear rumble “gestures” which achieved 95% accuracy. Finally, we evaluate the use of ear rumbling for interaction, grounded in three manual, dual-task application scenarios (N=8). This highlights the applicability of EarRumble as a low-effort and discreet eyes- and hands-free interaction technique that users found “magical” and “almost telepathic”.

Modern mobile phones are equipped with many sensors, which can increasingly be used to sense various environmental phenomena. In particular, mobile sensing has enabled crowdsourced data collection at an unprecedented scale. However, as laypersons are involved in this, concerns regarding the data quality arise. This work explores the gamification of smartphone-based measurement processes in practice by embedding a sensing task into a mobile minigame. The underlying idea is – rather than to educate the user on how to correctly perform a measurement task – to opportunistically execute the measurement in the background once the smartphone is in a suitable context. To this end, this paper presents the design and evaluation of SpaceMaze, a smartphone game with the goal of minimising user error by introducing appropriate game mechanics to influence the phone context, using the example of mobile noise level monitoring. A large user study that compares SpaceMaze to two non-gamified apps for noise level monitoring (N = 360 in total) shows that SpaceMaze can successfully reduce user errors when compared to simple non-gamified ambient noise level monitoring applications and that the minigame is generally perceived as being enjoyable. Solutions for remaining problems, such as noise generated by the players, are discussed.

Sleep breathing disorder is a serious threat to a large share of the population. This paper presents a low-cost, tiny sensor system based on Volatile Organic Component (VOC) sensing for the detection of sleep apnea/hypopnea. We present two designs, discuss wearability aspects and show that the sensor works similar to gold standard Polysomnography (PSG).

Human Activity Recognition (HAR) with body-worn sensors has been studied intensively in the past decade. Existing approaches typically rely on data from inertial sensors. This paper explores the potential of using point cloud data gathered from wearable depth cameras for on-body activity recognition. We discuss effects of different granularity in the depth information and compare their performance to inertial sensor based HAR. We evaluated our approach with a total of sixteen participants performing nine distinct activity classes in three home environments. 10-fold cross-validation results of KNN and Random Forests classification exhibit a significant increase in F-score from inertial data to depth information (by > 12 percentage points) and show a further improvement when combining low-resolution depth matrices and sensor data. We discuss the performance of the different sensor types for different contexts and show that overall, depth sensors prove to be suitable for HAR.

Wearables like smartwatches or fitness trackers are increasingly entering everyday life. This offers new and unexplored opportunities to use their unique features - like the ability to act as vibrotactile actuators - as interfaces in computer games to increase game immersion. Current tactile cues in games are mostly restricted to event based-feedback and practically exclusive to console gaming. This paper presents LifeTact, a system which informs players about remaining hit points through a tactile heartbeat. The Tactile heartbeat is emitted via existing wearables like smartwatches or fitness trackers. The system was implemented and evaluated in a between-subject study with gamers, who rated the system as innovative, beneficial to the user experience and immersive.

Participatory Sensing, i.e. collaboratively taking sensor measurements with mobile devices in a Citizen Science fashion, has become increasingly popular. Because such scenarios often require a critical mass of users, applying gamification to different areas in order to increase user engagement has been proposed. However, existing attempts often default to the standard points, badges, and leaderboards and fail to recognize the potential of exploiting game design elements beyond creating user engagement. We propose not to think of Gamified Participatory Sensing when designing such systems, but rather of Sensified Gaming. To this end, this work presents a collection of design patterns and game mechanics that can be used to identify or design suitable games, into which participatory sensing tasks can be embedded. We identified four core tasks from participatory environmental sensing and sensor networks research, reviewed hundreds of design patterns and map each of the 63 selected patterns to the core tasks.

This work presents the bPart, a highly integrated autonomous sensor platform for use with mobile phones and devices. It consists of a Bluetooth Low Energy (BLE) radio and several MEMS sensors, all integrated in a volume of less than 1cm³, including the battery. Aside from the wireless transceiver, the bPart features sensors for ambient illumination, 3D-acceleration, temperature and relative humidity. In addition, there is a button and a magnetic switch for binary input and a RGB-LED for user feedback. A secondary LED in the infrared spectrum enables camera-assisted identification and tracking of the node. Runtimes of several years are possible on the included CR2023 lithium coin cell, through the low energy radio, onboard power-conversion and low-power sleep modes. The latter is rated below 2µW and a single data packet consumes about 75µWs. Its low energy consumption makes the bPart suitable for operation with energy harvesting, which we have validated with a 33cm² solar cell in indoor lighting conditions.

This paper proposes using a 2D waveguide for a novel means of authentication in public Wi-Fi infrastructures. The design of the system is presented, and its practicability and usability is comparatively discussed with that of five other tag and context based authentication schemes, two of which have not been previously realized. In accordance with the presented application scenarios, all of the schemes were implemented in a platform-independent fashion built on web technology.

This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial off-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching meaningful accuracy. We conducted a series of experiments to juxtapose the performance of a gauged high-accuracy measurement device and a cheap COTS sensor that we fitted on a Bluetooth-enabled sensor module that can be interconnected with a mobile phone. Calibration and processing procedures using multi-sensor data fusion are presented, that perform very well in lab situations and show practically relevant results in a realistic setting. An on-the-fly calibration correction step is proposed to address remaining issues by taking advantage of co-located measurements in Participatory Sensing scenarios. By sharing few measurement across devices, a high measurement accuracy can be achieved in mobile urban sensing applications, where devices join in an ad-hoc fashion. A performance evaluation was conducted by co-locating measurement devices with a municipal measurement station that monitors particulate matter in a European city, and simulations to evaluate the on-the-fly cross-device data processing have been done.

Human Activity Recognition (HAR) using accelerometers has been studied intensively in the past decade. Recent HTML5 methods allow sampling a mobile phone's sensors from within web pages. Our objective is to leverage this for the creation of individual activity recognition modules that can be included into web applications to allow them to gain context-awareness. In this work, jActivity, a first prototype of such a platform-independent HTML5/JavaScript framework is presented, along with experiments to determine the general feasibility and challenges for HAR in web applications. Our results indicate that the realization looks promising, albeit so far limited to certain devices/user agents.