Niels van Berkel

The curse of quantified-self: an endless quest for answers

Quantified Selfers are individuals that take a proactive stance to collect and act upon their personal data. However, these endeavours towards a better insight into ones life often do not last long. An important challenge for QS is sustaining data collection over a long period of time (i.e., months, years, decades). In this paper we discuss the drivers, needs and concerns of longitudinal QS-data collection. We argue that to support longitudinal QS various obstacles have to be overcome, including i) integration and sharing of data between a variety of (new) devices, ii) incorporating human input for psychological data collection and iii) providing answers to the questions people really have.

Quantifying Sources and Types of Smartwatch Usage Sessions

We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

Challenges of situational impairments during interaction with mobile devices

User interaction with mobile devices can be negatively affected by contextual factors, known as situationally-induced impairments. In this paper, we provide a systematic overview of established situational impairments and their impact on interaction with mobile devices, as well as existing methods for their detection and design guidelines to overcome them. We also propose a research roadmap for this topic where we argue that more experiments are required regarding the less investigated situational impairments. Furthermore, we argue that successful detection of the presence of a specific situational impairment is paramount before solutions can be proposed to adapt mobile interfaces to accommodate potential situational impairments.

Crowdsourcing situated & subjective knowledge for decision support

In this paper we present a study on crowdsourcing subjective knowledge. We introduce a mobile app that was built for this purpose, and compare results from two datasets collected using the app. One dataset was collected during a workshop and the other one during a one-week long field trial. We present interview findings on mobile knowledge collection. Further, we discuss the types of information that should optimally be collected on the go, and show how our data analysis supports the qualitative findings. This work directly continues our earlier efforts on creating a platform that encapsulates wisdom of the crowd for decision support.

Predicting interruptibility for manual data collection: a cluster-based user model

Previous work suggests that Quantified-Self applications can retain long-term usage with motivational methods. These methods often require intermittent attention requests with manual data input. This may cause unnecessary burden to the user, leading to annoyance, frustration and possible application abandonment. We designed a novel method that uses on-screen alert dialogs to transform recurrent smartphone usage sessions into moments of data contributions and evaluate how accurately machine learning can reduce unintended interruptions. We collected sensor data from 48 participants during a 4-week long deployment and analysed how personal device usage can be considered in scheduling data inputs. We show that up to 81.7% of user interactions with the alert dialogs can be accurately predicted using user clusters, and up to 75.5% of unintended interruptions can be prevented and rescheduled. Our approach can be leveraged by applications that require self-reports on a frequent basis and may provide a better longitudinal QS experience.

The Experience Sampling Method on Mobile Devices

The Experience Sampling Method (ESM) is used by scientists from various disciplines to gather insights into the intra-psychic elements of human life. Researchers have used the ESM in a wide variety of studies, with the method seeing increased popularity. Mobile technologies have enabled new possibilities for the use of the ESM, while simultaneously leading to new conceptual, methodological, and technological challenges. In this survey, we provide an overview of the history of the ESM, usage of this methodology in the computer science discipline, as well as its evolution over time. Next, we identify and discuss important considerations for ESM studies on mobile devices, and analyse the particular methodological parameters scientists should consider in their study design. We reflect on the existing tools that support the ESM methodology and discuss the future development of such tools. Finally, we discuss the effect of future technological developments on the use of the ESM and identify areas requiring further investigation.

Tapping Task Performance on Smartphones in Cold Temperature

We present a study that quantifies the effect of cold temperature on smartphone input performance, particularly on tapping tasks. Our results show that smartphone input performance decreases when completing tapping tasks in cold temperatures. We show that colder temperature is associated with lower throughput and less accurate performance when using the phone in both one-handed and two-handed operations. We also demonstrate that colder temperature is related to higher error rate when using the phone in one-handed operation only, but not two-handed. Finally, we identify a number of design recommendations from the literature that can be considered as a countermeasure to poorer smartphone input performance in completing tapping tasks in cold temperature.

Towards Commoditised Near Infrared Spectroscopy

Near Infrared Spectroscopy (NIRS) is a sensing technique in which near infrared light is transmitted into a sample, followed by light absorbance measurements at various wavelengths. This technique enables the inference of the inner chemical composition of the scanned sample, and therefore can be used to identify or classify objects. In this paper, we describe how to facilitate the use of NIRS by non- expert users in everyday settings. Our work highlights the key challenges of placing NIRS devices in the hands of non-experts. We develop a system to mitigate these challenges, and evaluate it in a user study. We show how NIRS technology can be successfully utilised by untrained users in an unsupervised manner through a special enclosure and an accompanying smartphone app. Finally, we discuss potential future developments of commoditised NIRS.

Gamification of Mobile Experience Sampling Improves Data Quality and Quantity

The Experience Sampling Method is used to capture high-quality in situ data from study participants. This method has become popular in studies involving smartphones, where it is often adapted to motivate participation through the use of gamification techniques. However, no work to date has evaluated whether gamification actually affects the quality and quantity of data collected through Experience Sampling. Our study systematically investigates the effect of gamification on the quantity and quality of experience sampling responses on smartphones. In a field study, we combine event contingent and interval contingent triggers to ask participants to describe their location. Subsequently, participants rate the quality of these entries by playing a game with a purpose. Our results indicate that participants using the gamified version of our ESM software provided significantly higher quality responses, slightly increased their response rate, and provided significantly more data on their own accord. Our findings suggest that gamifying experience sampling can improve data collection and quality in mobile settings.

Sensing Cold-Induced Situational Impairments in Mobile Interaction Using Battery Temperature

Previous work has highlighted the detrimental effect of cold ambience on fine-motor skills during interaction with mobile devices. In this work, we develop a method to infer changes in finger temperature of smartphone users without the need for specialised hardware. Specifically, we demonstrate that smartphone battery temperature is a reliable gauge for determining changes to finger temperature. In addition, we show that the behaviour of smartphone battery temperature in cold settings is consistent across different smartphone models and battery configurations. Our method can be used to determine cold-induced situational impairments, and trigger interface adaptations during mobile interaction.