Chu Luo

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.

A data hiding approach for sensitive smartphone data

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

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.

Social-aware device-to-device communication: a contribution for edge and fog computing?

The exploitation of the opportunistic infrastructure via Device-to-Device (D2D) communication is a critical component towards the adoption of new paradigms such as edge and fog computing. While a lot of work has demonstrated the great potential of D2D communication, it is still unclear whether the benefits of the D2D approach can really be leveraged in practice. In this paper, we develop a software sensor, namely Detector, which senses the infrastructure in proximity of a mobile user. We analyze and evaluate D2D on the wild, i.e., not in simulations. We found that in a realistic environment, a mobile is always co-located in proximity to at least one other mobile device throughout the day. This suggests that a device can schedule tasks processing in coordination with other devices, potentially more powerful, instead of handling the processing of the tasks by itself.

Instrumenting smartphones with portable NIRS

In this paper we propose a mobile sensing solution that uses Near Infrared Spectroscopy (NIRS) and discuss its potential in future everyday use cases. The proposed design enables novice end users to classify various objects using NIRS and without prior knowledge of the technology itself. We describe how an instrument that traditionally has been used solely by trained lab personnel, can be commoditized to be used by any end user with a mobile device. The preliminary results indicate that samples can be identified with high accuracy, but that a series of implementation and design challenges must be first accounted for.

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.

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.

TestAWARE: A Laboratory-Oriented Testing Tool for Mobile Context-Aware Applications

Although mobile context instrumentation frameworks have simplified the development of mobile context-aware applications, it remains challenging to test such applications. In this paper, we present TestAWARE that enables developers to systematically test context-aware applications in laboratory settings. To achieve this, TestAWARE is able to download, replay and emulate contextual data on either physical devices or emulators. To support both white -box and black-box testing, TestAWARE has been implemented as a novel structure with a mobile client and code library. In blackbox testing scenarios, developers can manage data replay through the mobile client, without writing testing scripts or modifying the source code of the targeted application. In white-box testing scenarios, developers can manage data replay and test functional/non-functional properties of the targeted application by writing testing scripts using the code library. We evaluated TestAWARE by quantifying its maximal data replay speed, and by conducting a user study with 13 developers. We show that TestAWARE can overcome data synchronisation challenges, and found that PC-based emulators can replay data significantly faster than physical smartphones and tablets. The user study highlights the usefulness of TestAWARE in the systematic testing of mobile context-aware applications in laboratory settings.

Designing a context-aware assistive infrastructure for elderly care

We present an assistive healthcare platform, CARE, which aims to provide daily support for elderly caregivers with context-aware, unobtrusive, and actionable information. This information is collected through a plethora of IoT sensors installed strategically at an elderly care centre and is accessed through an Android tablet application. The applications goal is to empower nurses with a better understanding of elderly needs and ultimately, improve the care service. We investigate how IoT devices and sensors can enable a pervasive healthcare system, and discuss a wide-range of important parameters for integration of elderly care practices.