4,842 research outputs found
mCollector: Sensor-enabled health-data collection system for rural areas in the developing world
Health data collection poses unique challenges in rural areas of the developing world. mHealth systems that are used by health workers to collect data in remote rural regions should also record contextual information to increase confidence in the fidelity of the collected data. We built a user-friendly, mobile health-data collection system using wireless medical sensors that interface with an Android application. The data-collection system was designed to support minimally trained, non-clinical health workers to gather data about blood pressure and body weight using off-the-shelf medical sensors. This system comprises a blood-pressure cuff, a weighing scale and a portable point-of-sales printer. With this system, we introduced a new method to record contextual information associated with a blood-pressure reading using a tablet’s touchscreen and accelerometer. This contextual information can be used to verify that a patient’s lower arm remained well-supported and stationary during her blood-pressure measurement. This method can allow mHealth applications to guide untrained patients (or health workers) in measuring blood pressure correctly. Usability is a particularly important design and deployment challenge in remote, rural areas, given the limited resources for technology training and support. We conducted a field study to assess our system’s usability in rural India, where we logged health worker interactions with the app’s interface using an existing usability toolkit. Researchers analyzed logs from this toolkit to evaluate the app’s user experience and quantify specific usability challenges in the app. We have recorded experiential notes from the field study in this document. We present four contributions to future mHealth projects in this document: \u3e We describe a method for measuring lower-arm stillness and support during a blood-pressure measurement, using an off-the-shelf Android tablet. \u3e We evaluate our method for measuring lower-arm stillness with a preliminary user study of 12 subjects and found that our method can distinguish stationary arms from different types of lower-arm movement with 90% accuracy. \u3e We conduct an experiential study with 28 participants and three app operators. In this study, we evaluate our system’s field usability by deploying it in rural India. \u3e We provide a quantitative usability analysis of our mobile-data-collection app’s interface using an existing usability toolkit
PainDroid: An android-based virtual reality application for pain assessment
Earlier studies in the field of pain research suggest that little efficient intervention currently exists in response to the exponential increase in the prevalence of pain. In this paper, we present an Android application (PainDroid) with multimodal functionality that could be enhanced with Virtual Reality (VR) technology, which has been designed for the purpose of improving the assessment of this notoriously difficult medical concern. Pain- Droid has been evaluated for its usability and acceptability with a pilot group of potential users and clinicians, with initial results suggesting that it can be an effective and usable tool for improving the assessment of pain. Participant experiences indicated that the application was easy to use and the potential of the application was similarly appreciated by the clinicians involved in the evaluation. Our findings may be of considerable interest to healthcare providers, policy makers, and other parties that might be actively involved in the area of pain and VR research
Mobile support in CSCW applications and groupware development frameworks
Computer Supported Cooperative Work (CSCW) is an established subset of the field of Human Computer Interaction that deals with the how people use computing technology to enhance group interaction and collaboration. Mobile CSCW has emerged as a result of the progression from personal desktop computing to the mobile device platforms that are ubiquitous today.
CSCW aims to not only connect people and facilitate communication through using computers; it aims to provide conceptual models coupled with technology to manage, mediate, and assist collaborative processes. Mobile CSCW research looks to fulfil these aims through the adoption of mobile technology and consideration for the mobile user. Facilitating collaboration using mobile devices brings new challenges. Some of these challenges are inherent to the nature of the device hardware, while others focus on the understanding of how to engineer software to maximize effectiveness for the end-users. This paper reviews seminal and state-of-the-art cooperative software applications and development frameworks, and their support for mobile devices
Dynamic Deployment of Sensing Experiments in the Wild Using Smartphones
Part 1: Full Research PapersInternational audienceWhile scientific communities extensively exploit simulations to validate their theories, the relevance of their results strongly depends on the realism of the dataset they use as an input. This statement is particularly true when considering human activity traces, which tend to be highly unpredictable. In this paper, we therefore introduce APISENSE, a distributed crowdsensing platform for collecting realistic activity traces. In particular, APISENSE provides to scientists a participative platform to help them to easily deploy their sensing experiments in the wild. Beyond the scientific contributions of this platform, the technical originality of APISENSE lies in its Cloud orientation and the dynamic deployment of scripts within the mobile devices of the participants.We validate this platform by reporting on various crowdsensing experiments we deployed using Android smartphones and comparing our solution to existing crowdsensing platforms
Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications
Reducing network latency in mobile applications is an effective way of
improving the mobile user experience and has tangible economic benefits. This
paper presents PALOMA, a novel client-centric technique for reducing the
network latency by prefetching HTTP requests in Android apps. Our work
leverages string analysis and callback control-flow analysis to automatically
instrument apps using PALOMA's rigorous formulation of scenarios that address
"what" and "when" to prefetch. PALOMA has been shown to incur significant
runtime savings (several hundred milliseconds per prefetchable HTTP request),
both when applied on a reusable evaluation benchmark we have developed and on
real applicationsComment: ICSE 201
Simulation of a Context Aware Cellphone System
As the number of mobile devices we carry increase, the job of managing those devices throughout the day becomes cumbersome. This is especially true for cell phones. Despite the many benefits they provide, cell phones create problems that arise from a mismatch between the user’s context and the cell phone’s behavior. In large part, the mismatch occurs because owners do not remember to frequently update their cell phone configurations according to the current context. It is desirable for mobile devices to automatically configure themselves based on the context of the environment and user preferences. While automatic configuration of cell phones may prove too tedious as different sensors are involved, it still serves as an effective means of managing cell phones in different contextual environments. In this work, a context-aware cell phone was simulated and its response was tested in different environments to see if it would react accordingly. The sensor, media and audio manager framework of the android operating system were used in capturing data from the cellphone’s inbuilt sensor, and contextual information were derived from the data captured. A combination of these contextual information was used to deduce the likely activity the user is carrying out at a particular moment, and appropriate cellphone configuration. Keywords: Context, Context-Awareness, Cellphone, Android, Sensors, Platfor
PhysioVR: a novel mobile virtual reality framework for physiological computing
Virtual Reality (VR) is morphing into a ubiquitous
technology by leveraging of smartphones and screenless cases in
order to provide highly immersive experiences at a low price
point. The result of this shift in paradigm is now known as mobile
VR (mVR). Although mVR offers numerous advantages over
conventional immersive VR methods, one of the biggest
limitations is related with the interaction pathways available for
the mVR experiences. Using physiological computing principles,
we created the PhysioVR framework, an Open-Source software
tool developed to facilitate the integration of physiological signals
measured through wearable devices in mVR applications.
PhysioVR includes heart rate (HR) signals from Android
wearables, electroencephalography (EEG) signals from a low cost brain computer interface and electromyography (EMG)
signals from a wireless armband. The physiological sensors are
connected with a smartphone via Bluetooth and the PhysioVR
facilitates the streaming of the data using UDP communication
protocol, thus allowing a multicast transmission for a third party
application such as the Unity3D game engine. Furthermore, the
framework provides a bidirectional communication with the VR
content allowing an external event triggering using a real-time
control as well as data recording options. We developed a demo
game project called EmoCat Rescue which encourage players to
modulate HR levels in order to successfully complete the in-game
mission. EmoCat Rescue is included in the PhysioVR project
which can be freely downloaded. This framework simplifies the
acquisition, streaming and recording of multiple physiological
signals and parameters from wearable consumer devices
providing a single and efficient interface to create novel
physiologically-responsive mVR applications.info:eu-repo/semantics/publishedVersio
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