17,008 research outputs found
Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity
Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201
Smart Sensing Systems for the Daily Drive
When driving, you might sometimes wonder, "Are there any disruptions on my regular route that might delay me, and will I be able to find a parking space when I arrive?" Two smartphone-based prototype systems can help answer these questions. The first is ParkSense, which can be used to sense on-street parking-space occupancy when coupled with electronic parking payment systems. The second system can sense and recognize a user's repeated car journeys, which can be used to provide personalized alerts to the user. Both systems aim to minimize the impact of sensing tasks on the device's lifetime so that the user can continue to use the device for its primary purpose. This department is part of a special issue on smart vehicle spaces
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Digital maps have become a part of our daily life with a number of commercial
and free map services. These services have still a huge potential for
enhancement with rich semantic information to support a large class of mapping
applications. In this paper, we present Map++, a system that leverages standard
cell-phone sensors in a crowdsensing approach to automatically enrich digital
maps with different road semantics like tunnels, bumps, bridges, footbridges,
crosswalks, road capacity, among others. Our analysis shows that cell-phones
sensors with humans in vehicles or walking get affected by the different road
features, which can be mined to extend the features of both free and commercial
mapping services. We present the design and implementation of Map++ and
evaluate it in a large city. Our evaluation shows that we can detect the
different semantics accurately with at most 3% false positive rate and 6% false
negative rate for both vehicle and pedestrian-based features. Moreover, we show
that Map++ has a small energy footprint on the cell-phones, highlighting its
promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on
Sensing, Communication, and Networking (IEEE SECON 2014
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
Mining user activity as a context source for search and retrieval
Nowadays in information retrieval it is generally accepted that if we can better
understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval
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