23 research outputs found
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either \emph{(a)
personalized medicine for individuals} or \emph{(b) public health for
populations}, in this work we review the design of these mobile sensing apps,
and propose to categorize the design of these apps/systems in two paradigms --
\emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both
sensing paradigms might incorporate with common ubiquitous sensing
technologies, such as wearable sensors, mobility monitoring, mobile data
offloading, and/or cloud-based data analytics to collect and process sensing
data from individuals, we present a novel taxonomy system with two major
components that can specify and classify apps/systems from aspects of the
life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \&
Participation}, \emph{(2) Health Surveillance \& Data Collection}, and
\emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different
goals of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the configurations
and interactions between these two components. In addition to summarization,
the proposed taxonomy system also helps figure out the potential directions of
mobile sensing for health from both personalized medicines and population
health perspectives.Comment: Submitted to a journal for revie
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
Delivering treatment recommendations via pervasive electronic devices such as
mobile phones has the potential to be a viable and scalable treatment medium
for long-term health behavior management. But active experimentation of
treatment options can be time-consuming, expensive and altogether unethical in
some cases. There is a growing interest in methodological approaches that allow
an experimenter to learn and evaluate the usefulness of a new treatment
strategy before deployment. We present the first development of a treatment
recommender system for emotion regulation using real-world historical mobile
digital data from n = 114 high socially anxious participants to test the
usefulness of new emotion regulation strategies. We explore a number of offline
contextual bandits estimators for learning and propose a general framework for
learning algorithms. Our experimentation shows that the proposed doubly robust
offline learning algorithms performed significantly better than baseline
approaches, suggesting that this type of recommender algorithm could improve
emotion regulation. Given that emotion regulation is impaired across many
mental illnesses and such a recommender algorithm could be scaled up easily,
this approach holds potential to increase access to treatment for many people.
We also share some insights that allow us to translate contextual bandit models
to this complex real-world data, including which contextual features appear to
be most important for predicting emotion regulation strategy effectiveness.Comment: Accepted at RecSys 202
Energy Optimization for Outdoor Activity Recognition
The mobile phone is no longer only a communication device, but also a powerful environmental sensing unit that can monitor a user’s ambient context. Mobile users take their devices with them everywhere which increases the availability of persons’ traces. Extracting and analyzing knowledge from these traces represent a strong support for several applications domains, ranging from traffic management to advertisement and social studies. However, the limited battery capacity of mobile devices represents a big hurdle for context detection, no matter how useful the service may be. We present a novel approach to online recognizing users’ outdoor activities without depleting the mobile resources. We associate the places visited by individuals during their movements with meaningful human activities using a novel algorithm that clusters incrementally user’s moves into different types of activities. To optimize the battery consumption, the algorithm behaves variably on the basis of users’ behaviors and the remaining battery level. Studies using real GPS records from two big datasets demonstrate that the proposal is effective and is capable of inferring human activities without draining the phone resources
Battery-Aware Mobile Solution for Online Activity Recognition from Users’ Movements
One of the unique features of mobile applications is location awareness. Mobile users take their devices with them everywhere which increases the availability of persons' traces. Extracting and analyzing knowledge from these traces represent a strong support for several application domains, ranging from traffic management to advertisement and social studies. We present a novel approach to online recognition of users' outdoor activities without depleting mobile resources. We associate the places visited by individuals during their movements to meaningful human activities using a novel algorithm that incrementally clusters a user's moves into different types of activities. To optimize battery consumption, the algorithm behaves variably according to users' behaviours and the remaining battery level. Studies using real GPS records from a spatio-temporal region demonstrate that the proposal is effective and is capable of inferring human activities without draining the phone resources
Online recognition of people's activities from raw GPS data: semantic trajectory data analysis
The widespread use of GPS devices leads to an increasing availability of people traces. The GPS trajectory of a moving object is a time stamped sequence of latitude and longitude coordinates. The analysis and extraction of knowledge from GPS trajectories is important for several applications domains, ranging from traffic management to advertisement and social studies. We present an approach capable of incrementally extracting semantic locations from people's trajectories and inferring the activity done by users. We associate the places visited by people during their movements to a meaningful human activities using a novel algorithm that cluster incrementally user's moves into different type of activities. Studies using GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of inferring human activities without depleting the phone resources
Online Prediction of People’s Next Point-of-Interest: Concept Drift Support
Current advances in location tracking technology provide exceptional amount of data about the users’ movements. The volume of geospatial data collected from moving users’ challenges human ability to analyze the stream of input data. Therefore, new methods for online mining of moving object data are required. One of the popular approaches available for moving objects is the prediction of the unknown future location of an object. In this paper we present a new method for online prediction of users’ next important locations to be visited that not only learns incrementally the users’ habits, but also detects and supports the drifts in their patterns. Our original contribution includes a new algorithm of online mining association rules that support the concept drift
Prediction of next destinations from irregular Patterns
Few decades ago, understanding human behaviors was considered as a mystery where predicting people's future was impossible. Many changes have been noticed since that era. Thanks to current advances in location tracking technology and data mining techniques, predicting users' behaviors has become possible. In this paper we present a new algorithm to online predict users' next visited locations that not only learns incrementally the users' habits, but also detects and supports the drifts in their patterns. Our original contribution includes a new algorithm for online association rules mining that supports the concept drift
Un environnement d'exploration visuelle de données spatio-temporelles issues de simulation
International audienc