Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

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Activity recognition using cell phone accelerometers

Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

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A survey of mobile phone sensing

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.

https://www.cs.dartmouth.edu/%7Esensorlab/pubs/cenceme_sensys08.pdf

Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

Sensor-Based Activity Recognition

Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.

The Mobile Sensing Platform: An Embedded Activity Recognition System

We integrate wireless sensor networks in an undergraduate embedded systems course that exposes students to an important emerging technology in the core of the computer-engineering curriculum.

The flock: mote sensors sing in undergraduate curriculum

Embedded systems are increasingly becoming connected through wireless networking. These devices now form the basis of many of today's consumer products including cell phones and video game controllers. In the "Software for Embedded Systems" class in the Department of Computer Science & Engineering at the University of Washington, we used the design of a multi-player video game as motivation for the principal concepts in wireless embedded systems. Each student in the class designed an accelerometer-based game controller and then, the class as a whole, developed a multi-player video game that allowed 28 players (the number of students in the course) to play simultaneously. In this paper, we first describe the context of the course and its goals followed by the hardware/software platform we used to realize the game controller. We then detail the pedagogical approach we used to collectively design the video game (loosely based on soccer) and conclude with the lessons learned from this group design experience and how we would enhance the project and course in the future.

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Multi-player soccer and wireless embedded systems

To simplify interfacing a variety of external sensors with consumer Android devices, we developed a user-level framework that streamlines both application and driver development by providing abstractions that separate responsibilities between the user application, sensor framework, and device driver. These abstractions simplify the creation of sensing applications and provide for a high level of customization and flexibility, thereby enabling a variety of wired and wireless sensors to be connected to mobile devices. To avoid issues with modifying locked consumer devices, the framework's driver architecture is implemented at the user-level. To increase the variety of mobile sensing applications, we have developed a reconfigurable interfacing board that allows the framework to communicate with external sensors that have low-level digital or analog interfaces. We discuss field deployments that leverage the framework to address global health issues.

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ODK sensors: an application-level sensor framework for Android devices

The Mobile Sensing Platform: An Embedded Activity Recognition System The MSP is a small wearable device designed for embedded activity recognition with the aim of broadly supporting context-aware ubiquitous computing applications.

Bruce Hemingway

Papers

The Mobile Sensing Platform: An Embedded Activity Recognition System

The flock: mote sensors sing in undergraduate curriculum

Multi-player soccer and wireless embedded systems

ODK sensors: an application-level sensor framework for Android devices

The Mobile Sensing Platform: An Embedded Activity Recognition System The MSP is a small wearable device designed for embedded activity recognition with the aim of broadly supporting context-aware ubiquitous computing applications.