Sasank Reddy

Using mobile phones to determine transportation modes

As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6% when tested on a dataset obtained from sixteen individuals.

Recruitment framework for participatory sensing data collections

Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm - participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus.

Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.

Biketastic: sensing and mapping for better biking

Bicycling is an affordable, environmentally friendly alternative transportation mode to motorized travel. A common task performed by bikers is to find good routes in an area, where the quality of a route is based on safety, efficiency, and enjoyment. Finding routes involves trial and error as well as exchanging information between members of a bike community. Biketastic is a platform that enriches this experimentation and route sharing process making it both easier and more effective. Using a mobile phone application and online map visualization, bikers are able to document and share routes, ride statistics, sensed information to infer route roughness and noisiness, and media that documents ride experience. Biketastic was designed to ensure the link between information gathering, visualization, and bicycling practices. In this paper, we present architecture and algorithms for route data inferences and visualization. We evaluate the system based on feedback from bicyclists provided during a two-week pilot.

Examining micro-payments for participatory sensing data collections

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.

Lifetrak: music in tune with your life

Advances in sensing technology and wider availability of network services is beckoning the use of context-awareness in ubiquitous computing applications. One region in which these technologies can play a major role is in the area of entertainment. Particularly, context-awareness can be used to provide higher quality interaction between humans and the media they are interacting with. We propose a music player, Lifetrak, that is in tune with a persons life by using a context-sensitive music engine to drive what music is played. This context engine is influenced by (i) the location of the user, (ii) the time of operation, (iii) the velocity of the user, and (iv) urban environment information such as traffic, weather, and sound modalities. Furthermore, we adjust the context engine by implementing a learning model that is based on user feedback on whether a certain song is appropriate for a particular context. Also, we introduce the idea of a context equalizer that adjusts how much a certain sensing modality affects what song is chosen. Since the music player will be implemented on a mobile device, there is a strong focus on creating a user interface that can be manipulated by users on the go. The goal of Lifetrak is to liberate a user from having to consciously specify the music that they want to play. Instead, Lifetrak intends to create a music experience for the user that is in rhythm with themselves and the space they reside in.

Determining transportation mode on mobile phones

As mobile phones advance in functionality and capability, they are increasingly being used as instruments for personal monitoring. Applications are being developed that take advantage of the sensing capabilities of mobile phones - many have accelerometers, location capabilities, imagers, and microphones - to infer contextual information. We focus on one type of context, the transportation mode of an individual, with the goal of creating a convenient (no requirement to place sensors externally or have specific position/orientation settings) classification system that uses a mobile phone with a GPS receiver and an accelerometer sensor to determine if an individual is stationary, walking, running, biking, or in motorized transport. The target application for this transportation mode inference involves assessing the hazard exposure and environmental impact of an individuals travel patterns. Our prototype classification system consisting of a decision tree followed by a first-order hidden Markov model achieves the application requirement of having accuracy level greater than 90% when testing with our dataset consisting of twenty hours of data collected across six individuals.

Ambulation: A Tool for Monitoring Mobility Patterns over Time Using Mobile Phones

An important tool for evaluating the health of patients who suffer from mobility-affecting chronic diseases such as MS, Parkinson’s, and Muscular Dystrophy is assessment of how much they walk. Ambulation is a mobility monitoring system that uses Android and Nokia N95 mobile phones to automatically detect the user’s mobility mode. The user’s only required interaction with the phone is turning it on and keeping it with him/her throughout the day, with the intention that it could be used as his/her everyday mobile phone for voice, data, and other applications, while Ambulation runs in the background. The phone uploads the collected mobility and location information to a server and a secure, intuitive web-based visualization of the data is available to the user and any family, friends or caregivers whom they authorize, allowing them to identify trends in their mobility and measure progress over time and in response to varying treatments.

Using Context Annotated Mobility Profiles to Recruit Data Collectors in Participatory Sensing

Mobile phones and accompanying network layers provide a platform to capture and share location, image, and acoustic data. This substrate enables participatory sensing: coordinated data gathering by individuals and communities to explore the world around them. Realizing such widespread and participatory sensing poses difficult challenges. In this paper, we discuss one particular challenge: creating a recruitment service to enable sensing organizers to select well-suited participants. Our approach concentrates on finding participants based on geographic and temporal coverage, as determined by context-annotated mobility profiles that model transportation mode, location, and time. We outline a three-stage recruitment framework designed to be parsimonious so as to limit risk to participants by reducing the location and context information revealed to the system. Finally, we illustrate the utility of the framework, along with corresponding modeling technique for mobility information, by analyzing data from a pilot mobility study consisting of ten users.

A framework for data quality and feedback in participatory sensing

The rapid adoption of mobile phones by society over the last decade and the increasing ability to capture, classifying, and transmit a wide variety of data (image, audio, and location) have enabled a new sensing paradigm - where humans carrying mobile phones can act as sensor systems. Human-in-the-loop sensor systems raise many new challenges in areas of sensor data quality assessment, mobility and sampling coordination, and user interaction procedures.