The Mobile Sensing Platform: An Embedded Activity Recognition System

Choudhury, Tanzeem; Consolvo, Sunny; Harrison, Beverly L.; Hightower, Jeffrey; LaMarca, Anthony; Legrand, Louis; Rahimi, Asgar Bradaran; Rea, A.; Bordello, G.; Hemingway, Bruce; Klasnja, Predrag; Koscher, Karl; Landay, James A.; Lester, Jonathan; Wyatt, Danny; Haehnel, Dirk

doi:10.1109/mprv.2008.39

Cited by 507 publications

(262 citation statements)

References 5 publications

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“…to recognize the common user activities, with the use of bi-axial accelerometer and light sensor by Maurer et al [16]. Choudhury et al [8] used a model consisting of seven different sensors to recognize activities. Liu et al [17] have adopted smartphone sensors to analyze vehicle and pedestrian behaviors.…”

Section: Related Workmentioning

confidence: 99%

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Liu¹,

Zhou²,

Shakya³

et al. 2018

IJMLC

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Abstract-Smartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs.

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Section: Related Workmentioning

confidence: 99%

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Liu¹,

Zhou²,

Shakya³

et al. 2018

IJMLC

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“…We place either Mulle v3 sensor 4 (accelerometer) or the Android Phone with its inbuilt accelerometer on the user's waist to detect body motion using decision trees. We chose the waist of the user as the most appropriate position [2]. A Bluetooth RFID reader was used.…”

Section: Sacaar System Test-bed and Prototype Implementationmentioning

confidence: 99%

“…ADLs are the focus of several research areas such as health care, aged care, emergency, security and comfort [1][2][3]. The activities performed by human users are highly complex and multi-tasking comes naturally.…”

Section: Introductionmentioning

confidence: 99%

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Complex Activity Recognition Using Context Driven Activity Theory in Home Environments

Saguna

Zaslavsky

Chakraborty

2011

Smart Spaces and Next Generation Wired/Wireless Networking

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Abstract. This paper proposes a context driven activity theory (CDAT) and reasoning approach for recognition of concurrent and interleaved complex activities of daily living (ADL) which involves no training and minimal annotation during the setup phase. We develop and validate our CDAT using the novel complex activity recognition algorithm on two users for three weeks. The algorithm accuracy reaches 88.5% for concurrent and interleaved activities. The inferencing of complex activities is performed online and mapped onto situations in near real-time mode. The developed systems performance is analyzed and its behavior evaluated.

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“…It promises to have great impact on a variety of domains like elder care [6], fitness monitoring [9,14], and intelligent contextaware applications [5]. Recently several researchers have used the accelerometers embedded in smartphones to detect activities such as walking, standing, running and sitting with the goal of developing context-aware applications [8,10].…”

Section: Related Workmentioning

confidence: 99%

Coarse In-Building Localization with Smartphones

Parnandi

Le²,

Vaghela³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Geographic location of a person is important contextual information that can be used in a variety of scenarios like disaster relief, directional assistance, context-based advertisements, etc. GPS provides accurate localization outdoors but is not useful inside buildings. We propose an coarse indoor localization approach that exploits the ubiquity of smart phones with embedded sensors. GPS is used to find the building in which the user is present. The Accelerometers are used to recognize the user's dynamic activities (going up or down stairs or an elevator) to determine his/her location within the building. We demonstrate the ability to estimate the floor-level of a user. We compare two techniques for activity classification, one is naive Bayes classifier and the other is based on dynamic time warping. The design and implementation of a localization application on the HTC G1 platform running Google Android is also presented.

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The Mobile Sensing Platform: An Embedded Activity Recognition System

Cited by 507 publications

References 5 publications

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Complex Activity Recognition Using Context Driven Activity Theory in Home Environments

Coarse In-Building Localization with Smartphones

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