Mobile Context Inference Using Low-Cost Sensors

Welbourne, Evan; Lester, Jonathan; LaMarca, Anthony; Borriello, Gaetano

doi:10.1007/11426646_24

Cited by 38 publications

(30 citation statements)

References 15 publications

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“…For example, the coefficients from 0.5 Hz to 3 Hz can be used as the key discriminating coefficients for the running and walking activities [47,62].…”

Section: Spectral Analysis Of Key Coefficientsmentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

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Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

486

347

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The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care, and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers it is possible to determine if a user is walking or running at a specific time of day. By correlating this knowledge with GPS data it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

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“…For example, the coefficients from 0.5 Hz to 3 Hz can be used as the key discriminating coefficients for the running and walking activities [47,62].…”

Section: Spectral Analysis Of Key Coefficientsmentioning

confidence: 99%

Preprocessing techniques for context recognition from accelerometer data

Figo

Diniz

Ferreira

et al. 2010

Pers Ubiquit Comput

486

347

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“…Regarding the inference of user activities such as "walking" or "running", there have been a myriad of approaches, ranging from simple processing steps and threshold operations [14,15,2] to the use of neural networks as a clustering algorithm [11]; or even using non-supervised time-series segmentation [16]. As an example, the work presented in [1] infers activities such as "walking", "running", "standing", and "sitting" with a single 3-axis accelerometer, claiming an accuracy of 96%.…”

Section: Related Workmentioning

confidence: 99%

“…In the work presented in this article, we extract signal features using techniques similar to those described in [2,15]. For context inference we combine signal-processing and machine-learning techniques, using decision trees [21] to fuse features and to identify user activities.…”

Section: Related Workmentioning

confidence: 99%

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Providing user context for mobile and social networking applications

Santos

Cardoso

Ferreira

et al. 2010

Pervasive and Mobile Computing

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The processing capabilities of mobile devices coupled with portable and wearable sensors provide the basis for new context-aware services and applications tailored to the user environment and its daily activities. In this article, we describe the approach developed within the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth to provide user contexts. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information in web-centered servers that support well-known social networking services. In the current prototype, context inference is based on decision trees to learn and to identify contexts dynamically at run-time, but the middleware allows the integration of different inference engines if necessary. Experimental results in a real-world setting suggest that the proposed solution is a promising approach to provide user context to local mobile applications as well as to network-level applications such as social networking services.

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“…It processes the Wi-Fi signal strengths received by a standard network cards received from 802.11 base stations using a signal propagation modeling method to calculate the mobile users' positions and is capable of achieving a median error distance of 2 to 3 meters. In the Place Lab [25] based system described in [26], signals from cell phone networks, Wi-Fi signals, and data from an accelerometer are fused to provide location information with an average accuracy of 20 to 30 meters. Using Wi-Fi, fingerprinting systems like RADAR can improve the resolution to 1 to 2 meters.…”

Section: Indoor Localization and Navigation Aidsmentioning

confidence: 99%

ShopTalk: Independent Blind Shopping Through Verbal Route Directions and Barcode Scans

Nicholson¹,

Kulyukin²,

Coster³

2009

TOREHJ

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Independent shopping in modern grocery stores that carry thousands of products is a great challenge for people with visual impairments. ShopTalk is a proof-of-concept wearable system designed to assist visually impaired shoppers with finding shelved products in grocery stores. Using synthetic verbal route directions and descriptions of the store layout, ShopTalk leverages the everyday orientation and mobility skills of independent visually impaired travelers to direct them to aisles with target products. Inside aisles, an off-the-shelf barcode scanner is used in conjunction with a software data structure, called a barcode connectivity matrix, to locate target product on shelves. Two experiments were performed at a real world supermarket. A successful earlier single-subject experiment is summarized and a new experiment involving ten visually impaired participants is presented. In both experiments, ShopTalk was successfully used to guide visually impaired shoppers to multiple products located in aisles on shelves. ShopTalk is a feasible system for guiding visually impaired shoppers who are skilled, independent travelers. Its design does not require any hardware instrumentation of the store and leads to low installation and maintenance costs.

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Mobile Context Inference Using Low-Cost Sensors

Cited by 38 publications

References 15 publications

Preprocessing techniques for context recognition from accelerometer data

Preprocessing techniques for context recognition from accelerometer data

Providing user context for mobile and social networking applications

ShopTalk: Independent Blind Shopping Through Verbal Route Directions and Barcode Scans

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