Time series segmentation for context recognition in mobile devices

Himberg, Johan; Korpiaho, K.; Mannila, Heikki; Tikanmaki, J.; Toivonen, Hannu

doi:10.1109/icdm.2001.989520

Cited by 152 publications

(106 citation statements)

References 13 publications

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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%

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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Section: Related Workmentioning

confidence: 99%

Providing user context for mobile and social networking applications

Santos

Cardoso

Ferreira

et al. 2010

Pervasive and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…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 [13,14,2] to the use of neural networks as a clustering algorithm [10]; or even using non-supervised time-series segmentation [15]. As an example, the work presented in [1] infers activities such as "walking", "running", "standing", and "sitting" with a single 3-axis accelerometer.…”

Section: Related Workmentioning

confidence: 99%

Mobile Context Provider for Social Networking

Cardoso

Ferreira

Diniz

2009

Lecture Notes in Computer Science

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Abstract. The ability to infer user context based on a mobile device together with a set of external sensors opens up the way to new contextaware services and applications. In this paper, we describe a mobile context provider that makes use of sensors available in a smartphone as well as sensors externally connected via bluetooth. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information to well-known social networking services such as Twitter and Hi5. In the current prototype, context inference is based on decision trees, but the middleware allows the integration of other inference engines. Experimental results suggest that the proposed solution is a promising approach to provide user context to both local and network-level services.

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“…Many other formulations exist and the problem has been studied extensively in various settings. To name a few approaches, Himberg et al [12] compare a large number of different algorithms on real-world mobiledevice data. Keogh et al [13] show how one can use segmentation in order to obtain efficient indexing of time series.…”

Section: Related Workmentioning

confidence: 99%

“…Segmentation algorithms are widely used for extracting structure from sequences; there exist a variety of applications where this approach has been taken [12,13,16,17,19,20]. Sequence segmentation is suitable in the numerous cases where the underlying process producing the sequence has several relatively stable states, and in each state the sequence can be assumed to be described by a simple model.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and dimensionality reduction

Bingham¹,

Gionis²,

Haiminen³

et al. 2006

Proceedings of the 2006 SIAM International Conference on Data Mining

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Sequence segmentation and dimensionality reduction have been used as methods for studying high-dimensional sequences -they both reduce the complexity of the representation of the original data. In this paper we study the interplay of these two techniques. We formulate the problem of segmenting a sequence while modeling it with a basis of small size, thus essentially reducing the dimension of the input sequence. We give three different algorithms for this problem: all combine existing methods for sequence segmentation and dimensionality reduction. For two of the proposed algorithms we prove guarantees for the quality of the solutions obtained. We describe experimental results on synthetic and real datasets, including data on exchange rates and genomic sequences. Our experiments show that the algorithms indeed discover underlying structure in the data, including both segmental structure and interdependencies between the dimensions.

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Time series segmentation for context recognition in mobile devices

Cited by 152 publications

References 13 publications

Providing user context for mobile and social networking applications

Providing user context for mobile and social networking applications

Mobile Context Provider for Social Networking

Segmentation and dimensionality reduction

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