Publication

OPPORTUNITY: Towards opportunistic activity and context recognition systems

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Daniel Roggen, Yu Fang
2009
Conference Papers

Abstract

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project’s objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

Official source

https://infoscience.epfl.ch/entities/publication/8e3caf0d-4cc8-4046-af4d-edb3d5407dd4

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José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Daniel Roggen, Yu Fang
2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/8e3caf0d-4cc8-4046-af4d-edb3d5407dd4

About this result

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Data processing

Data processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer. The term "Data Processing", or "DP" has also been used to refer to a department within an organization responsible for the operation of data processing programs. Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant.

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Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results.

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