Long-Term Activity Recognition from Wristwatch Accelerometer Data

Clicks: 256
ID: 110796
2014
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Abstract
With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user’s needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view.
Reference Key
garcia-ceja2014sensorslong-term Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Enrique Garcia-Ceja;Ramon F. Brena;Jose C. Carrasco-Jimenez;Leonardo Garrido;Garcia-Ceja, Enrique;Brena, Ramon F.;Carrasco-Jimenez, Jose C.;Garrido, Leonardo;
Journal sensors
Year 2014
DOI
10.3390/s141222500
URL
https://www.mdpi.com/1424-8220/14/12/22500##related_content
Keywords
clustering crf activity recognition accelerometer sensor long-term activities hmm viterbi subclassing watch context-aware artificial intelligence Monitoring National Center for Biotechnology Information NCBI NLM MEDLINE humans pubmed abstract nih national institutes of health national library of medicine models statistical reproducibility of results research support non-u.s. gov't algorithms enrique garcia-ceja sensitivity and specificity time factors pmid:25436652 pmc4299024 doi:10.3390/s141222500 ramon f brena leonardo garrido accelerometry / methods* actigraphy / methods* computer simulation data interpretation longitudinal studies markov chains ambulatory / methods* motor activity / physiology* pattern recognition automated / methods*

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