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Open AccessArticle

Long-Term Activity Recognition from Wristwatch Accelerometer Data

Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico
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Expanded conference paper based on “Long-Term Activities Segmentation Using Viterbi Algorithm with a k-Minimum-Consecutive-States Constraint”, the 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014).
Sensors 2014, 14(12), 22500-22524; https://doi.org/10.3390/s141222500
Received: 30 July 2014 / Revised: 18 October 2014 / Accepted: 14 November 2014 / Published: 27 November 2014
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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. View Full-Text
Keywords: activity recognition; long-term activities; accelerometer sensor; CRF; HMM; Viterbi; clustering; subclassing; watch; context-aware activity recognition; long-term activities; accelerometer sensor; CRF; HMM; Viterbi; clustering; subclassing; watch; context-aware
MDPI and ACS Style

Garcia-Ceja, E.; Brena, R.F.; Carrasco-Jimenez, J.C.; Garrido, L. Long-Term Activity Recognition from Wristwatch Accelerometer Data. Sensors 2014, 14, 22500-22524.

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