SEAbIRD: Sensor Activity Identification from Streams of Data

Molano-Pulido, José; Jiménez-Guarín, Claudia

doi:10.1007/978-3-319-66562-7_5

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In light of wearable smart sensors and ambient assisted living, great advances have been made in modeling human behavior patterns, in particular in the space of activity modeling and tracking or recommender systems [23]. These works have focused in modeling human behavior through repeated actions from captured videos, images, or websites [20], [24]- [27]; action prediction [20], [28], [29]; periodic action prediction [16], [25], [26]; or learning context [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data

2019

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Extracting indicative characteristics from the sensor data provide diverse avenues for improving the well-being of the elderly people living alone in their homes through understanding and identifying their behavioral patterns while considering any environmental changes. In this paper, we present a new model to explore the challenges associated with mining patterns from the body sensor data and their potential use in discovering regular human routines through mining periodic patterns from a non-uniform temporal database. The non-uniform nature of the temporal database adds more challenges to the mining of periodic patterns as the items may have different periodicity and frequency occurrences. Another challenge is how to discover the correlation between the discovered patterns. In addition, we examine the contextenriched periodic patterns which provide more insights about residents' health. A new algorithm for the contextualized-correlated periodic pattern mining from a non-uniform temporal database is presented along with an extensive evaluation of its performance using a real-life dataset.

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