A probabilistic interval-based event calculus for activity recognition

Activity recognition refers to the detection of temporal combinations of ‘low-level’ or ‘short-term’ activities on sensor data. Various types of uncertainty exist in activity recognition systems and this often leads to erroneous detection. Typically, the frameworks aiming to handle uncertainty compu...

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Bibliographic Details
Published in:Annals of mathematics and artificial intelligence Vol. 89; no. 1-2; pp. 29 - 52
Main Authors: Artikis, Alexander, Makris, Evangelos, Paliouras, Georgios
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2021
Springer
Springer Nature B.V
Subjects:
Activity recognition
Algorithms
Artificial Intelligence
Calculus
Complex Systems
Computer Science
Credibility
Datasets
Intervals
Logic programming
Mathematical functions
Mathematics
Probability
Sensors
Statistical analysis
Uncertainty
Action languages
Complex event recognition
Probabilistic logic programming
68T37
ISSN:1012-2443, 1573-7470
Online Access:Get full text
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Summary:Activity recognition refers to the detection of temporal combinations of ‘low-level’ or ‘short-term’ activities on sensor data. Various types of uncertainty exist in activity recognition systems and this often leads to erroneous detection. Typically, the frameworks aiming to handle uncertainty compute the probability of the occurrence of activities at each time-point. We extend this approach by defining the probability of a maximal interval and the credibility rate for such intervals. We then propose a linear-time algorithm for computing all probabilistic temporal intervals of a given dataset. We evaluate the proposed approach using a benchmark activity recognition dataset, and outline the conditions in which our approach outperforms time-point-based recognition.
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-019-09664-4