|
Modeling and reasoning with ProbLog: an application in recognizing complex activities
Sztyler, Timo
;
Civitarese, Gabriele
;
Stuckenschmidt, Heiner
|
DOI:
|
https://doi.org/10.1109/PERCOMW.2018.8480299
|
|
URL:
|
https://ieeexplore.ieee.org/document/8480299
|
|
Document Type:
|
Conference or workshop publication
|
|
Year of publication:
|
2018
|
|
Book title:
|
2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 : Athens, Greece, March 19-23, 2018
|
|
Page range:
|
259-264
|
|
Conference title:
|
CoMoRea '18: 14th Workshop on Context and Activity Modeling and Recognition : affiliated to IEEE PerCom 2018
|
|
Location of the conference venue:
|
Athens, Greece
|
|
Date of the conference:
|
19.-23.03.2018
|
|
Place of publication:
|
Piscataway, NJ
|
|
Publishing house:
|
IEEE Computer Society
|
|
ISBN:
|
978-1-5386-3228-4 , 978-1-5386-3227-7 , 978-1-5386-3226-0
|
|
Publication language:
|
English
|
|
Institution:
|
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
|
|
Subject:
|
004 Computer science, internet
|
|
Abstract:
|
Smart-home activity recognition is an enabling tool for a wide range of ambient assisted living applications. The recognition of ADLs usually relies on supervised learning or knowledge-based reasoning techniques. In order to overcome the well-known limitations of those two approaches and, at the same time, to combine their strengths to improve the recognition rate, many researchers investigated Markov Logic Networks (MLNs). However, MLNs require a non-trivial effort by experts to properly model probabilities in terms of weights. In this paper, we propose a novel method based on ProbLog. ProbLog is a probabilistic extension of Prolog, which allows to explicitly define probabilistic facts and rules. With respect to MLN, the inference mode of ProbLog is based on the closed-world assumption and it has faster response times. We propose a simple and flexible ProbLog model, which we exploit to recognize complex ADLs in an online fashion. Considering a dataset with 21 subjects, our results show that our method reaches high F-measure (83%). Moreover, we also show that the response time of ProbLog is satisfying for real-time applications.
|
 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Sztyler, Timo
;
Civitarese, Gabriele
;
Stuckenschmidt, Heiner
Google Scholar:
Sztyler, Timo
;
Civitarese, Gabriele
;
Stuckenschmidt, Heiner
ORCID:
Sztyler, Timo, Civitarese, Gabriele and Stuckenschmidt, Heiner  ORCID: https://orcid.org/0000-0002-0209-3859
You have found an error? Please let us know about your desired correction here: E-Mail
Actions (login required)
 |
Show item |
|
|
