Smart-homectivity 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.

Modeling and Reasoning with ProbLog: An Application in Recognizing Complex Activities / T. Sztyler, G. Civitarese, H. Stuckenschmidt - In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)[s.l] : IEEE, 2018 Oct 08. - ISBN 9781538632277. - pp. 259-264 (( convegno IEEE PerCom tenutosi a Athens nel 2018.

Modeling and Reasoning with ProbLog: An Application in Recognizing Complex Activities

G. Civitarese;
2018-10-08

Abstract

Smart-homectivity 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/595101
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