Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning

Riboni, Daniele; Sztyler, Timo; Civitarese, Gabriele; Stuckenschmidt, Heiner

doi:10.1145/2971648.2971691

Cited by 79 publications

(54 citation statements)

References 28 publications

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“…References [32] and [33] have adopted MLN to recognize the activities, but they work mainly for sequence activities without the probability learning which is just the knowledge-driven method. Civitarese et al have presented the duration concept by calculating the difference between the beginning time and ending time [20].…”

Section: Simplifying Rule Expression (Subrules)mentioning

confidence: 99%

A hybrid approach to inferring the Internet of Things for complex activity recognition

Ning

Zhu

et al. 2019

J Wireless Com Network

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With the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel "Markov logic network" combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.

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Section: Simplifying Rule Expression (Subrules)mentioning

confidence: 99%

A hybrid approach to inferring the Internet of Things for complex activity recognition

Ning

Zhu

et al. 2019

J Wireless Com Network

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show abstract

“…The strong point of this category of techniques is that they are good at handling the intrinsic noise and uncertainty of sensor data. The main drawback is that a large annotated dataset of ADLs should be acquired to capture most execution patterns in different situations [19]. Indeed, activity execution patterns are strongly coupled to the individuals characteristics and home environment, and the portability of activity datasets is an open issue [20].…”

Section: Adls Recognitionmentioning

confidence: 99%

“…In Chapter 4, we propose an unsupervised method which overcomes the limitations of data-driven and knowledge-driven approaches [19]. First, it does not need the acquisition of an expensive labeled dataset.…”

Section: My Contributionsmentioning

confidence: 99%

Human Activity Recognition in Smart-Home Environments for Health-Care Applications

Civitarese¹

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This thesis presents several contributions on this topic. The major ones are two novel hybrid ADLs recognition algorithms. The former is supervised while the latter is unsupervised. Preliminary results, which still need to be confirmed, show that the recognition rate of the unsupervised method is comparable to the one obtained by the supervised one, with the great advantage of not requiring the acquisition of an annotated dataset. Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are presented. Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors. Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented. Experimental results on several datasets show the effectiveness of all the proposed techniques.ii

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“…In [17], authors present an approach through ontological and probabilistic reasoning, which requires a relevant knowledge engineering effort to define a comprehensive ontology of activities, home environment, and sensor events. This work does not include real-time evaluation presenting a results based on sensor events of 81%.…”

Section: Introductionmentioning

confidence: 99%

Real-time Recognition of Interleaved Activities Based on Ensemble Classifier of Long Short-Term Memory with Fuzzy Temporal Windows

et al. 2018

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In this paper, we present a methodology for Real-Time Activity Recognition of Interleaved Activities based on Fuzzy Logic and Recurrent Neural Networks. Firstly, we propose a representation of binary-sensor activations based on multiple Fuzzy Temporal Windows. Secondly, an ensemble of activity-based classifiers for balanced training and selection of relevant sensors is proposed. Each classifier is configured as a Long Short-Term Memory with self-reliant detection of interleaved activities. The proposed approach was evaluated using well-known interleaved binary-sensor datasets comprised of activities of daily living.

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Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning

Cited by 79 publications

References 28 publications

A hybrid approach to inferring the Internet of Things for complex activity recognition

A hybrid approach to inferring the Internet of Things for complex activity recognition

Human Activity Recognition in Smart-Home Environments for Health-Care Applications

Real-time Recognition of Interleaved Activities Based on Ensemble Classifier of Long Short-Term Memory with Fuzzy Temporal Windows

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