Analytical Review on Ontological Human Activity Recognition Approaches

Keyvanpour, Mohammad Reza; Zolfaghari, Samaneh; Zall, Raziyeh

doi:10.4018/ijebr.2017040104

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…In smart homes, assistive systems must possess cognitive capabilities to understand dynamic situations in both temporal and spatial dimensions, compensating for potential cognitive limitations of inhabitants [83]. This necessitates data that is interpretable and processable by these systems.…”

Section: Semantic-based Representation and Object Extractionmentioning

confidence: 99%

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

Zolfaghari,

Stoev,

Yordanova

2024

IEEE Access

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With the global population aging, the demand for technologies facilitating independent living, especially for those with cognitive impairments, is increasing. This paper addresses this need by conducting a comprehensive evaluation of the Rostock Kitchen Task Assessment dataset, a pivotal resource in kitchen task activity recognition. Our study begins with an in-depth introduction, emphasizing the increasing prevalence of neurodegenerative disorders and the crucial role of assistive technologies. Our contributions encompass a systematic literature review, design and implementation of a working prototype of our envisioned system, refinement of the Rostock Kitchen Task Assessment dataset, creation of a semantically annotated dataset, extraction of statistical features, comparative analysis, and rigorous model performance assessment. The core of our work is the thorough evaluation and benchmarking of different activity recognition approaches using the aforementioned Rostock Kitchen Task Assessment dataset. Our experimental results demonstrate that despite encountering an imbalance problem in the dataset, the fusion of the Hidden Markov Model and Random Forest leads to superior results, achieving a weightedaveraged F 1 -score of 74.10% for all available activities and 81.40% for the most common actions in the Rostock Kitchen Task Assessment dataset. Moreover, through systematic analysis, we identify strengths and suggest potential refinements, thereby advancing the field of kitchen activity recognition. This offers valuable insights for researchers and practitioners in assistive and remote care technologies.

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Section: Semantic-based Representation and Object Extractionmentioning

confidence: 99%

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

Zolfaghari,

Stoev,

Yordanova

2024

IEEE Access

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“…These approaches can handle temporary and uncertain data and create dynamic and personalized activity models [7]. However, they require a large amount of data for training and learning, and learned activity models cannot be easily applied to new users, which leads to scalability and reusability issues [29,30,32]. A challenging issue in data-driven HAR approaches involves the extraction of expressive features from spatiotemporal sensor data [23,33,34].…”

Section: Related Workmentioning

confidence: 99%

“…Knowledge-based approaches build activity models by exploiting prior knowledge, using knowledge engineering and management technologies [29,32]. Their models are easily explainable, and they do not require the collection of large amounts of data.…”

Section: Related Workmentioning

confidence: 99%

Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces

2023

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The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.

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“…Zolfaghari et al [58] proposed six criteria to evaluate human activity recognition models and frameworks and compare them with each other. Here, we exploit these benchmarks to show the proposed system's advantages and drawbacks.…”

Section: Model Evaluationmentioning

confidence: 99%

Ontological, Fully Probabilistic Knowledge Model for Human Activity Recognition

Foudeh

Salim²

2023

Jurnal Teknologi

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Efficiency and scalability are obstacles that have not yet received a viable response from the human activity recognition research community. This paper proposes an activity recognition method. The knowledge model is in the form of ontology, the state-of-the-art in knowledge representation and reasoning. The ontology starts with probabilistic information about subjects’ low-level activities and location and then is populated with the assertion axioms learned from data or defined by the user. Unlike methods that choose only the most probable candidate from sensor readings, the proposed method keeps multiple candidates with the known degree of confidence for each one and involves them in decision making. Using this method, the system is more flexible to deal with unreliable data, readings from sensors, and the final recognition rate is improved. Besides, to resolve the scalability problem, a system is designed and implemented to do reasoning and storing in a relational database management system. Numerical evaluations and conceptual benchmarking prove the proposed system feasibility.

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Analytical Review on Ontological Human Activity Recognition Approaches

Cited by 9 publications

References 30 publications

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces

Ontological, Fully Probabilistic Knowledge Model for Human Activity Recognition

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