A probabilistic data-driven method for human activity recognition

Foudeh, Pouya; Khorshidtalab, Aida; Salim, Naomie

doi:10.3233/ais-180496

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…In the previous work [48], the Opportunity dataset was used to generate a new dataset with probabilistic predictions. Fast signal processing methods, suitable for batch processing, are applied to compute proper features from sensor signals.…”

Section: Applied Datasetmentioning

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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Section: Applied Datasetmentioning

confidence: 99%

Ontological, Fully Probabilistic Knowledge Model for Human Activity Recognition

Foudeh

Salim²

2023

Jurnal Teknologi

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“…The key data extracted from the literature includes the smart home datasets used; prediction algorithms; data structures used for modelling behaviour; and input data types e.g. sensors, 4 Python API search and de-duplication, https://github.com/robdunne-uom/cs-literature-search 5 Filter validation files, https://www.dropbox.com/sh/dfggv6b02kzyh5v/ AADetG-mtb3s0i7q6e3XkPU9a?dl=0…”

Section: Data Extractionmentioning

confidence: 99%

Computational methods for predicting human behaviour in smart environments

Dunne

Matthews

Vega

et al. 2023

AIS

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This systematic literature review presents the computational methods of human behaviour prediction research from Pentland and Liu’s seminal 1999 paper on human behaviour prediction to the latest research to date. The PRISMA framework for systematic reviews was used as the review methodology to structure this information aggregation. This review provides a high-level summary of the field with key areas identified for new research. The results show that there are frequently used datasets for training predictive models: MavHome, MavLab, LIARA, CASAS, PlaceLab, and REDD. Accuracies in the range of 43.9% to 100% for predictions of varying complexity. Common data structures for modelling behavioural data: Vectors, tables, trees, Markov models, and graphs. Algorithms that fall into three distinct categories: Machine Learning (NN, RL, LSTM), Probabilistic Graphical Models (namely Bayesian and Markov variants), and Statistical and Trend Analysis (ARIMA, Prophet). Additionally, we document other notably useful algorithms that fall outside of these three main categories including Jaro-Winkler and Levenshtein distances. Opportunities identified for further research include the use of audio as the data source for behaviour prediction methods, and applying times-series prediction machine learning algorithms (RNN, LSTM) to the smart home problem space.

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“…The experimental results indicated the competitiveness of their proposed method. In [28], the authors proposed a data-driven probabilistic model for recognizing low and medium level human activities. They evaluated their proposed model on the Opportunity dataset and obtained state-of-the-art results.…”

Section: Related Workmentioning

confidence: 99%

C2FHAR: Coarse-to-Fine Human Activity Recognition With Behavioral Context Modeling Using Smart Inertial Sensors

et al. 2020

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Smart sensing devices are furnished with an array of sensors, including locomotion sensors, which enable continuous and passive monitoring of human activities for the ambient assisted living. As a result, sensor-based human activity recognition has earned significant popularity in the past few years. A lot of successful research studies have been conducted in this regard. However, the accurate recognition of in-the-wild human activities in real-time is still a fundamental challenge to be addressed as human physical activity patterns are adversely affected by their behavioral contexts. Moreover, it is essential to infer a user's behavioral context along with the physical activity to enable context-aware and knowledge-driven applications in real-time. Therefore, this research work presents ''C2FHAR'', a novel approach for coarseto-fine human activity recognition in-the-wild, which explicitly models the user's behavioral contexts with activities of daily living to learn and recognize the fine-grained human activities. For addressing realtime activity recognition challenges, the proposed scheme utilizes a multi-label classification model for identifying in-the-wild human activities at two different levels, i.e., coarse or fine-grained, depending upon the real-time use-cases. The proposed scheme is validated with extensive experiments using heterogeneous sensors, which demonstrate its efficacy.

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A probabilistic data-driven method for human activity recognition

Cited by 6 publications

References 31 publications

Ontological, Fully Probabilistic Knowledge Model for Human Activity Recognition

Ontological, Fully Probabilistic Knowledge Model for Human Activity Recognition

Computational methods for predicting human behaviour in smart environments

C2FHAR: Coarse-to-Fine Human Activity Recognition With Behavioral Context Modeling Using Smart Inertial Sensors

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