Future Activities Prediction Framework in Smart Homes Environment

Mohamed, Mahmoud A.; El-Kilany, Ayman; El-Tazi, Neamat

doi:10.1109/access.2022.3197618

Cited by 11 publications

(6 citation statements)

References 50 publications

(57 reference statements)

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“…An LSTM is particularly adept at processing sequences of data rather than data with less than three dimensions, such as images. As such it is well suited to time series data, like observed human behaviour in a smart home, and produces comparable results to algorithms stated in Table 7 [50] [51] [52] [13] [53]. The feature of LSTMs that make it especially good at behaviour prediction is the lower sensitivity to gaps between segments of data.…”

Section: Long Short Term Memory (Lstm)mentioning

confidence: 98%

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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Section: Long Short Term Memory (Lstm)mentioning

confidence: 98%

Computational methods for predicting human behaviour in smart environments

Dunne

Matthews

Vega

et al. 2023

AIS

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“…IoT-based SHS systems, where the user can monitor the home environment as well as control various home appliances such as lights, fans, and other switches, are already in development [137]. For better speech recognition in appliance control, researchers are working to develop a better speech recognition algorithm [160][161][162][163].…”

Section: Elderly and Physically Challenged Peoplementioning

confidence: 99%

Smart Home System: A Comprehensive Review

Chakraborty

Islam

Shahriyar

et al. 2023

Journal of Electrical and Computer Engineering

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Smart home is a habitation that has been outfitted with technological solutions that are intended to provide people with services that are suited to their needs. The purpose of this article is to perform a systematic assessment of the latest smart home literature and to conduct a survey of research and development conducted in this field. In addition to presenting a complete picture of the current smart home system’s (SHS) development and characteristics, this paper provides a deep insight into latest hardware and trends. The research then moves on to a detailed discussion of some of the important services provided by the SHS and its advantages. The paper also statistically discusses the current and future research trends in the SHS, followed by a detailed portrayal of the difficulties and roadblocks in implementing them. The comprehensive overview of the SHS presented in this paper will help designers, researchers, funding agencies, and policymakers have a bird’s-eye view of the overall concept, attributes, technological aspects, and features of modern SHSs.

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“…Radio-based technology relies on the information and characteristics of signals to detect human activity. Wearable motion sensors that track the movement of body parts might detect particular behaviors like sitting or walking [6].…”

Section: Introductionmentioning

confidence: 99%

Human Fall Detection for Smart Home Caring using Yolo Networks

LUO¹

2023

IJACSA

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In order to help the elderly and limit the incidence of falls that result in injuries, effective fall detection in smart home applications is a challenging topic. Many techniques have been created employing both vision and non-vision-based technologies. Many researchers have been drawn to the visionbased technique amongst them because of its viability and application. However, there is still room for improvement in the effectiveness of fall detection given the poor accuracy rate and high computational cost issues with current vision-based techniques. This study introduces a new dataset for posture and fall detection, whose photo images were gathered from Internet resources and data augmentation. It employs YOLO networks for fall detection purpose. Furthermore, different YOLO networks are implemented on our dataset to address the most accurate and effective model. Based on assessment parameters including accuracy, F1 score, recall, and mAP, the performance of the various YOLOv5n, s and YOLOv6s versions are compared. As experimental results showed, the YOLOv5s performed better than other.

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Future Activities Prediction Framework in Smart Homes Environment

Cited by 11 publications

References 50 publications

Computational methods for predicting human behaviour in smart environments

Computational methods for predicting human behaviour in smart environments

Smart Home System: A Comprehensive Review

Human Fall Detection for Smart Home Caring using Yolo Networks

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