Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living

Kavitha, R.; Binu, Sumitra

doi:10.1016/j.procs.2020.01.044

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…This sensor stream is divided into segments using different segmentation techniques like activity-based segmentation, sensor-based segmentation, time-based segmentation and area-based segmentation. The first two techniques are used for offline activity recognition and thelast twoare used for online activity recognition [19]. In this work activity-based segmentation which divides the sensor streams based on activity type for an offline activity recognition is used in the pre-processing phase.…”

Section: Pre-processingmentioning

confidence: 99%

Smart Home Activity Recognition for Ambient Assisted Living (AAL)

Rajamohan¹,

Nazz²

2022

ECS Trans.

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With the increasing age of an individual, the chances of being prone to chronic diseases like diabetes or non-curable diseases like Alzheimer’s Syndrome or Parkinson's Syndrome increases. Due to the health issues, elderly must be accompanied by caretakers to monitor their well-being at all times. With growing responsibilities and work pressure, the family members may find it challenging to find a trustworthy caretaker. In such scenarios, an assisted living environment acts as a boon. A normal home embedded with different sensors to monitor an individual’s well-being is called as Ambient Assisted Living(AAL). This living environment detects anomalous behaviour and recognizes human activities. In this research paper, a smart home activity recognition model is proposed and implemented using four machine learning algorithms using six different publicly available datasets. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the dataset.

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Section: Pre-processingmentioning

confidence: 99%

Smart Home Activity Recognition for Ambient Assisted Living (AAL)

Rajamohan¹,

Nazz²

2022

ECS Trans.

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“…By recognizing human activities, many security and safety challenges from real-world applications might be overcome or mitigated, such as those produced by health-care monitoring. A region-based segmentation technology was proposed in [127], which can effectively slice the sensor data stream to accurately recognize human activities. Biological signals can also be used in data steganography to prevent data theft in smart medical systems [116].…”

Section: ) Biological Factorsmentioning

confidence: 99%

A Comprehensive Survey of Security Issues of Smart Home System: “Spear” and “Shields,” Theory and Practice

Yang

Sun

2022

IEEE Access

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The rapid development and wide application of the Internet of Things (IoT) and sensor technologies have produced good opportunities for the development of IoT-based smart home systems (SHSs). However, during the rapid market expansion of SHSs, security challenges associated with SHSs have become a primary concern of people because they are so closely related to people's daily lives. These security problems may damage information assets and pose a serious threat to people's health and life. This study investigates security issues in SHSs and provides a comprehensive overview of research to date. In this review, after analyzing the existing definition and concept of SHSs proposed by authoritative encyclopedias and academic literature, we propose a more accurate, elaborated definition of SHSs, analyze their architecture, extract six natural and contextual features, and summarize spears (cyber-attack means) vs. shields (countermeasures) in detail in the context of SHSs. Then, the security frameworks and evaluation technologies in SHSs are discussed. Different scenarios for technology integration and the practical research results in SHSs, such as blockchain, cloud computing, Internet of Vehicles, and AI are presented meticulously. After that, two special issues related to security are discussed. We believe that future research on SHS security should focus on four aspects: the unification of architecture, resource limitation, fragmentation, and code and firmware security. In addition, research on SHS security should be given priority over its commercialization process.

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Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Colpas

Vicario

De-la-Hoz-Franco

et al. 2020

Sensors

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Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.

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Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living

Cited by 3 publications

References 5 publications

Smart Home Activity Recognition for Ambient Assisted Living (AAL)

Smart Home Activity Recognition for Ambient Assisted Living (AAL)

A Comprehensive Survey of Security Issues of Smart Home System: “Spear” and “Shields,” Theory and Practice

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

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