SensCare: Semi-automatic Activity Summarization System for Elderly Care

Wu, Pang; Peng, Hao‐Kai; Zhu, Jiang; Zhang, Ying

doi:10.1007/978-3-642-32320-1_1

Cited by 35 publications

(29 citation statements)

References 14 publications

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“…A blackboard architecture initially proposed by Winograd for tracking employee locations [13] was adapted by Wu et al for use in a system called SensCare [14]. SensCare is a system for monitoring the activities of elderly people, using a heterogeneous collection of sensors, for the purpose of producing a semi-automatic lifelog.…”

Section: Architectures For Cps In Healthcarementioning

confidence: 99%

“…This architecture was chosen for its history of use in closely related systems such as environmental monitoring and vehicle control systems. In addition the layered architecture, publish-and-subscribe, and blackboard were all chosen due to their use in previously proposed or prototype implementations of healthcare related CPS [7,[9][10][11][12][13][14]. The essential architectural qualities and a description of each in terms of CPS are detailed below:…”

Section: Architectural Quality Analysismentioning

confidence: 99%

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A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

Seifert

Reza

2016

Computers

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This paper surveys the available system architectures for cyber-physical systems. Several candidate architectures are examined using a series of essential qualities for cyber-physical systems for healthcare. Next, diagrams detailing the expected functionality of infusion pumps in two of the architectures are analyzed. The STRIDE Threat Model is then used to decompose each to determine possible security issues and how they can be addressed. Finally, a comparison of the major security issues in each architecture is presented to help determine which is most adaptable to meet the security needs of cyber-physical systems in healthcare.

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Section: Architectures For Cps In Healthcarementioning

confidence: 99%

Section: Architectural Quality Analysismentioning

confidence: 99%

A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

Seifert

Reza

2016

Computers

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“…Year Ref Classification Algorithms Evaluation Method Processing Platform 2014 [1] Multidimensional sensing heuristic correlate to accelerometer measurements Phone 2014 [2] MPEG compact descriptors for visual search Phone 2011 [19] Hidden Markov Models 4-fold cross validation and precision/recall measures Phone 2011 [17] Multiclass Logistic Regression 4-fold cross validation and precision/recall measures Server 2011 [20] Transfer learning Embedded Decision Tree 10 times 10-fold cross validation Server 2011 [28] Hidden Markov Chain Server 2011 [18] Decision Tree, Naïve Bayes, Random Forest, Logistics Regression, RBF Network, Support Vector Machine 10-fold cross validation Server 2011 [30] Smoothed Single-layer Hidden Markov Models F-measure Server 2011 [14] Decision Tree Phone 2011 [16] Hidden Markov Model false non match rate (FNMR), false match rate (FMR)…”

Section: Comparison Of Classification Algorithms In Activity Recognitionmentioning

confidence: 99%

Analysis of Image Classification and Annotation Methods for Smartphone

2015

IJSR

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Rapid development in computing technology brings computers and humans to be seamlessly integrated in future. The innovation of smart phone has driven computing era towards ubiquitous and pervasive computing. Recognizing human activity has garnered a lot of interest and has raised significant researches-concerns in identifying contextual information useful to human activity recognition. The central elements of pervasive computing is, the ability to recognize and understand its users dynamically for which classification, object separation and annotation required. This paper discusses about classification algorithms used in smart phonebased human activity. Existing technologies pertaining to smart phone-based researches in human activity recognition will be highlighted and discussed.

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“…Applications that can benefit from AR include smart home, smart healthcare, senior care, personal fitness, etc. [1,2]. Nowadays, many approaches with different features were proposed to recognize a wide range of activities.…”

Section: Introductionmentioning

confidence: 99%

Using Convolutional Layer Features for Indoor Human Activity Recognition based on Spatial Location Information

Li¹,

Zhao²,

Li³

et al. 2018

dtcse

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Identifying human actions has great importance for various applications, especially in the smart home, fitness tracking and health monitoring domains. However, human activity recognition still remains a challenging task. This is mainly due to the broad range of human activities as well as the rich variation of a given activity can be performed. In this paper, we dealt with the problem by making use of spatial location information of three different parts of a human body, which are derived via three UWB (ultrawide band) tags and an Ubisense positioning system. In order to improve the accuracy, we proposed a recognition method: convolutional layer features plus SVM (Support Vector Machine). We pre-process the raw spatial location data and transfer them into motion feature, frequency feature and statistic feature. These features are input into the CNN (Convolutional Neural Network) to generate the convolutional layer features, and then we use SVM to classify these features. By comparing the experimental results, the best recognition rate of different experimenters is 89.75%, which shows its feasibility.

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SensCare: Semi-automatic Activity Summarization System for Elderly Care

Cited by 35 publications

References 14 publications

A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

Analysis of Image Classification and Annotation Methods for Smartphone

Using Convolutional Layer Features for Indoor Human Activity Recognition based on Spatial Location Information

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