scholarly journals Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1457 ◽  
Author(s):  
Jinghuan Guo ◽  
Yiming Li ◽  
Mengnan Hou ◽  
Shuo Han ◽  
Jianxun Ren
Keyword(s):  
Activity Recognition ◽  
Adaptive Systems ◽  
Smart Home ◽  
Population Aging ◽  
Smart Homes ◽  
Daily Activities ◽  
Matching Method ◽  
Home Based ◽  
Great Progress ◽  
F Measure

With the development of population aging, the recognition of elderly activity in smart homes has received increasing attention. In recent years, single-resident activity recognition based on smart homes has made great progress. However, few researchers have focused on multi-resident activity recognition. In this paper, we propose a method to recognize two-resident activities based on time clustering. First, to use a de-noising method to extract the feature of the dataset. Second, to cluster the dataset based on the begin time and end time. Finally, to complete activity recognition using a similarity matching method. To test the performance of the method, we used two two-resident datasets provided by Center for Advanced Studies in Adaptive Systems (CASAS). We evaluated our method by comparing it with some common classifiers. The results show that our method has certain improvements in the accuracy, recall, precision, and F-Measure. At the end of the paper, we explain the parameter selection and summarize our method.

scholarly journals Development of a Role Model for the Use of Smart Home Based Data in Medical Care

2021 ◽  
Author(s):  
Isabel Richter ◽  
Corinna Mielke ◽  
Reinhold Haux
Keyword(s):  
Literature Review ◽  
Health System ◽  
Medical Care ◽  
Patient Care ◽  
Smart Home ◽  
Role Model ◽  
Smart Homes ◽  
Care Process ◽  
Home Based ◽  
Home Systems

Smart home systems create new opportunities for patient care. In this paper, a role model is created for the different groups of people involved in the care process of an occupant. Based on a systematic literature review seven roles were identified. A second literature review deals with the topic Feedback of Smart Home Systems. Combining both reviews visualization proposals were created and are presented for two of the roles. The role model is adapted to German health system but could be transformed for different countries. To confirm the results an evaluation of role model and visualization proposal should be done in collaboration with possible users of smart homes.

Rule Making Algorithms for Smart Home Control

2014 ◽  
Vol 693 ◽  
pp. 451-456
Author(s):  
Andrej Strasiftak ◽  
Dušan Mudrončík ◽  
Andrea Peterková
Keyword(s):  
Activity Recognition ◽  
Smart Home ◽  
Smart Homes ◽  
Basic Knowledge ◽  
Automation System ◽  
Home Automation ◽  
Learning Mechanism ◽  
Knowledge Database ◽  
Home Automation System ◽  
Rule Making

In this article we want to introduce our first tested concept of a learning mechanism for smart homes. It is a number of interrelated algorithms that search the database of events in the home automation system. By searching, mechanism selects those events that at the time repeated several times under well-defined rules. Finally creates a database of basic rules, some basic knowledge database of activities. These can then serve as the management rules or may be further examined for activity recognition.

scholarly journals An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances

2014 ◽  
Vol 12 ◽  
pp. 58-78 ◽  
Author(s):  
Corinne Belley ◽  
Sebastien Gaboury ◽  
Bruno Bouchard ◽  
Abdenour Bouzouane
Keyword(s):  
Activity Recognition ◽  
Smart Home ◽  
Inexpensive Method ◽  
Home Based

scholarly journals A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4474 ◽  
Author(s):  
Du ◽  
Lim ◽  
Tan
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Power Saving ◽  
Smart Homes ◽  
The Elderly ◽  
Human Mind ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Home Based ◽  
Passive Rfid

Smart Homes are generally considered the final solution for living problem, especially for the health care of the elderly and disabled, power saving, etc. Human activity recognition in smart homes is the key to achieving home automation, which enables the smart services to automatically run according to the human mind. Recent research has made a lot of progress in this field; however, most of them can only recognize default activities, which is probably not needed by smart homes services. In addition, low scalability makes such research infeasible to be used outside the laboratory. In this study, we unwrap this issue and propose a novel framework to not only recognize human activity but also predict it. The framework contains three stages: recognition after the activity, recognition in progress, and activity prediction in advance. Furthermore, using passive RFID tags, the hardware cost of our framework is sufficiently low to popularize the framework. In addition, the experimental result demonstrates that our framework can realize good performance in both activity recognition and prediction with high scalability.

Comprehensive Ontological Model for Senior Wellness Activity Recognition in Smart Homes

2020 ◽  
pp. 148-167
Author(s):  
Hajar Khallouki ◽  
Rachid Benlamri ◽  
Abdulsalalm Yassine
Keyword(s):  
Activity Recognition ◽  
Smart Home ◽  
Environmental Parameters ◽  
Smart Homes ◽  
Well Being ◽  
The Elderly ◽  
Patient Profile ◽  
Ontological Model ◽  
Watching Tv ◽  
Lying Down

There are several works in the field of smart homes for healthcare, with different types of sensors used to monitor medical, behavioral and environmental parameters for patients. In the context of smart home for the elderly, the use of sensors needs to be adapted to respect the privacy of elders and to work passively without the need for caregiver assistance. Most research in this area focused on activity recognition (e.g. eating, sleeping, watching TV, etc.) which may be defined as the identification of a sequence of actions (e.g. using microwave, lying down, etc.). In this chapter, we propose a comprehensive ontological model for well-being activity recognition in smart home. Our approach takes into account different aspects of the well-being context such as patient profile, object being used to perform the activity, the time of running the activity, its location, etc. In order to validate the proposed ontology and reason on it, we perform a set of queries and inference rules.

scholarly journals Predicting Activities of Daily Living with Spatio-Temporal Information

2020 ◽  
Vol 12 (12) ◽  
pp. 214
Author(s):  
Sook-Ling Chua ◽  
Lee Kien Foo ◽  
Hans W. Guesgen
Keyword(s):  
Activities Of Daily Living ◽  
Activity Recognition ◽  
Daily Living ◽  
Independent Living ◽  
Smart Home ◽  
Contextual Information ◽  
Smart Homes ◽  
The Elderly ◽  
Temporal Information ◽  
User Activities

The smart home has begun playing an important role in supporting independent living by monitoring the activities of daily living, typically for the elderly who live alone. Activity recognition in smart homes has been studied by many researchers with much effort spent on modeling user activities to predict behaviors. Most people, when performing their daily activities, interact with multiple objects both in space and through time. The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This paper shows the importance of spatial and temporal information for reasoning in smart homes and demonstrates how such information is represented for activity recognition. Evaluation was conducted on three publicly available smart-home datasets. Our method achieved an average recognition accuracy of more than 81% when predicting user activities given the spatial and temporal information.

scholarly journals Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1479 ◽  
Author(s):  
Wing W.Y. Ng ◽  
Shichao Xu ◽  
Ting Wang ◽  
Shuai Zhang ◽  
Chris Nugent
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Activity Recognition ◽  
Basis Function ◽  
Smart Home ◽  
Sensor Data ◽  
Support Vector ◽  
Home Based ◽  
Radial Basis ◽  
The Impact

Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people’s lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people’s activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively.

scholarly journals A two-layer framework for activity recognition with multi-factor activity pheromone matrix

2018 ◽  
Vol 189 ◽  
pp. 10001 ◽  
Author(s):  
Liwen Xu ◽  
Guoli Wang ◽  
Xuemei Guo
Keyword(s):  
Activity Recognition ◽  
Population Aging ◽  
Daily Activities ◽  
Sensor Data ◽  
Time Shift ◽  
Layer Model ◽  
Small Areas ◽  
Double Layer Model ◽  
Statistic Model ◽  
Environment Perception

With rapid population aging and increasingly indoor sensing technologies, mining effective information in sensor data is in need that we can analyse individual behaviour semantics, or even predict intentions. The model for indoor activity recognition (AR) is usually based on statistic while sensor data can impliedly reflect abundant information in order. Behaviour will trigger environment perception sensors. Inspired by information transmission in nature, persistent action keeps activity pheromone accumulating and inactive action keeps it volatilizing along with time shift. Different from statistic model, our framework proposes a method to construct multi factor features named activity pheromone matrix (APM). It has a double-layer model for recognizing daily activities include the high-overlapping. The experimental results show that our method can effectively promote the accuracy of activities recognition compared with the existing statistical models, even the high-overlapping activities in small areas.

scholarly journals An Accessible Smart Home Based on Integrated Multimodal Interaction

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5464
Author(s):  
Ana Patrícia Rocha ◽  
Maksym Ketsmur ◽  
Nuno Almeida ◽  
António Teixeira
Keyword(s):  
Smart Home ◽  
Multimodal Interaction ◽  
Environmentally Friendly ◽  
Smart Homes ◽  
Information And Communications Technology ◽  
Communications Technology ◽  
Home Based ◽  
Project Smart ◽  
Multiple Devices ◽  
Adaptive Interaction

Our homes are becoming increasingly sensorized and smarter. However, they are also becoming increasingly complex, making accessing them and their advantages difficult. Assistants have the potential for improving the accessibility of smart homes, by providing everyone with an integrated, natural, and multimodal way of interacting with the home’s ecosystem. To demonstrate this potential and contribute to more environmentally friendly homes, in the scope of the project Smart Green Homes, a home assistant highly integrated with an ICT (Information and communications technology) home infrastructure was developed, deployed in a demonstrator, and evaluated by seventy users. The users’ global impression of our home assistant is in general positive, with 61% of the participants rating it as good or excellent overall and 51% being likely or very likely to recommend it to others. Moreover, most think that the assistant enhances interaction with the smart home’s multiple devices and is easy to use by everyone. These results show that a home assistant providing an integrated view of a smart home, through natural, multimodal, and adaptive interaction, is a suitable solution for enhancing the accessibility of smart homes and thus contributing to a better living ambient for all of their inhabitants.

scholarly journals Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1593
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun ◽  
Ho-Young Kwak
Keyword(s):  
Reinforcement Learning ◽  
Smart Home ◽  
Smart Homes ◽  
Smart Devices ◽  
Home Gateway ◽  
Home Based ◽  
Information And Communication ◽  
Smart Home Gateway ◽  
Iot Devices ◽  
The Right

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

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