ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs

A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure

A review of smart homes in healthcare

The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes’ residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human–Computer interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area

Sensor-based early activity recognition inside buildings to support energy and comfort management systems

Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of 81%. Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about 60%

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. 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Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Monitoring Quality of Life Indicators at Home from Sparse and Low-Cost Sensor Data.

Supporting older people, many of whom live with chronic conditions or cognitive and physical impairments, to live independently at home is of increasing importance due to ageing demographics. To aid independent living at home, much effort is being directed at reliably detecting activities from sensor data to monitor people’s quality of life or to enhance self-management of their own health. Current efforts typically leverage smart homes which have large numbers of sensors installed to overcome challenges in the accurate detection of activities. In this work, we report on the results of machine learning models based on data collected with a small number of low-cost, off-the-shelf passive sensors that were retrofitted in real homes, some with more than a single occupant. Models were developed from the sensor data collected to recognize activities of daily living, such as eating and dressing as well as meaningful activities, such as reading a book and socializing. We evaluated five algorithms and found that a Recurrent Neural Network was most accurate in recognizing activities. However, many activities remain difficult to detect, in particular meaningful activities, which are characterized by high levels of individual personalization. Our work contributes to applying smart healthcare technology in real-world home settings

Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits

Multi-Residential Activity Labelling in Smart Homes with Wearable Tags Using BLE Technology

Smart home platforms show promising outcomes to provide a better quality of life for residents in their homes. One of the main challenges that exists with these platforms in multi-residential houses is activity labeling. As most of the activity sensors do not provide any information regarding the identity of the person who triggers them, it is difficult to label the sensor events in multi-residential smart homes. To deal with this challenge, individual localization in different areas can be a promising solution. The localization information can be used to automatically label the activity sensor data to individuals. Bluetooth low energy (BLE) is a promising technology for this application due to how easy it is to implement and its low energy footprint. In this approach, individuals wear a tag that broadcasts its unique identity (ID) in certain time intervals, while fixed scanners listen to the broadcasting packet to localize the tag and the individual. However, the localization accuracy of this method depends greatly on different settings of broadcasting signal strength, and the time interval of BLE tags. To achieve the best localization accuracy, this paper studies the impacts of different advertising time intervals and power levels, and proposes an efficient and applicable algorithm to select optimal value settings of BLE sensors. Moreover, it proposes an automatic activity labeling method, through integrating BLE localization information and ambient sensor data. The applicability and effectiveness of the proposed structure is also demonstrated in a real multi-resident smart home scenario