NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Smart Phone Based Data Mining for Human Activity Recognition

AbstractAutomatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present novel data analytic scheme for intelligent Human Activity Recognition (AR) using smartphone inertial sensors based on information theory based feature ranking algorithm and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments with a publicly available database1 of human activity with smart phone inertial sensors show that the proposed approach can indeed lead to development of intelligent and automatic real time human activity monitoring for eHealth application scenarios for elderly, disabled and people with special needs

Sleeping activity recognition for an intelligent tele-monitoring system

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Petia I. RadevaPeople that need assistance, as for instance elderly or disabled people, may be affected by a decline in daily functioning that usually involves the reduction and discontinuity in daily routines, as well as, a worsening in the overall quality of life. Thus, there is the need to intelligent systems able to monitor indoor activities of users to detect emergencies, recognise activities, send notifications, and provide a summary of all the relevant information. In this TFG, a machine learning system is presented, it is aimed at improving the ruled-based system accuracy in detecting whether the user is performing their sleeping activity or not. It has been integrated in a sensor-based tele-monitoring and home support system. The data used to build and evaluate the system was obtained from a real-world environment with real end-users, thus ensuring the data reflect the complexities of the real-world

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

Competitive Live Evaluation of Activity-recognition Systems

In order to ensure the validity and usability of activity recognition approaches, an agreement on a set of standard evaluation methods is needed. Due to the diversity of the sensors and other hardware employed, designing and accepting standard tests is a difficult task. This article presents an initiative to evaluate activity recognition systems: a living-lab evaluation established through an annual competition − EvAAL-AR (Evaluating Ambient Assisted Living Systems through Competitive Benchmarking − Activity Recognition). In the competition, each team brings their own activity-recognition system, which is evaluated live on the same activity scenario performed by an actor. The evaluation criteria attempt to capture the practical usability: recognition accuracy, user acceptance, recognition delay, installation complexity, and interoperability with ambient assisted living systems. The article also presents the competing systems with emphasis on two best-performing ones: (i) a system that achieved the best recognition accuracy, and (ii) a system that was evaluated as the best overall. Finally, the article presents lessons learned from the competition and ideas for future development of the competition and of the activity recognition field in general

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs