A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

Forkan, Abdur Rahim Mohammad; Khalil, Ibrahim; Tari, Zahir; Foufou, Sebti; Bouras, Abdelaziz

doi:10.1016/j.patcog.2014.07.007

Cited by 181 publications

(123 citation statements)

References 41 publications

Supporting

Mentioning

123

Contrasting

Order By: Relevance

“…Khali et al [13] proposed a scheme which describes the pattern recognition models to detect behavior and healthrelated changes in a patient in AAL environment. In this scheme, hidden markov model approach is used for detecting an abnormal behavior.…”

Section: Related Workmentioning

confidence: 99%

A Review on Applications of Ambient Assisted Living

Desai¹,

Jhaveri²

2017

IJCA

View full text Add to dashboard Cite

The use of smart environments in the delivery of a pervasive care is the research topic that has witnessed increasing interest in recent years. These environments aim to deliver pervasive care through ubiquitous sensing by monitoring the occupant's activities of daily living. In order for these environments to succeed in achieving their goal, it is crucial that sensors deployed in the environments perform faultlessly. Ambient Assisted Living (AAL) is currently one of the important research and development areas, where accessibility, usability, and learning play a major role and where future interfaces are an important concern for applied engineering. In this paper, we discuss the research work carried out for various applications of AAL. We summarize techniques and tools for AAL and also their benefits and limitations. Using a summarized data we also look for different future challenges. The aim of this paper is to survey different AAL technologies to assist the elderly people to live in a smart environment.

show abstract

Section: Related Workmentioning

confidence: 99%

A Review on Applications of Ambient Assisted Living

Desai¹,

Jhaveri²

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…Other related research efforts in humancentered systems typically address challenges on how to improve their performance and usability. For example, Octopus [3], BodyCloud [4], CocaMAAL [5], BDCaM [6] and [7,8,9,10,11,12,14,15] describe various features recognition and detection techniques that may allow users to gather and process data from sensing devices with high accuracy. However, they do not address the cost and limited resources challenges (such CPU, storage, battery, network cost) in order to achieve that high performance.…”

Section: Related Workmentioning

confidence: 99%

Low-cost mobile personal clouds

Radenkovic

Huynh

2016

2016 International Wireless Communications and Mobile Computing Conference (IWCMC)

View full text Add to dashboard Cite

Abstract:We propose a mobile peer to peer personal cloud architecture which allows users to capture, store, analyse, interact with and share different types of personal and context data with no privacy leakage. Our mobile personal cloud can host multiple different services which are intelligent, distributed, dynamic and operate in real time. In this paper we describe one service that we designed and deployed on our mobile personal cloud called Mobile Wellbeing Companion Cloud (MWCC). Using low-cost, off-the-shelf hardware components and opensource software, our MWCC combines several sensor network technologies to allow users to monitor and interact with their personal data and environment in real time without privacy leakage. MWCC augments heterogeneous sensors data with state of the art machine learning algorithms for signal filtering, fast classification and analysis and provides interactive data visualisation for transparent user interaction. We show that our MWCC is easy to use and highly accurate while managing to keep resource costs low.

show abstract

“…Finally, in Forkan, Khalil, Tari, Foufou, and Bouras (2015) pat tern recognition models for detecting behavioral and health-related changes in a patient who is monitored continuously in an assisted liv ing environment is described. The early anticipation of anomalies can improve the rate of disease prevention.…”

Section: Context-aware Approachesmentioning

confidence: 99%

“…Although this work is similar to this paper in the sense that it implements Hidden Markov Models and fuzzy logic, the main difference is that in this work these components are used to de tect drifts as an external layer. In contrast, the work of Forkan et al (2015) use them as a base learner, detecting abnormal behaviors for a specific context as it is the case of health problems and therefore it is not suitable to deal with concept recurrence.…”

Section: Context-aware Approachesmentioning

confidence: 99%

Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function

Ángel

Bártolo²,

Ernestina

2016

Expert Systems with Applications

View full text Add to dashboard Cite

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behavior to the volatile underlying data distributions, what has been called concept drift. It is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classifica-tion models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The intro-duction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of the MM-PRec system, that integrates a meta-model mechanism and a fuzzy similarity function. The theoretical proposal of MM-PRec is also validated in this paper by means of different experiments using both synthetic and real datasets.

show abstract

A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

Cited by 181 publications

References 41 publications

A Review on Applications of Ambient Assisted Living

A Review on Applications of Ambient Assisted Living

Low-cost mobile personal clouds

Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function

Contact Info

Product

Resources

About