A Tailored Smart Home for Dementia Care

Dementia refers to a group of chronic conditions that cause the permanent and gradual cognitive decline. Therefore, a Person with Dementia (PwD) requires constant care from various classes of caregivers. The care costs of PwDs bear a tremendous burden on healthcare systems around the world. It is commonly accepted that utilising Smart Homes (SH), as an instance of Ambient Assisted Living (AAL) technologies, can facilitate the care, and consequently improve the quality of PwDs well-being. Nevertheless, most of the existing platforms assume dementia care is a straight application of standard SH technology without accommodating the specific requirements of dementia care. A consequence of this approach is the inadequacy and unacceptability of generic SH systems in the context of dementia care. Contrary to most of the existing SH systems proposed for dementia care, this study considers the specific requirements of PwDs and their care circle in all development steps of an SH. In addition, it investigates how utilising novel design and computing approaches can enhance the quality of SHs for dementia care. To do so, the requirements of dementia care stakeholders are collected, analysed and reflected on in an SH system design. Extensions and adaptation of existing frameworks and technologies are proposed to implement a prototype based on the resulting design. Finally, thorough evaluations and validation of the prototype are carried out. The evaluations by a group of stakeholders show the suitability of the proposed methodology and consequently the resulting prototypes for reducing dementia care difficulties as well as its potential for deployment in the real-world environment

Smart Homes Design for People with Dementia

In this paper, we present a user-centred approach for designing and developing smart homes for people with dementia. In contrast to most of the existing literature related to dementia, the present approach aims at tailoring the system to the specific needs of dementia using a scenario-based methodology. Scenarios are based on typical dementia symptoms which are collected from research literatures and validated by dementia caregivers. They portray the common behaviour of people with dementia. Because they explain real-world situations, scenarios are meant to generalise the requirements of smart homes for people with dementia. Hence, a top-down approach is followed to summarise the content of the scenarios into the essential requirements for smart home frameworks dedicated to monitoring people with dementia

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks.

In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis. In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. First of all, the paper presents a methodology for generating synthetic data reflecting on some behavioural difficulties of people with dementia given the difficulty of obtaining real-world data. Secondly, the paper explores Convolutional Neural Networks (CNNs) to model patterns in activity sequences and detect abnormal behaviour related to dementia. Activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. Moreover, the performance of CNNs is compared against the state-of-art methods such as Naïve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM), Conditional Random Fields (CRFs). The results obtained indicate that CNNs are competitive with those state-of-art methods

Information propagation in social networks during crises: A structural framework

In crisis situations like riots, earthquakes, storms, etc. information plays a central role in the process of organizing interventions and decision making. Due to their increasing use during crises, social media (SM) represents a valuable source of information that could help obtain a full picture of people needs and concerns. In this chapter, we highlight the importance of SM networks in crisis management (CM) to show how information is propagated through. The chapter also summarizes the current state of research related to information propagation in SMnetworks during crises. In particular three classes of information propagation research categories are identified: network analysis and community detection, role and topic-oriented information propagation, and infrastructure-oriented information propagation. The chapter describes an analysis framework that deals with structural information propagation for crisismanagement purposes. Structural propagation is about broadcasting specific information obtained from social media networks to targeted sinks/receivers/hubs like emergency agencies, police department, fire department, etc. Specifically, the framework aims to identify the discussion topics, known as sub-events, related to a crisis (event) from SM contents. A brief description of techniques used to detect topics and the way those topics can be used in structural information propagation are presented

Model Selection in Online Learning for Times Series Forecasting.

This paper discusses the problem of selecting model parameters in time series forecasting using aggregation. It proposes a new algorithm that relies on the paradigm of prediction with expert advice, where online and offline autoregressive models are regarded as experts. The desired goal of the proposed aggregation-based algorithm is to perform not worse than the best expert in the hindsight. The theoretical analysis shows that the algorithm has a guarantee that holds for any data sequence. Moreover, the empirical evaluation shows that the algorithm outperforms other popular model selection criteria such as Akaike and Bayesian information criteria on cyclic behaving time series

Enhancement of LivCloud for live cloud migration

Virtualization techniques aim at handling the growing demand for computing, storage and communication resources in cloud computing. However, cloud providers often offer their own proprietary virtualization platforms. As a result, cloud users’ VMs are tightly coupled to providers’ IaaS, hindering live migration of VMs to different providers. A number of live cloud migration approaches have been proposed to solve this coupling issue. Our approach, named LivCloud, is among those approaches. It is designed over two stages, basic design stage and the enhancement stage. The implementation of the basic design has been introduced and evaluated on Amazon EC2 and Packet bare metal cloud. This paper discusses the implementation of the second stage, the enhancement of the basic design on Packet. In particular, it illustrates how LivCloud is implemented in two different scenarios. The first scenario deploys KVM bridge networking, OpenvSwitch and C scripts used to meet the network configuration changes during the VMs relocating. This scenario achieves better downtime of one second compared to the basic design of LivCloud. The second scenario uses OpenVPN, OpenDayLight (ODL) and Cisco OpenFlow Manager (OFM) to successfully live migrate VMs back and forth between LivCloud and Packet. This scenario achieves better downtime between 400 and 600 milliseconds. As part of the discussion, the paper proposes a third potential scenario to successfully meet the live cloud migration requirements. This scenario aims to eliminate any downtime occurred in the first two scenarios by utilizing the Open Overlay Router (OOR), Locator Identifier Separator Protocol (LISP) and ODL

Social media for crisis management: clustering approaches for sub-event detection

Social media is getting increasingly important for crisis management, as it enables the public to provide information in different forms: text, image and video which can be valuable for crisis management. Such information is usually spatial and time-oriented, useful for understanding the emergency needs, performing decision making and supporting learning/training after the emergency. Due to the huge amount of data gathered during a crisis, automatic processing of the data is needed to support crisis management. One way of automating the process is to uncover sub-events (i.e., special hotspots) in the data collected from social media to enable better understanding of the crisis. We propose in the present paper clustering approaches for sub-event detection that operate on Flickr and YouTube data since multimedia data is of particular importance to understand the situation. Different clustering algorithms are assessed using the textual annotations (i.e., title, tags and description) and additional metadata information, like time and location. The empirical study shows in particular that social multimedia combined with clustering in the context of crisis management is worth using for detecting sub-events. It serves to integrate social media into crisis management without cumbersome manual monitoring

Modeling Interaction in Multi-Resident Activities

In this paper we investigate the problem of modeling multi-resident activities. Specifically, we explore different approaches based on Hidden Markov Models (HMMs) to deal with parallel activities and cooperative activities. We propose an HMM-based method, called CL-HMM, where activity labels as well as observation labels of different residents are combined to generate the corresponding sequence of activities as well as the corresponding sequence of observations on which a conventional HMM is applied. We also propose a Linked HMM (LHMM) in which activities of all residents are linked at each time step. We compare these two models to baseline models which are Coupled HMM (CHMM) and Parallel HMM (PHMM). The experimental results show that the proposed models outperform CHMM and PHMM when tested on parallel and cooperative activities

MSAFIS: an evolving fuzzy inference system

In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), and stable gradient descent algorithm (SGD). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SGD is used instead of the Kalman filter for the updating of parameters. The SGD improves the Kalman filter, because it first obtains a better learning in big data. The effectiveness of the introduced method is verified by two experiments