Prediction of mobility entropy in an ambient intelligent environment

Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understanding human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term. In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

AN INVESTIGATION OF ENTREPRENEURIAL MOTIVATION: BOUTIQUE HOTELS IN NORTHERN THAILAND

Purpose – entrepreneurship scholars have argued extensively that the phenomenon of entrepreneurship and entrepreneurial motivation cannot be studied in isolation from their broader socioeconomic environment. This study addresses this gap by examining the entrepreneurial motivation of hotel entrepreneurs in Northern Thailand. The study also investigates how various mediating factors and motivations to start a business shape tourism entrepreneurs\u27 behaviour in relation to growth strategies. Design/ Methodology/ Approach – qualitative research was conducted in Northern Thailand 2012 and the follow-up study in 2019. Purposive and snowball sampling strategies were used. The primary data collection method was semi-structured interviews. Findings – the study identifies the coexistence of both lifestyle and growth-oriented entrepreneurs. The results show that the entrepreneurial decision to enter the hotel industry was not solely determined by the entrepreneur\u27s own actions, but significantly by the family. The role of family in business creation is directive and not facilitative. Business growth was a desirable strategy for both lifestyle and growth-oriented entrepreneurs. Originality of the research – study shows that entrepreneurial motivation cannot be properly understood if it is studied in isolation from the wider socio-economic context. Moreover, it challenges the prevailing classification of tourism entrepreneurs into lifestyle-oriented and growth-oriented

Multi-sensor activity recognition of an elderly person.

The rapid increase in the number of ageing population brings major issues to health care including a rise in care cost, high demand in long- term care, burden to caregivers, and insufficient and ineffective care. Activity recognition can be used as the key part of the intelligent sys- tems to allow elderly people to live independently at homes, reduce care cost and burden to the caregivers, provide assurance for the fam- ilies, and promote better care. However, current activity recognition systems mainly focus on the technical aspect i.e. systems accuracy and neglects the practical aspects such as acceptance, usability, cost and privacy. The practicality of the system is the vital indication whether the system will be adopted. This research aims to develop the activity recognition system which considers both practical and technical aspects using multiple wrist-worn sensors. An extensive literature review in wearable sensor based activity recog- nition and its applications in healthcare have been carried out. Novel multi-sensor activity recognition utilising multiple low-cost, non-intrusive, non-visual wearable sensors is proposed. The sensor fusion is per- formed at feature and classi er levels using the proposed feature se- lection and classi er combination techniques. The multi-sensor ac- tivity recognition data sets have been collected. The rst data set contains data from accelerometer collected from seven young adults. The second data set contains data from accelerometer, altimeter, and temperature sensor collected from 12 elderly people in home environ- ment performing 10 activities. The third data set contains sensor data from accelerometer, gyroscope, temperature sensor, altimeter, barometer, and light sensor worn on the users wrist and a heart rate monitor worn over the users chest. The data set is collected from 12 elderly persons in a real home environment performing 13 activities. This research proposes two feature selection methods, Feature Com- bination (FC) and Maximal Relevancy and Maximal Complementary (MRMC), based on the relationship between feature and classes as well as the relationship between a group of features and classes. The experimental studies show that the proposed techniques can select an optimum set of features from irrelevant, overlapped, and partly over- lapped features. The studies also show that FC and MRMC obtain higher classi cation performances than popular techniques including MRMR, NMIFS, and Clamping. Two classi er combination tech- niques based on Genetic Algorithm (GA) are proposed. The rst technique called GA based Fusion Weight (GAFW), uses GA nd the optimum fusion weights. The results indicate that 99% of classi er fusion using GAFW achieves equal or higher accuracy than using only the best classi er. While other fusion weight techniques cannot guar- antee accuracy improvement, GAFW is a more suitable method for determining fusion weight regardless which fusion techniques are used. Another algorithm called GA based Combination Model (GACM) is proposed to nd the optimal combination between classi er, weight function, and classi er combiners. The algorithm does not only nd the model which has the minimum classi cation error but also select the one that is simpler. Other criteria e.g. select the classi er with low computation can also be easily added to the algorithm. The re- sults show that in general GACM can nd the optimum combinations automatically. The comparison against manually selection revealed that there is no statistical signi cant in the performances. Applications of the proposed work in home care and decision support system are discussed The results of this research will have a signi cant impact on the future health care where people can be health monitored from their homes to promote healthy living, detect any changes in behaviour, and improve quality of care

Training Evaluation in a Smart Farm using Kirkpatrick Model: A Case Study of Chiang Mai

Farmers can now use IoT to improve farm efficiency and productivity by using sensors for farm monitoring to enhance decision-making in areas such as fertilization, irrigation, climate forecast, and harvesting information. Local farmers in Chiang Mai, Thailand, on the other hand, continue to lack knowledge and experience with smart farm technology. As a result, the 'SUNSpACe' project, funded by the European Union's Erasmus+ Program, was launched to launch a training course which improve the knowledge and performance of Thai farmers. To assess the effectiveness of the training, The Kirkpatrick model was used in this study. Eight local farmers took part in the training, which was divided into two sections: mobile learning and smart farm laboratory. During the training activities, different levels of the Kirkpatrick model were conducted and tested: reaction (satisfaction test), learning (knowledge test), and behavior (performance test). The overall result demonstrated the participants' positive reaction to the outcome. The paper also discusses the limitations and suggestions for training activities

An Ambient Assisted Living Technology Platform for Informal Carers of the Elderly - iCarer

For most families with elderly relatives, care within their own home is by far the most preferred option -both for the elderly and their carers. However, frequently these carers are the partners of the person with long-term care needs, and themselves are elderly and in need of support to cope with the burdens and stress associated with these duties. When it becomes too much for them, they may have to rely on professional care services, or even use residential care for a respite. In order to support the carers as well as the elderly person, an ambient assisted living platform has been developed. The system records information about the activities of daily living using unobtrusive sensors within the home, and allows the carers to record their own wellbeing state. By providing facilities to schedule and monitor the activities of daily care, and providing orientation and advice to improve the care given and their own wellbeing, the system helps to reduce the burden on the informal carers

A practical multi-sensor activity recognition system for home-based care

To cope with the increasing number of aging population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care

An AAL collaborative system: the AAL4ALL and a mobile assistant case study

"15th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2014, Amsterdam, The Netherlands, October 6-8, 2014"The areas of Ambient Assisted Living (AAL) and Intelligent Systems (IS) are in full development, but there are still some issues to be resolved. One issue is the myriad of user oriented solutions that are rarely built to interact or integrate with other systems available in the market. In this paper we present the AAL4ALL project and the UserAccess implementation, showing a novel approach towards virtual organizations, interoperability and certification. The aim of this project is to provide a collaborative network of services and devices that connect every user and product from other developers, building a heterogeneous ecosystem. Thus establishing an environment for collaborative care systems, which may be available to the users in from of safety services, comfort services and healthcare services.Project "AAL4ALL", co-financed by the European Community Fund FEDER, through COMPETE - Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013 and the project PEst-OE/EEI/UI0752/2014. Project CAMCoF - Context-aware Multimodal Communication Framework fund-ed by ERDF -European Regional Development Fund through the COMPETE Pro-gramme (operational programme for competitiveness) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis

Background LAM is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.Patients and methods Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the NHLBI LAM registry. Prospective outcomes were associated with cluster results.Results Two and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and TSC (p=0.041). The third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model future risk of pneumothorax was 3.3 fold (95% C.I. 1.7–5.6) greater in cluster one than two (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters two and three than cluster one (