Fuzzy optimization to improve mobile wellness applications for young-elderly

Mobile applications and specifically wellness applications are used increasingly by different age-segments of the general population. This is facilitated by the large amount of data collected through various built-in sensors in the smartphone or other mobile devises, e.g. smart watches. Young-elderly cohort (60-75 year old individual) is probably one of the most potential user groups that would benefit from using mobile health and wellness applications, if their needs and preferences are precisely addressed. General knowledge is limited on understanding to what extent mobile wellness applications can and should provide precise recommendations which improve the users’ health and physical conditions. To address this problem, the current study identifies the potential benefits of utilizing fuzzy optimization tools to design recommendation systems that can take into consideration the (i) imprecision in the data and (ii) the imprecision by which one can estimate the effect of a recommendation on the user of the system. The proposed approach, depending on the context of use, identifies a set of actions to be taken by the users in order to optimize the physical or mental condition from various perspectives. The model is illustrated through the example of walking speed optimization which is an important issue for the young-elderly

Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach

Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation

Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing

System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability

[EN] The conditions of the environment where a person lives have a great impact on his wellness state. When buying a new house, it is important to select a place that aids in improving the wellness state of the buyer or, at least, keeps it at the same level. A deficient wellness state implies an increase of stress and the appearance of some effects associated with it. Heart rate variability (HRV) allows measuring the stress or wellness levels of a person by measuring the difference in time between heartbeats. A low HRV is related to high stress levels whereas a high HRV is associated with a high wellness state. In this paper, we present a system that measures the wellness and stress levels of home buyers by employing sensors that measure the HRV. Our system is able to process the data and recommend the best neighborhood to live in considering the wellness state of the buyer. Several tests were performed utilizing different locations. In order to determine the best neighborhood, we have developed an algorithm that assigns different values to the area in accordance with the HRV measures. Results show that the system is effective in providing the recommendation of the place that would allow the person to live with the highest wellness state.Lacuesta Gilabert, R.; García-García, L.; García-Magariño, I.; Lloret, J. (2017). System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability. IEEE Access. 5:10594-10604. doi:10.1109/ACCESS.2017.2702107S1059410604

Progressive sequence matching for ADL plan recommendation

Activities of Daily Living (ADLs) are indicatives of a person's lifestyle. In particular, daily ADL routines closely relate to a person's well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or RADLART, which proactively recommends healthier choices of activities based on the learnt associations among the user profiles, ADL sequence, and wellness scores. For empirical evaluation, extensive simulations have been conducted to assess the improvement in wellness scores for synthetic users with different acceptance rates of the provided recommendations. Experiments on real users further show that recommendations given by RADLART are generally more acceptable by the users because it takes into considerations of both the user profiles and the performed activities.NRF (Natl Research Foundation, S’pore)Accepted versio

Context-aware Knowledge-based Systems: A Literature Review

Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services’ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented