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

A STEP TOWARD AN INTELLIGENT AND INTEGRATED COMPUTER-AIDED DESIGN OF APPAREL PRODUCTS

An apparel product (or “apparel”) is a human product. The design of an apparel product (or “apparel design”) should share many features of general product design and be conducted with a high degree of systematics and rationality. However, the current practice of apparel design is relatively more experience-based and ad-hoc than it should be. Besides, computer support to apparel design is quite limited in that there are several software systems available for supporting apparel design but they are isolated. Two reasons may explain this above situation: (1) absence of the ontology of apparel and apparel design, and (2) absence of a systematic and rational apparel design process. Furthermore, apparel is a specialized type of product in that all three inherent requirements (i.e., function, comfort related to ergonomics, and pleasure related to aesthetics) are equally important, especially the latter, which creates positive affects in the human wearer. In general, knowledge of how to design an apparel product for pleasure/affects is missing from the current design. The general motivation for the research conducted in this thesis is to locate and articulate this “missing knowledge” in order to advance design technology including computer-aided design for modern apparel products. The specific objectives of the research presented in this thesis are: (1) development of a model for the ontology of apparel or apparel system so that all basic concepts and their relationships related to the apparel system are captured; (2) development of a systematic design process for apparel that captures all the inherent characteristics of design, namely iteration and open-endedness; and (3) development of a computer-aided system for affective design for apparel, whereby human feeling once described can be computed with the result that an apparel product meets the wearer’s “feeling needs” (functional and ergonomic needs are assumed to be satisfied or not the concern of this thesis). There are several challenges to achieving the foregoing objectives. The first of these is the understanding of ontology for apparel and apparel design, given that there are so many types of apparel and ad-hoc apparel design processes in practice. The second challenge is the generalization and aggregation of the various ad-hoc apparel design processes that exist in practice. Third is the challenge presented by imprecise information and knowledge in the aspect of human’s affect. All three above challenges have been tackled and answered in this thesis. The first challenge is tackled with the tool of data modeling especially semantic-oriented data modeling. The second challenge is tackled with the general design theory such as general design phase theory, axiomatic design theory, and FCBPSS knowledge architecture (F: function, C: context, B: behavior, P: principle, SS: state and structure). The third challenge is tacked with the data mining technique and subjective rating technique. Several contributions are made with this thesis. First is the development of a comprehensive ontology model for apparel and apparel design that provides a basis for computer-aided design and manufacturing of apparel in the future. Second is the development of a general apparel design process model that offers a reference model for any specific apparel design process. Third is the provision of new “data mining” technology for acquiring words in human language that express affects. It should be noted that this technology is domain-independent, and thus it is applicable to any other type of product for affective design. The final contribution is the development of a method for searching apparel design parameters which describe an apparel product meeting a wearer’s required feelings described by “feeling words”. The database of words and the algorithm can be readily incorporated into commercial software for computer aided design of apparel products with the new enabler (i.e., design for affect or feeling)

Smart Fitness System: Training Programming

Sistemas de recomendação no geral estão a ser cada vez mais usados por empresas que procuram oferecer uma experiência de utilização mais individual e personalizada aos seus clientes. Obter feedback em transações de negócio online nunca foi tão fácil e acessível, o que apenas ajuda a catalisar a evolução dos sistemas de recomendação. Adicionalmente, o uso de dispositivos tecnológicos como smartphones e computadores, juntamente com a conexão à internet, estão também a crescer a um ritmo acelerado sem sinal de paragem em vista. Juntando-se a este grupo de indústrias em crescimento está a indústria fitness, que está a ficar cada vez mais popular. Com isto, mais e mais pessoas estão a começar a usar os dispositivos mencionados anteriormente em combinação com as suas atividades fitness, para aumentar o seu desempenho, monitorizar progresso, definir objetivos, entre outros. Consequentemente, o mercado para sistemas fitness (p.e. aplicações fitness) está a aumentar e já é bastante denso. No entanto, a qualidade associada com tais sistemas fica um pouco aquém tanto em termos de inovação como de funcionalidades essenciais. Como resultado disto, este projeto propôs uma solução – um sistema fitness sob a forma de uma aplicação móvel aliada a um poderoso sistema de recomendação. Este sistema é pretendido que providencie uma experiência mais individual e personalizada para qualquer tipo de utilizador fitness através da oferta de funcionalidades essenciais como registo e monitorização de informação, análise de progresso, e também através de funcionalidades inovadoras como a implementação de um sistema de recomendação capaz de sugerir tópicos relacionados com fitness (p.e. regimes de treino ou exercícios específicos) baseado em múltiplos fatores como os objetivos, características individuais e historial de cada utilizador. Além do mais, deve também oferecer um assistente pessoal virtual, onde os utilizadores podem expressar as suas questões e dúvidas, e tê-las respondidas instantaneamente por um chatbot. Durante o desenvolvimento foi decidido que um segundo sistema de recomendação seria necessário para melhorar o sistema no geral. Este, o sistema, depois de implementado, foi avaliado e pode ser concluído que o resultado foi um sucesso, tendo passado em todas as métricas definidas, exceto uma, com classificações médias nos questionários de satisfação acima de 4/5. O feedback obtido por um especialista no sistema de recomendação foi altamente vantajoso e no geral decentemente positivo, apenas com algumas questões que necessitam de melhoramento. Embora o sistema de recomendação inteligente não tenha conseguido ser testado com informação aplicável, a investigação e trabalho feito constituem uma mais valia caso mais tarde exista a possibilidade de aplicar dados reais.Recommender systems in general are increasingly becoming more exploited by companies who seek to provide a more individual and personalized user-experience to their customers. The fact that providing feedback on online business transactions is also becoming ever so easier only helps to catalyze the evolution of recommender systems. Moreover, the use of technological devices such as smartphones and computers, in conjunction with an internet connection, are also continuing to grow at a fast pace, with no slowing down in sight. Joining on this group of growing industries is the fitness sector, which is becoming increasingly popular. With this, more and more people are starting to use the aforementioned devices in combination with their fitness activities, to boost performance, monitor progress, define goals, among other things. Consequently, the market for fitness systems (i.e. fitness applications) is expanding and is already very dense. However, the associated quality with such systems falls short both in terms of innovation and even crucial features. As a result, this dissertation proposes a solution - an innovative fitness system in the form of a mobile application allied with a powerful recommender system. The system is intended to provide a more individual and personalized experience to any type of fitness user through the offering of crucial features including the log and monitor of information, progress analysis, and also through innovative features such as the implementation of a recommender system capable of making fitness-related suggestions (i.e. training regimens or specific exercises) based on multiple factors like the user’s individual goals, characteristics, and history. Additionally, it should also provide a personal virtual assistant, where users can express their questions and doubts and have them answered instantly by a chatbot. During development, it was decided that a second recommender system was required to improve the system as a whole. This, the system, after being implemented, was evaluated and it can be concluded that the result was a success, having passed in all the defined metrics, except one, with average classifications of 4/5 on the satisfaction inquiries. The feedback obtained from the expert on the recommender system was highly useful and, in general, decently positive, having only a few questions that need improvement. Even though the intelligent recommender system couldn’t be tested with applicable data, the investigation and work done constitute a great asset in case there’s the opportunity to employ real data

Ontology-based personalized performance evaluation and dietary recommendation for weightlifting.

Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology.Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology

New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

Principles for Designing Context-Aware Applications for Physical Activity Promotion

Mobile devices with embedded sensors have become commonplace, carried by billions of people worldwide. Their potential to influence positive health behaviors such as physical activity in people is just starting to be realized. Two critical ingredients, an accurate understanding of human behavior and use of that knowledge for building computational models, underpin all emerging behavior change applications. Early research prototypes suggest that such applications would facilitate people to make difficult decisions to manage their complex behaviors. However, the progress towards building real-world systems that support behavior change has been much slower than expected. The extreme diversity in real-world contextual conditions and user characteristics has prevented the conception of systems that scale and support end-users’ goals. We believe that solutions to the many challenges of designing context-aware systems for behavior change exist in three areas: building behavior models amenable to computational reasoning, designing better tools to improve our understanding of human behavior, and developing new applications that scale existing ways of achieving behavior change. With physical activity as its focus, this thesis addresses some crucial challenges that can move the field forward. Specifically, this thesis provides the notion of sweet spots, a phenomenological account of how people make and execute their physical activity plans. The key contribution of this concept is in its potential to improve the predictability of computational models supporting physical activity planning. To further improve our understanding of the dynamic nature of human behavior, we designed and built Heed, a low-cost, distributed and situated self-reporting device. Heed’s single-purpose and situated nature proved its use as the preferred device for self-reporting in many contexts. We finally present a crowdsourcing system that leverages expert knowledge to write personalized behavior change messages for large-scale context-aware applications.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144089/1/gparuthi_1.pd