Multimodal hybrid reasoning methodology for personalized wellbeing services. (1st February 2016)

• Record Type: Journal Article
• Authors: Ali, Rahman; Afzal, Muhammad; Hussain, Maqbool; Ali, Maqbool; Siddiqi, Muhammad Hameed; Lee, Sungyoung; Ho Kang, Byeong
• Is Part Of: Computers in biology and medicine. Volume 69(2016)
• Page Start: 10
• Page End: 28
• Publication Date: 2016-02-01
• Subjects: Multimodal reasoning -- Hybrid reasoning -- Case-based reasoning (CBR) -- Hybrid-CBR -- Rule-based reasoning (RBR) -- Preference-based reasoning (PBR) -- Physical activity recommendation -- Wellness services; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.11.013
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: A wellness system provides wellbeing recommendations to support experts in promoting a healthier lifestyle and inducing individuals to adopt healthy habits. Adopting physical activity effectively promotes a healthier lifestyle. A physical activity recommendation system assists users to adopt daily routines to form a best practice of life by involving themselves in healthy physical activities. Traditional physical activity recommendation systems focus on general recommendations applicable to a community of users rather than specific individuals. These recommendations are general in nature and are fit for the community at a certain level, but they are not relevant to every individual based on specific requirements and personal interests. To cover this aspect, we propose a multimodal hybrid reasoning methodology ( HRM ) that generates personalized physical activity recommendations according to the user׳s specific needs and personal interests. The methodology integrates the rule-based reasoning ( RBR ), case-based reasoning ( CBR ), and preference-based reasoning ( PBR ) approaches in a linear combination that enables personalization of recommendations. RBR uses explicit knowledge rules from physical activity guidelines, CBR uses implicit knowledge from experts׳ past experiences, and PBR uses users׳ personal interests and preferences. To validate the methodology, a weight management scenario is considered and experimented with. The RBR part of the methodology generates goal, weight status, and plan recommendations, the CBR part suggests the top three relevant physical activities for executing the recommended plan, and the PBR part filters out irrelevant recommendations from the suggested ones using the user׳s personal preferences and interests. To evaluate the methodology, a baseline-RBR system is developed, which is improved first using ranged rules and ultimately using a hybrid-CBR . A comparison of the results of these systems shows that hybrid-CBR outperforms the modified-RBR and baseline-RBR systems. Hybrid-CBR yields a 0.94% recall, a 0.97% precision, a 0.95% f -score, and low Type I and Type II errors. Graphical abstract: Highlights: Studying personalized wellbeing services and enabling development of technology supported strategies. Integrating reasoning methodologies to support development of multimodal hybrid reasoning models. Utilizing explicit and implicit knowledge from guidelines and expert׳s past experiences to enhance personalization. Integrating rule-based, case-based and preference-based reasoning linearly for precise and personalized recommendations.