Reminder Care System: An Activity-Aware Cross-Device Recommendation System

Alzheimer’s disease (AD) affects large numbers of elderly people worldwide and represents a significant social and economic burden on society, particularly in relation to the need for long term care facilities. These costs can be reduced by enabling people with AD to live independently at home for a longer time. The use of recommendation systems for the Internet of Things (IoT) in the context of smart homes can contribute to this goal. In this paper, we present the Reminder Care System (RCS), a research prototype of a recommendation system for the IoT for elderly people with cognitive disabilities. RCS exploits daily activities that are captured and learned from IoT devices to provide personalised recommendations. The experimental results indicate that RCS can inform the development of real-world IoT applications.

[1]  Abdenour Bouzouane,et al.  Intelligent temporal data driven world actuation in ambient environments: Case study: Anomaly recognition and assistance provision in smart home , 2013, 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS).

[2]  Joseph Kee-Yin Ng,et al.  SmartMind: Activity Tracking and Monitoring for Patients with Alzheimer's Disease , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[3]  Ron Brookmeyer,et al.  Forecasting the prevalence of preclinical and clinical Alzheimer's disease in the United States , 2018, Alzheimer's & Dementia.

[4]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[5]  Lina Yao,et al.  WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes , 2018, Computing.

[6]  Satoshi Tanaka,et al.  Applying Ontology and Probabilistic Model to Human Activity Recognition from Surrounding Things , 2007 .

[7]  Choong Seon Hong,et al.  Autonomic Inferring of M2M-IoT Service-usage from User-Emotion and Environmental Information , 2013 .

[8]  Ray Strong,et al.  A Recommendation System for Proactive Health Monitoring Using IoT and Wearable Technologies , 2017, 2017 IEEE International Conference on AI & Mobile Services (AIMS).

[9]  Didier Stricker,et al.  Creating and benchmarking a new dataset for physical activity monitoring , 2012, PETRA '12.

[10]  J. M. Eklund,et al.  Real-Time Analysis for Intensive Care: Development and Deployment of the Artemis Analytic System , 2010, IEEE Engineering in Medicine and Biology Magazine.

[11]  David Massimo,et al.  User Preference Modeling and Exploitation in IoT Scenarios , 2018, IUI.

[12]  Iyad Tumar,et al.  A Proactive Multi-type Context-Aware Recommender System in the Environment of Internet of Things , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[13]  Tsang Wai-Hung Nelson,et al.  Tracking Indoor Activities of Patients with Mild Cognitive Impairment Using Motion Sensors , 2017, AINA 2017.

[14]  Lina Yao,et al.  Web-Based Management of the Internet of Things , 2015, IEEE Internet Computing.

[15]  Jacques Demongeot,et al.  Health "Smart" home: information technology for patients at home. , 2002, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[16]  Diane J. Cook,et al.  PUCK: an automated prompting system for smart environments: toward achieving automated prompting—challenges involved , 2012, Personal and Ubiquitous Computing.

[17]  Sachchidanand Singh,et al.  IoT and distributed machine learning powered optimal state recommender solution , 2016, 2016 International Conference on Internet of Things and Applications (IOTA).

[18]  Zhiwen Yu,et al.  A context-aware reminder system for elders based on fuzzy linguistic approach , 2012, Expert Syst. Appl..

[19]  Yasamin Sahaf,et al.  COMPARING SENSOR MODALITIES FOR ACTIVITY RECOGNITION , 2011 .

[20]  Munjo Kim,et al.  Data Pipeline for Generation and Recommendation of the IoT Rules Based on Open Text Data , 2016, 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[21]  Lina Yao,et al.  Things of Interest Recommendation by Leveraging Heterogeneous Relations in the Internet of Things , 2016, ACM Trans. Internet Techn..

[22]  Richard O. Oyeleke,et al.  Situ-Centric Reinforcement Learning for Recommendation of Tasks in Activities of Daily Living In Smart Homes , 2018, 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC).

[23]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[24]  Lina Yao,et al.  RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array , 2015, EAI Endorsed Trans. Ambient Syst..

[25]  Adrian Basarab,et al.  Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer's Disease , 2018, IEEE Journal of Biomedical and Health Informatics.

[26]  Bernard Lefebvre,et al.  Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy , 2007, OWLED.

[27]  Li Liu,et al.  Recognizing Complex Activities by a Probabilistic Interval-Based Model , 2016, AAAI.