Activity Recognition for Diabetic Patients Using a Smartphone

Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines machine-learning and symbolic reasoning to recognise high-level lifestyle activities using sensor data obtained primarily from the patient’s smartphone. We compare five methods for machine-learning which differ in the amount of manually labelled data by the user, to investigate the trade-off between the labelling effort and recognition accuracy. In an evaluation on real-life data, the highest accuracy of 83.4 % was achieved by the MCAT method, which is capable of gradually adapting to each user.

[1]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.

[2]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[3]  Alfonso E. Romero,et al.  Recognising lifestyle activities of diabetic patients with a smartphone , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[4]  Oliver Amft,et al.  Ambient, On-Body, and Implantable Monitoring Technologies to Assess Dietary Behavior , 2011 .

[5]  Esther Rodríguez-Villegas,et al.  COMMODITY12: A smart e-health environment for diabetes management , 2013, J. Ambient Intell. Smart Environ..

[6]  Mitja Lustrek,et al.  Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors , 2016, IEEE Journal of Biomedical and Health Informatics.

[7]  Dieter Fox,et al.  Location-Based Activity Recognition , 2005, KI.

[8]  Marek J. Sergot,et al.  A logic-based calculus of events , 1989, New Generation Computing.

[9]  Alfonso E. Romero,et al.  Activity Recognition for an Agent-Oriented Personal Health System , 2014, PRIMA.

[10]  Mitja Lustrek,et al.  Recognition of high-level activities with a smartphone , 2015, UbiComp/ISWC Adjunct.

[11]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[12]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[13]  Kostas Stathis,et al.  Hydra: A hybrid diagnosis and monitoring architecture for diabetes , 2014, 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom).

[14]  Matjaz Gams,et al.  Adapting activity recognition to a person with Multi-Classifier Adaptive Training , 2015, J. Ambient Intell. Smart Environ..

[15]  Mitja Lustrek,et al.  Demo abstract: Activity recognition and human energy expenditure estimation with a smartphone , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[16]  Sung-Bae Cho,et al.  Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.

[17]  D. Cook,et al.  Smart Home-Based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients , 2009, Journal of diabetes science and technology.