Pattern analysis in daily physical activity data for personal health management

Purpose: sedentary lifestyles have resulted in an increasing number of people who are at increased risk of various conditions and diseases, including overweight, obesity, and metabolic syndromes. Our objective was to systematically record the daily life journal on a platform to increase the self-awareness and improve the sedentary lifestyle and to assist clinicians in understanding and facilitating patients' daily physical activity. Method: we developed a portable activity pattern recognition system designed to automatically recognize the daily activity habits of users, and provide visualized life logs on the wellness self-management platform for patients and clinicians. Based on the participants' and the clinician's comments, appropriate modifications were made. Results: persuading people to improve their activities during non-working hours can enhance the general physical activity. Since users' smartphones automatically monitor their energy expenditure, healthcare professionals can use these data to assist their patients in addressing health problems stemming from the obesity or metabolic syndromes, thus empowering users to avert or delay the progression of diabetes, cardiovascular disease and other complications. Discussion and conclusions: the clinical pilot study showed the feasibility of applying this persuasive technology to improve the physical activity of overweight people. The limitation of the study is the need for Wi-Fi and 3G environments and a smartphone.

[1]  William E Kraus,et al.  Exercise training amount and intensity effects on metabolic syndrome (from Studies of a Targeted Risk Reduction Intervention through Defined Exercise). , 2007, The American journal of cardiology.

[2]  J. Mechanick,et al.  Complementary and alternative medicine and the management of the metabolic syndrome. , 2008, Journal of the American Dietetic Association.

[3]  Yiqiang Chen,et al.  Cross-People Mobile-Phone Based Activity Recognition , 2011, IJCAI.

[4]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[5]  Sian Lun Lau,et al.  Movement recognition using the accelerometer in smartphones , 2010, 2010 Future Network & Mobile Summit.

[6]  Teddy Mantoro,et al.  A Comparison Study of Classifier Algorithms for Mobile-phone's Accelerometer Based Activity Recognition , 2012 .

[7]  Tae-Seong Kim,et al.  A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer , 2010, IEEE Transactions on Information Technology in Biomedicine.

[8]  S.Y. Lee,et al.  Accelerometer's position free human activity recognition using a hierarchical recognition model , 2010, The 12th IEEE International Conference on e-Health Networking, Applications and Services.

[9]  Y.-K. Lee,et al.  Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis , 2010, 2010 5th International Conference on Future Information Technology.

[10]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[11]  Christoph Busch,et al.  Using Hidden Markov Models for accelerometer-based biometric gait recognition , 2011, 2011 IEEE 7th International Colloquium on Signal Processing and its Applications.

[12]  J. Shaw,et al.  Breaks in Sedentary Time , 2008, Diabetes Care.

[13]  Sathyanarayanan Rangarajan.  Using Hidden Markov Models for Biometric Gait Recognition , 2010 .

[14]  Goro Ishii,et al.  Kolmogorov-smirnov test in life test , 1959 .

[15]  Markus Neuhäuser,et al.  Wilcoxon-Mann-Whitney Test , 2011, International Encyclopedia of Statistical Science.

[16]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[17]  Diane J. Cook,et al.  Human Activity Recognition and Pattern Discovery , 2010, IEEE Pervasive Computing.

[18]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[20]  Teddy Mantoro,et al.  Recognizing user activity based on accelerometer data from a mobile phone , 2011, 2011 IEEE Symposium on Computers & Informatics.

[21]  Bernt Schiele,et al.  Discovery of activity patterns using topic models , 2008 .

[22]  Ruzena Bajcsy,et al.  Enhancing context awareness with activity recognition and radio fingerprinting , 2011, 2011 IEEE Fifth International Conference on Semantic Computing.

[23]  Vivian H. Heyward,et al.  Advanced Fitness Assessment & Exercise Prescription , 1997 .

[24]  Abdenour Bouzouane,et al.  Possibilistic Behavior Recognition in Smart Homes for Cognitive Assistance , 2010, Plan, Activity, and Intent Recognition.

[25]  Futoshi Naya,et al.  Workers' Routine Activity Recognition using Body Movements and Location Information , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[26]  T. Fu,et al.  Costs of metabolic syndrome‐related diseases induced by obesity in Taiwan , 2008, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[27]  Deborah Estrin,et al.  Improving activity classification for health applications on mobile devices using active and semi-supervised learning , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[28]  André Dias,et al.  Assessing physical activity in the daily life of cystic fibrosis patients , 2012, Pervasive Mob. Comput..

[29]  R. Wilcox.  Kolmogorov–Smirnov Test , 2005 .

[30]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[31]  Shin-Dug Kim,et al.  A Dynamic Approach to Recognize Activities in WSN , 2013, Int. J. Distributed Sens. Networks.