An Integrated Mobile System for Long-Term Aerobic Activity Monitoring and Support in Daily Life

This paper presents a mobile and unobtrusive platform that enables the accurate monitoring of physical activities in daily life, and is integrated into a healthcare system supporting out-of-hospital services. The main focus of the paper is to describe and evaluate a complete data processing chain for recognizing different activities, and estimating their intensity level. To develop a mobile platform that is more applicable in real-life scenarios than systems in previous work, an additional other activity class is introduced to deal with various everyday, household and fitness activities. Evaluation of the proposed methods is done on a dataset recorded by 9 subjects performing 16 different activities, while wearing the mobile platform consisting of three IMUs, a HR-monitor and a mobile companion unit. Another important aspect of this work is the integration of the mobile platform with an EHR, providing access for both the clinician (e.g. to enter a patient's medical record or to set up a care plan) and the patient (e.g. to watch assigned educational material). Feedback about the patient's daily progress is also given both to the patient and the clinician, preserving or even increasing the patient's motivation to follow the defined care plan, and providing valuable information on program adherence for the clinician.

[1]  L Alford,et al.  What men should know about the impact of physical activity on their health , 2010, International journal of clinical practice.

[2]  Didier Stricker,et al.  Introducing a modular activity monitoring system , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  A. Bauman,et al.  Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. , 2007, Circulation.

[4]  Juha Pärkkä,et al.  Personalization Algorithm for Real-Time Activity Recognition Using PDA, Wireless Motion Bands, and Binary Decision Tree , 2010, IEEE Transactions on Information Technology in Biomedicine.

[5]  H. Nieminen,et al.  Estimating Intensity of Physical Activity: A Comparison of Wearable Accelerometer and Gyro Sensors and 3 Sensor Locations , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  B E Ainsworth,et al.  Compendium of physical activities: an update of activity codes and MET intensities. , 2000, Medicine and science in sports and exercise.

[7]  M. Moy,et al.  Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[8]  Gerald Bieber,et al.  Activity Recognition for Everyday Life on Mobile Phones , 2009, HCI.

[9]  J. Naughton,et al.  Physical activity and the prevention of coronary heart disease. , 1972, Preventive medicine.

[10]  Xi Long,et al.  Single-accelerometer-based daily physical activity classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[12]  Didier Stricker,et al.  Towards global aerobic activity monitoring , 2011, PETRA '11.

[13]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[14]  Daniel Roggen,et al.  Designing and sharing activity recognition systems across platforms , 2011 .

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

[16]  Luc Cluitmans,et al.  Advancing from offline to online activity recognition with wearable sensors , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.