210888 Using mobile phones and sensors to identify physical activity type in real time

Monday, November 9, 2009: 8:51 AM

Stephen S. Intille, PhD , Massachusetts Institute of Technology, Cambridge, MA
Fahd Albinali, PhD , Massachusetts Institute of Technology, Cambridge, MA
Selene Mota, SM , Massachusetts Institute of Technology, Cambridge, MA
Emmanuel Munguia Tapia, PhD , Massachusetts Institute of Technology, Cambridge, MA
William L. Haskell, PhD , Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA
Accelerometer logging devices, such as Actigraphs, can measure gross overall activity intensity, but knowledge of activity type might prove valuable for both measurement and intervention studies. Some recent work has demonstrated that postures and certain ambulatory activities can be recognized using statistical machine learning algorithms. These algorithms work by analyzing features computed from raw accelerometer data, given a set of example activities, and then computing models for each target activity that can be automatically and quickly matched to new incoming data streams using statistical pattern matching. Funded by the NIH's Genes and Environment Initiative, we are developing new wireless accelerometers that communicate with the phone and permit detection of a wide variety of physical activities at the moment that they are happening without requiring a person to wear bulky or wired equipment. We will demonstrate the prototype system and discuss pilot results of testing the algorithm in the lab and in free-living conditions. In one experiment with 20 subjects, an overall accuracy of 75% on 51 different physical activities was obtained with 2 min of training data per activity. If activity types of different intensities are clustered (i.e., “walking 2mph” and “walking 3mph” are clustered as “walking”), overall activity type detection accuracy increased to 91%. We will discuss how this system might be used for both physical activity measurement research and enabling novel physical activity health promotion interventions that provide just-in-time, tailored, physical activity feedback using the mobile phones.

Learning Objectives:
1. Discuss one particular emerging technology for measurement of physical activity using mobile phones and wireless accelerometers. 2. Demonstrate how the technology can identify activity type in real time using data sent to the phone from the accelerometers. 3. Explain how the technology might be used for physical activity measurement research and for developing novel interventions for promoting physical activity.

Keywords: Technology, Physical Activity

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: Stephen Intille, Ph.D., is Technology Director of the House_n Consortium in the MIT Department of Architecture. His research is focused on the development of context-recognition algorithms and interface design strategies for ubiquitous computing environments and mobile devices. In current work he is developing systems for preventive health care that support healthy aging and well-being in the home by motivating longitudinal behavior change, especially using mobile phones. He received his Ph.D. from MIT in 1999 working on computational vision at the MIT Media Laboratory, an S.M. from MIT in 1994, and a B.S.E. degree in Computer Science and Engineering from the University of Pennsylvania in 1992. He has published research on computational stereo depth recovery, real-time and multi-agent tracking, activity recognition, perceptually-based interactive environments, and technology for preventive healthcare. Dr. Intille has been principal investigator on two NSF ITR grants focused on automatic activity recognition from sensor data in the home, as well as the MIT principal investigator on sensor-enabled health technology grants from Intel, the National Institutes of Health, and the Robert Wood Johnson Foundation. He received an IBM Faculty award in 2003. In current work he exploring how to create tools for common mobile phones that permit longitudinal measurement of health behaviors for research, especially the type, duration, intensity, and location of physical activity.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.