We are proposing to advance objective assessment methodology through development of an integrated measurement system to evaluate free-living physical activity (PA). The overall goal of this proposal is to develop and test new and innovative sensor technology to assess PA in the field over long periods of time with minimal subject burden and at relatively low cost. In addition to an accelerometer, which is commonly used in PA assessment research to measure body motion, we are proposing to use sensors that capture characteristics of breathing and the environmental context. Inclusion of these additional sensors ensures that the measurement system will objectively quantify GEI-relevant features (e.g. exposure to environmental contaminants, indoor vs. outdoor activity) and increase the precision and validity of estimates of PA mode and the associated energy expenditure. A rigorous process of design optimization will be employed so that the final integrated measurement system (IMS) will be appropriate for use in large-scale epidemiological studies at a reasonable cost and with minimal subject burden.
The specific aims are: 1) To design and develop a miniature self-contained IMS to assess physical activity. The device will include an accelerometer sensor, a ventilation sensor, and an environmental context sensor, which will determine if the activity takes place indoors or outdoors;2) To calibrate the IMS during different types and intensities of indoor and outdoor physical activity;3) To develop statistical data processing methods that will combine the information from the three streams of data (acceleration, ventilation and setting) to estimate physical activity mode and energy expenditure;4) To validate the modeled estimates of physical activity mode and energy expenditure developed in Aim 3. We have assembled a multi-disciplinary team representing the fields of exercise physiology, electrical engineering, signal processing, mechanotronics, and statistics which provides us with the broad expertise needed to successfully carry out this project. These areas include expertise in sensor design and fabrication (electrical engineering, mechanotronics, signal processing), validation and calibration of the sensors during light, moderate and vigorous activity (exercise physiology) and development of appropriate statistical models to evaluate the performance of the various sensors and to develop and test activity pattern recognition systems. A field-deployable system will be complete at the end of Year 4.
| Welch, Whitney A; Bassett, David R; Freedson, Patty S et al. (2014) Cross-validation of waist-worn GENEA accelerometer cut-points. Med Sci Sports Exerc 46:1825-30 |
| Welch, Whitney A; Bassett, David R; Thompson, Dixie L et al. (2013) Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer. Med Sci Sports Exerc 45:2012-9 |
| Liu, Shaopeng; Gao, Robert X; John, Dinesh et al. (2013) Tissue artifact removal from respiratory signals based on empirical mode decomposition. Ann Biomed Eng 41:1003-15 |
| John, Dinesh; Sasaki, Jeffer; Staudenmayer, John et al. (2013) Comparison of raw acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors. Sensors (Basel) 13:14754-63 |
| John, Dinesh; Staudenmayer, John; Freedson, Patty (2013) Simple to complex modeling of breathing volume using a motion sensor. Sci Total Environ 454-455:184-8 |
| Freedson, Patty; Bowles, Heather R; Troiano, Richard et al. (2012) Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. Med Sci Sports Exerc 44:S1-4 |
| Mo, Lingfei; Liu, Shaopeng; Gao, Robert X et al. (2012) Wireless design of a multisensor system for physical activity monitoring. IEEE Trans Biomed Eng 59:3230-7 |
| Liu, Shaopeng; Gao, Robert; He, Qingbo et al. (2012) Improved regression models for ventilation estimation based on chest and abdomen movements. Physiol Meas 33:79-93 |
| Liu, Shaopeng; Gao, Robert X; John, Dinesh et al. (2012) Multisensor data fusion for physical activity assessment. IEEE Trans Biomed Eng 59:687-96 |
| Staudenmayer, John; Zhu, Weimo; Catellier, Diane J (2012) Statistical considerations in the analysis of accelerometry-based activity monitor data. Med Sci Sports Exerc 44:S61-7 |
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