Metabolic energy expenditure estimation using a position-agnostic wearable sensor system

This paper presents an energy expenditure estimation method that uses a wearable accelerometer sensor, but does not rely on a priori knowledge about the location of the sensor. The sensor can be worn at any of three pre-defined locations, namely, right wrist, right thigh and right ankle. It is shown that once the system is trained, the proposed mechanism can perform sensor location detection in run-time within a period as short as 4.2 seconds. A sensor-position-specific energy expenditure estimation model is then applied. Experiments were carried out on 25 healthy subjects, and 14 activities with diverse intensities were included. It is demonstrated that the sensor position can be detected at an accuracy of 99.68%, and the Root-Mean-Square-Error for energy expenditure estimation is 1.79 METs (Metabolic Equivalent of Task).

[1]  D. Bassett,et al.  Estimating energy expenditure using accelerometers , 2006, European Journal of Applied Physiology.

[2]  Maxim A. Batalin,et al.  MEDIC: Medical embedded device for individualized care , 2008, Artif. Intell. Medicine.

[3]  Kuan Zhang,et al.  Improving energy expenditure estimation for physical activity. , 2004, Medicine and science in sports and exercise.

[4]  J. Seidell,et al.  The public health impact of obesity. , 2001, Annual review of public health.

[5]  Maxim A. Batalin,et al.  Incremental Diagnosis Method for Intelligent Wearable Sensor Systems , 2007, IEEE Transactions on Information Technology in Biomedicine.

[6]  U. Ekelund,et al.  Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. , 2004, Journal of applied physiology.

[7]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[8]  Bo Dong,et al.  Wearable diet monitoring through breathing signal analysis , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  Scott E Crouter,et al.  A novel method for using accelerometer data to predict energy expenditure. , 2006, Journal of applied physiology.

[10]  Kong Y Chen,et al.  An artificial neural network model of energy expenditure using nonintegrated acceleration signals. , 2007, Journal of applied physiology.

[11]  Bo Dong,et al.  Use of a Wireless Network of Accelerometers for Improved Measurement of Human Energy Expenditure , 2014, Electronics.

[12]  Graham W Horgan,et al.  An evaluation of the IDEEA™ activity monitor for estimating energy expenditure , 2012, British Journal of Nutrition.

[13]  Bo Dong,et al.  Wearable networked sensing for human mobility and activity analytics: A systems study , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[14]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[15]  Julien Penders,et al.  Energy expenditure estimation using wearable sensors: a new methodology for activity-specific models , 2012, Wireless Health.