Magazine article AI Magazine

Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

Magazine article AI Magazine

Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

Article excerpt

In the United States alone, approximately $2.6 trillion was spent on health care in 2010. It is well recognized that regular and accurate self-monitoring of physiological parameters and energy expenditure (calorie burn) can improve self-awareness of personal health by providing important feedback. Such awareness and tracking are prerequisites for cost-effective health management, illness reduction, health-conscious decision making, and long-term lifestyle changes.

There exists a wide spectrum of technologies available for monitoring physical activity, tracking energy expenditure, and managing weight. While many of these technologies provide some degree of accuracy, the most accurate among them, metabolic carts and calorimetry chambers, are bulky, expensive, and limited to laboratory and clinical use (Holdy 2004). In contrast, those that are small and inexpensive are, by and large, inaccurate.

At the high-accuracy end of the body monitor space is the doubly labeled water technique, a medical procedure that is guaranteed to give accurate measures of energy expenditure (Schoeller et al. 1986), but is very expensive and only gives readings for a 10- to 14-day period, making it impractical for continuous or short-term monitoring. At the less precise end of body monitor devices are several single-sensor (predominantly accelerometer-based) devices currently in the consumer market that are low cost and lightweight at the expense of accuracy (Beighle, Pangrazi, and Vincent 2001; Crouter et al. 2003).

We believe that a physiological monitoring device that provides estimates such as energy expenditure should be accurate, provide continuous user feedback, be user friendly, and be fully functional during all the activities of a user's daily life (free-living conditions). Moreover, the device should be cost-effective. The presented BodyMedia FIT armband system (BodyMedia 2011) achieves these goals. The effective integration of machine-learning methodologies and a multisensor technology used in a smart manner can rival medical-grade equipment in terms of clinical accuracy, at the same time surpassing such equipment by collecting data in real time under free-living conditions.

The BodyMedia FIT system is able to provide accurate free-living energy expenditure estimates for two principal reasons--usage of machine-learning-based algorithms and multiple-sensor technology. The system employs state-of-the-art data modeling and machine-learning techniques to implement a data-centered process to estimate, rather than measure, most key physiological parameters. Multiple sensors operate concurrently to provide a real-time user activity context, which, in turn, provides a context-sensitive estimate of the users' physiological parameters.

This article will describe some of the challenges associated with estimating energy expenditure, engineering the BodyMedia FIT armband, applying machine-learning techniques used in developing the estimation algorithms, as well as the results of several studies assessing accuracy of the device and the practical utility of the device in a weight-loss scenario.


Figure 1 shows the armband device (model MF). It is worn on the upper arm. The current commercial version uses four types of sensors: a three-axis accelerometer tracks the movement of the upper arm and body and provides information about body position. A synthetic heat-flux sensor measures the amount of heat being dissipated by the body to the immediate environment. Skin temperature and armband-cover temperature are measured by sensitive thermistors. The armband also measures galvanic skin response (GSR), the conductivity of the wearer's skin, which varies due to sweating and emotional stimuli. The armband contains a transceiver radio and a Universal Serial Bus (USB) port, allowing wireless transmission as well as wired uploading of data. The armband is made predominantly of natural Acrylonitrile Butadine Styrene (ABS) and 304 grade stainless steel and attaches to the arm with an elastic Velcro strap. …

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