Fusion of heart rate, respiration and motion measurements from a wearable sensor system to enhance energy expenditure estimation

• This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications. 

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences (hsv//eng)

Keyword

Accelerometer

Energy expenditure

Impedance pneumography

Neural network

Wearable device

Accelerometers

Heart

Neural networks

Energy expenditure estimation

Mean absolute error

Motion measurements

Multi-layer perceptron neural networks

Wearable devices

Wearable sensor systems

Wearable sensors

Publication and Content Type

ref (subject category)

art (subject category)