Combining wearable accelerometer and physiological data for activity and energy expenditure estimation

Abstract

Physical Activity (PA) is one of the most important determinants of health. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. In this paper we investigate the benefit of multiple physiological signals (Heart Rate (HR), respiration rate, Galvanic Skin Response (GSR), skin humidity) as well as accelerometer (ACC) data from two locations (wrist -combining ACC, GSR and skin humidity -and chest -combining ACC and HR) on PA type and EE estimation. We implemented single regression, activity recognition and activity-specific EE models on data collected from 16 subjects, while performing a set of PAs, grouped into six clusters (lying, sedentary, dynamic, walking, biking and running). Our results show that combining ACC and physiological signals improves performance for activity recognition (by 2 and 8% for the chest and wrist) and EE (by 36 -chest -and 35% -wrist -for single regression models, and by 18 -chest -and 46% -wrist -for activity-specific models). Physiological signals other than HR showed a coarser relation with level of physical exertion, resulting in being better predictors of activity cluster type and separation between inactivity and activity than EE, due to the weak correlation to EE within an activity cluster.

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data

“…RR interval represents the beat-to-beat fluctuations of the heart-rate, and the magnitude and underlying frequency of the decrease in RR interval that occurs with progressive exercise may be a more informative indicator of relative PA intensity than simple changes in HR [42]. Electrodermal activity and skin temperature have also been linked to relative PA intensity [43]. However, the classifiers based on these modalities alone did not provide satisfactory classification accuracy.…”

Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

“…Moreover, recent studies have pointed out the need for providing quality medical monitoring to a continuously increasing population. One of the proposed solutions resides in the use of wearable sensor systems [11], [12]. The capabilities of such systems include, among others, motion, physiological and biochemical sensing.…”

Functional Analysis of Mental Stress Based on Physiological Data of GSR Sensor

“…The authors in [9], investigate the classification techniques that can be used to automatically govern periods of acute stress relying on information confined in GSR and/or speech of a person. In [10], the authors implemented the single regression, activity recognition and activity-specific Energy Expenditure models on data collected from different subjects, while performing a set of Physical Activities, grouped into six clusters in the form of lying, sedentary, dynamic, walking, biking and running.…”