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). 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. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. Physical Activity (PA) is one of the most important determinants of health.
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