Fog Computing

15Reliable and Power-Efficient Machine Learning in Wearable Sensors

Parastoo Aliniaand Hassan Ghasemzadeh

Washington State University, Pullman, WA, USA

15.1 Introduction

Metabolic equivalent of task (MET) is an approximation of energy expenditure and an indicator of the intensity of physical activities. This measurement is commonly used to assess performance of physical activity interventions associated with many chronic illnesses such as coronary heart disease, type-2 diabetes, and cancer [1]. Healthy lifestyle changes such as diet control and exercise, which maintain a balance between dietary intake and calories burned, are key approaches in reducing complications due to these diseases [2]. This requires real-time tracking of physical activities that individuals at high risk of chronic diseases perform daily [3]. There are several approaches to calculate food intake and level of physical activity, including traditional self-reported questionnaires, indirect calorie meters, doubly labeled water techniques, and electrocardiographs [3, 4]. In recent years, however, accelerometers, gyroscopes, pressure sensors, and heart rate monitors have been used for physical activity detection and energy expenditure calculation [5–7] due to their small size, portability, low-power consumption, and low cost [3, 4].

Accelerometers have been widely used to estimate energy expenditure and MET of physical activities [3–5, 8]. Although, the current approaches for estimation of MET values using ...