An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

Abstract

In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

“…Not surprisingly, in the context of monitoring, various applications have been identified, e.g., health [ 60 , 89 , 115 , 158 , 159 ], smart buildings [ 90 , 116 , 160 ], agriculture [ 161 , 162 ], stress [ 61 , 117 , 136 , 163 ], transportation [ 91 ], military defense [ 164 ], etc. Other challenges comparatively highly studied in the included papers are QoS [ 92 , 93 , 118 , 128 , 137 , 138 , 139 , 152 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 ] with , and energy saving [ 62 , 63 , 94 , 95 , 96 , 114 , 119 , 142 , 143 , 144 , 175 ] with .…”