Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor  

作　　者：Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul 

机构地区：[1]Department of Computer Engineering,School of Information and Communication Technology University of Phayao,Phayao,56000,Thailand [2]Image Information and Intelligence Laboratory,Department of Computer Engineering,Faculty of Engineering,Mahidol University,Nakhon Pathom,73170,Thailand [3]Department of Mathematics,Faculty of Applied Science,King Mongkut’s University of Technology North Bangkok,Bangkok,10800,Thailand [4]Intelligent and Nonlinear Dynamic Innovations Research Center,Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok,Bangkok,10800,Thailand

出　　处：《Intelligent Automation & Soft Computing》2023年第9期2669-2686,共18页智能自动化与软计算（英文）

基　　金：supported by the Thailand Science Research and Innovation Fund;the University of Phayao(Grant No.FF66-UoE001);King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-66-KNOW-05.

摘　　要：Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical impli-cations.Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments.Consequently,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts.This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection capability.The proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body parts.The experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor activity.Moreover,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and F1-score.Furthermore,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关 键 词：Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]