A Novel Framework for Daily Life Activity and Context Recognition In-The-Wild Using Smartphone Inertial Sensors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256799" target="_blank" >RIV/61989100:27240/24:10256799 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10753579" target="_blank" >https://ieeexplore.ieee.org/document/10753579</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2024.3498852" target="_blank" >10.1109/ACCESS.2024.3498852</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Novel Framework for Daily Life Activity and Context Recognition In-The-Wild Using Smartphone Inertial Sensors

Popis výsledku v původním jazyce

Human Activity Recognition (HAR) systems are pivotal for numerous applications in pervasive computing. The rapid proliferation of smart devices, including smartphones has indeed transformed the way people live and interact with technology. Smartphones, in particular, have become indispensable for many individuals, with a large percentage of the global population owning and relying on them. Modern smartphones, equipped with various sensors have opened up new possibilities for HAR. This study introduces a novel framework for context-aware human activity recognition in real-world, unconstrained environments, leveraging smartphone inertial sensors (accelerometer and gyroscope). The proposed Human Activity and Associated Context Recognition (HAACR) framework includes data pre-processing, feature extraction and selection, and classification phases. Utilizing the publicly available Extrasensory dataset, the study examines 06 primary activities and 23 associated contexts. Data from 55 participants, filtered for completeness of both accelerometer and gyroscope readings, were used. Feature extraction was performed using multiple sliding window sizes. The extracted set of features was subjected to feature selection and later on classification using three different classifiers i.e., random forest, decision tree, and k-nearest neighbors classifiers. The proposed framework achieved the highest classification accuracy of 98.97%. This accuracy significantly surpasses previous state-of-the-art methods available in the literature, demonstrating the effectiveness of integrating data from multiple sensors for enhanced activity and context recognition in natural settings. The findings highlight the importance of window size and feature selection in improving recognition performance, offering valuable insights for future HAR research and applications. © 2013 IEEE.

Název v anglickém jazyce

A Novel Framework for Daily Life Activity and Context Recognition In-The-Wild Using Smartphone Inertial Sensors

Popis výsledku anglicky

Human Activity Recognition (HAR) systems are pivotal for numerous applications in pervasive computing. The rapid proliferation of smart devices, including smartphones has indeed transformed the way people live and interact with technology. Smartphones, in particular, have become indispensable for many individuals, with a large percentage of the global population owning and relying on them. Modern smartphones, equipped with various sensors have opened up new possibilities for HAR. This study introduces a novel framework for context-aware human activity recognition in real-world, unconstrained environments, leveraging smartphone inertial sensors (accelerometer and gyroscope). The proposed Human Activity and Associated Context Recognition (HAACR) framework includes data pre-processing, feature extraction and selection, and classification phases. Utilizing the publicly available Extrasensory dataset, the study examines 06 primary activities and 23 associated contexts. Data from 55 participants, filtered for completeness of both accelerometer and gyroscope readings, were used. Feature extraction was performed using multiple sliding window sizes. The extracted set of features was subjected to feature selection and later on classification using three different classifiers i.e., random forest, decision tree, and k-nearest neighbors classifiers. The proposed framework achieved the highest classification accuracy of 98.97%. This accuracy significantly surpasses previous state-of-the-art methods available in the literature, demonstrating the effectiveness of integrating data from multiple sensors for enhanced activity and context recognition in natural settings. The findings highlight the importance of window size and feature selection in improving recognition performance, offering valuable insights for future HAR research and applications. © 2013 IEEE.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

175176-175195

Kód UT WoS článku

001367281200017

EID výsledku v databázi Scopus

2-s2.0-85209769248