Human Activity Recognition: Review, Taxonomy and Open Challenges

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2022 Sep 9

PMID 36080922

Authors

Muhammad Haseeb Arshad

Muhammad Bilal

Abdullah Gani

Affiliations

Soon will be listed here.

Abstract

Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.

Citing Articles

BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition.

Del-Hoyo-Alonso R, Hernandez-Ruiz A, Maranes-Nueno C, Lopez-Bosque I, Aznar-Gimeno R, Salvo-Ibanez P Sensors (Basel). 2024; 24(20).

PMID: 39460206 PMC: 11511535. DOI: 10.3390/s24206729.

Detection of Lowering in Sport Climbing Using Orientation-Based Sensor-Enhanced Quickdraws: A Preliminary Investigation.

Moaveninejad S, Janes A, Porcaro C Sensors (Basel). 2024; 24(14).

PMID: 39065974 PMC: 11280810. DOI: 10.3390/s24144576.

A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.

Ahmed N, Numan M, Kabir R, Islam M, Watanobe Y Sensors (Basel). 2024; 24(13).

PMID: 39001122 PMC: 11244405. DOI: 10.3390/s24134343.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

Almujally N, Khan D, Al Mudawi N, Alonazi M, Alazeb A, Algarni A Sensors (Basel). 2024; 24(10).

PMID: 38793886 PMC: 11124841. DOI: 10.3390/s24103032.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Boborzi L, Decker J, Rezaei R, Schniepp R, Wuehr M Sensors (Basel). 2024; 24(9).

PMID: 38732771 PMC: 11085719. DOI: 10.3390/s24092665.

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