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Recent Trends in Sensor-Based and Vision-Based Human Activity Recognition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 23 September 2024 | Viewed by 13564

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Special Issue Editor

Dr. Hao Jiang

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College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Interests: human activity recognition; Internet of Things; machine learning; deep learning; sensors enabled IoT; Smart Homes
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Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) has become a major research topic with a wide range of applications, such as healthcare, public security, social interaction, and life assistance. The advent of advanced smart sensor technologies for IoT has dramatically improved the accuracy and robustness of HAR tasks. The automatic real-time human action and activity recognition using different sensors’ data and their combinations (e.g., vision sensors, depth sensors, and skeleton sensors) has been explored in recent years. Additionally, deep-learning-based methods have become popular for HAR and can automatically generate optimal features from raw input data generated from sensors without any human intervention and can also identify hidden patterns in data. Key challenges in the domain include, among others: the non-availability of a substantial number of labeled training samples, the higher computational cost and system resource requirements of deep learning architectures as opposed to shallow learning algorithms, the difficulty in data annotation scarcity, data heterogeneity, context-based and high-level interpretability, and non-invasive activity sensing. This Special Issue focuses on the current state-of-the-art HAR approaches in sensor-based or vision-based environments. The main objective is to stimulate original, unpublished research addressing the challenges above through the concurrent use of sensors or vision, schemes, frameworks, algorithms, and platforms. Potential topics include, but are not limited to:

Modeling and analysis for human activity recognition;
Machine-learning-driven frameworks for human activity recognition;
Human activity recognition under uncertainty, noise, and incomplete data;
Security and privacy issues in sensor-based or vision-based human activity recognition;
High computational power, small size, and low cost of sensors for human activity recognition.

Dr. Hao Jiang
Guest Editor

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Keywords

human activity recognition
machine learning
security
privacy
sensors
internet of things

Published Papers (5 papers)

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Research

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17 pages, 4729 KiB

Open AccessArticle

Human Activity Recognition in the Presence of Occlusion

by Ioannis Vernikos, Theodoros Spyropoulos, Evaggelos Spyrou and Phivos Mylonas

Sensors 2023, 23(10), 4899; https://doi.org/10.3390/s23104899 - 19 May 2023

Viewed by 1439

Abstract

The presence of occlusion in human activity recognition (HAR) tasks hinders the performance of recognition algorithms, as it is responsible for the loss of crucial motion data. Although it is intuitive that it may occur in almost any real-life environment, it is often underestimated in most research works, which tend to rely on datasets that have been collected under ideal conditions, i.e., without any occlusion. In this work, we present an approach that aimed to deal with occlusion in an HAR task. We relied on previous work on HAR and artificially created occluded data samples, assuming that occlusion may prevent the recognition of one or two body parts. The HAR approach we used is based on a Convolutional Neural Network (CNN) that has been trained using 2D representations of 3D skeletal motion. We considered cases in which the network was trained with and without occluded samples and evaluated our approach in single-view, cross-view, and cross-subject cases and using two large scale human motion datasets. Our experimental results indicate that the proposed training strategy is able to provide a significant boost of performance in the presence of occlusion. Full article

(This article belongs to the Special Issue Recent Trends in Sensor-Based and Vision-Based Human Activity Recognition)

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26 pages, 6676 KiB

Open AccessArticle

A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton–Raphson-Based Feature Selection Approach for Human Gait Recognition

by Faiza Jahangir, Muhammad Attique Khan, Majed Alhaisoni, Abdullah Alqahtani, Shtwai Alsubai, Mohemmed Sha, Abdullah Al Hejaili and Jae-hyuk Cha

Sensors 2023, 23(5), 2754; https://doi.org/10.3390/s23052754 - 02 Mar 2023

Cited by 4 | Viewed by 1768

Abstract

The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video sequences accurately; however, it is a challenging and time-consuming approach. Due to important applications, such as biometrics and video surveillance, HGR has improved performance over the last half-decade. Based on the literature, the challenging covariant factors that degrade gait recognition performance include walking while wearing a coat or carrying a bag. This paper proposed a new two-stream deep learning framework for human gait recognition. The first step proposed a contrast enhancement technique based on the local and global filters information fusion. The high-boost operation is finally applied to highlight the human region in a video frame. Data augmentation is performed in the second step to increase the dimension of the preprocessed dataset (CASIA-B). In the third step, two pre-trained deep learning models—MobilenetV2 and ShuffleNet—are fine-tuned and trained on the augmented dataset using deep transfer learning. Features are extracted from the global average pooling layer instead of the fully connected layer. In the fourth step, extracted features of both streams are fused using a serial-based approach and further refined in the fifth step by using an improved equilibrium state optimization-controlled Newton–Raphson (ESOcNR) selection method. The selected features are finally classified using machine learning algorithms for the final classification accuracy. The experimental process was conducted on 8 angles of the CASIA-B dataset and obtained an accuracy of 97.3, 98.6, 97.7, 96.5, 92.9, 93.7, 94.7, and 91.2%, respectively. Comparisons were conducted with state-of-the-art (SOTA) techniques, and showed improved accuracy and reduced computational time. Full article

(This article belongs to the Special Issue Recent Trends in Sensor-Based and Vision-Based Human Activity Recognition)

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31 pages, 24417 KiB

Open AccessArticle

Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques

by Sireesha Gundu and Hussain Syed

Sensors 2023, 23(5), 2569; https://doi.org/10.3390/s23052569 - 25 Feb 2023

Cited by 1 | Viewed by 1439

Abstract

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset. Full article

(This article belongs to the Special Issue Recent Trends in Sensor-Based and Vision-Based Human Activity Recognition)

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12 pages, 1730 KiB

Open AccessArticle

Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

by Keshav Thapa, Yousung Seo, Sung-Hyun Yang and Kyong Kim

Sensors 2023, 23(2), 683; https://doi.org/10.3390/s23020683 - 06 Jan 2023

Cited by 4 | Viewed by 1577

Abstract

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform. Full article

(This article belongs to the Special Issue Recent Trends in Sensor-Based and Vision-Based Human Activity Recognition)

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Review

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33 pages, 2616 KiB

Open AccessReview

Human Activity Recognition: Review, Taxonomy and Open Challenges

by Muhammad Haseeb Arshad, Muhammad Bilal and Abdullah Gani

Sensors 2022, 22(17), 6463; https://doi.org/10.3390/s22176463 - 27 Aug 2022

Cited by 47 | Viewed by 6500

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. Full article

(This article belongs to the Special Issue Recent Trends in Sensor-Based and Vision-Based Human Activity Recognition)

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