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Data Driven Human Activity Recognition in Smart World
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Data Driven Human Activity Recognition in Smart World

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5950

Special Issue Editors

Dr. Wen Qi

E-Mail Website
Guest Editor
School of Future Technology, South China University of Technology, Guangzhou 511442, China
Interests: human-computer interaction; machine learning; artificial intelligence; robotics; smart wearables; human data analysis; adaptive control
Special Issues, Collections and Topics in MDPI journals
Dr. Stavros Ntalampiras

E-Mail Website
Guest Editor
Department of Computer Science, University of Milan, 20133 Milan, Italy
Interests: audio analyzing; AI; computer vision; robotics; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human behavior recognition integrates multi-disciplinary knowledge, such as artificial intelligence, image processing, computer vision theory, sensors, and cognitive science. It has a wide range of application scenarios in video surveillance, human–computer interaction, automatic driving, health and fitness detection, meta-universe, and other aspects. It is also a research hotspot and a key direction for researchers from various countries in recent years.

Currently, the research work in the field of behavior recognition mainly focuses on the following three aspects: based on wearable sensors, video image detection, and radio frequency signal recognition. However, no matter these methods are inseparable from sensors, human behavior data acquisition needs a variety of sensor cooperation. The leading equipment based on sensor behavior recognition is an infrared sensor, acceleration sensor, photoelectric sensor, and so on. Of course, it cannot be separated from other intelligent sensor equipment. During identification, testers need to wear or be in the detection range of sensor equipment, which can judge the behavior by calculating the data characteristics of the joint. The research of behavior recognition based on sensors gradually emerged, which enabled humans to enter the era of intelligent wearable technology. Although these behavior recognition methods have achieved many excellent results in their respective fields, they are mostly applied in some scenes. For behavior recognition in people's daily life, there is an urgent need for a simple, convenient, and inexpensive detection method that does not need to be worn and is not affected by the environment.

This Special Issue, therefore, aims to put together original research and review articles on the latest research advances, technologies, and solutions for identifying data-driven human activities in an intelligent world.

Dr. Wen Qi
Dr. Stavros Ntalampiras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

18 pages, 3045 KiB  
Article
Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology
by Maria Sideridou, Evangelia Kouidi, Vassilia Hatzitaki and Ioanna Chouvarda
Sensors 2024, 24(7), 2037; https://doi.org/10.3390/s24072037 - 22 Mar 2024
Cited by 2 | Viewed by 1886
Abstract
Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics [...] Read more.
Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely. Full article
(This article belongs to the Special Issue Data Driven Human Activity Recognition in Smart World)
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17 pages, 6790 KiB  
Article
Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints
by Kang Wang, Liwei Pang and Xiaoli Li
Sensors 2023, 23(7), 3749; https://doi.org/10.3390/s23073749 - 5 Apr 2023
Cited by 4 | Viewed by 3444
Abstract
The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have [...] Read more.
The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have proven ineffective in accurately identifying non-stopping points caused by trailing or round trips. To address this issue, this paper proposes a novel density peak clustering algorithm based on coherence distance, incorporating temporal and entropy constraints, referred to as the two-step DPCC-TE. The proposed algorithm introduces a coherence index to integrate spatial and temporal features, and imposes temporal and entropy constraints on the clusters to mitigate local density increase caused by slow-moving points and back-and-forth movements. Moreover, to address the issue of interactions between subclusters after one-step clustering, a two-step clustering algorithm is proposed based on the DPCC-TE algorithm. Experimental results demonstrate that the proposed two-step clustering algorithm outperforms the DBSCAN-TE and one-step DPCC-TE methods, and achieves an accuracy of 95.49% in identifying stopping points. Full article
(This article belongs to the Special Issue Data Driven Human Activity Recognition in Smart World)
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