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Sensors for Human Activity Recognition: 3rd Edition
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Sensors for Human Activity Recognition: 3rd Edition

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 4258

Special Issue Editors

Dr. Hui Liu

E-Mail Website
Guest Editor
Cognitive Systems Laboratory, Faculty of Mathematic/Informatics, University of Bremen, 28359 Bremen, Germany
Interests: biosignal processing; feature selection and feature space reduction; human activity recognition; real-time recognition systems; knee bandage; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tanja Schultz

E-Mail Website
Guest Editor
Computer Science, Faculty of Mathematic/Informatics, University of Bremen, 28359 Bremen, Germany
Interests: biosignal processing; human-centered man–machine interfaces; user states and traits modeling; machine learning; interfaces based on muscle and brain activities; automatic speech recognition; silent speech interfaces; brain interfaces
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hugo Gamboa

E-Mail Website
Guest Editor
Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UNL), Faculty of Sciences and Technology, NOVA University of Lisbon, 2820-001 Caparica, Portugal
Interests: instrumentation; signal processing; machine learning; human activity recognition (HAR)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) has been playing an increasingly important role in the digital age. 

High-quality sensory observations applicable to recognizing users' activities, whether through external or internal (wearable) sensing technology, are inseparable from sensors' sophisticated designs and appropriate applications. 

Having been studied and verified adequately, traditional sensors suitable for human activity recognition—such as external sensors for smart homes, optical sensors such as cameras (for capturing video signals), and bioelectrical and biomechanical sensors for wearable applications—continue to be researched in depth for more effective and efficient usage, among which specific areas of life facilitated by sensor-based HAR have been continuously increasing. 

Meanwhile, innovative sensor research for HAR is also extremely active in the academic community, including brand new types of sensors appropriate for HAR, new designs and applications of the above-mentioned traditional sensors, and the introduction of non-traditional HAR-related sensor types into HAR tasks, among others. 

This Special Issue aims to provide researchers in related fields with a platform to demonstrate their unique insights and ground-breaking achievements, encouraging authors to submit their state-of-the-art research and contributions on sensors for HAR. 

The main topics for this Issue include the following: 

  • Sensor design and development;
  • Embedded signal processing;
  • Biosignal instrumentation;
  • Mobile sensing and mobile-phone-based signal processing;
  • Wearable sensor and body sensor networks;
  • Printable sensors;
  • Implants;
  • Behavior recognition;
  • Applications to healthcare, sports, edutainment, and others;
  • Sensor-based machine learning.

You may choose our Joint Special Issue in Biosensors.

Dr. Hui Liu
Prof. Dr. Tanja Schultz
Dr. Hugo Gamboa
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.

Keywords

  • human activity recognition
  • wearable sensor
  • behavior recognition

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Related Special Issue

Published Papers (5 papers)

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Research

15 pages, 1990 KiB  
Article
New Parameters Based on Ground Reaction Forces for Monitoring Rehabilitation Following Tibial Fractures and Assessment of Heavily Altered Gait
by Christian Wolff, Elke Warmerdam, Tim Dahmen, Tim Pohlemann, Philipp Slusallek and Bergita Ganse
Sensors 2025, 25(8), 2475; https://doi.org/10.3390/s25082475 - 15 Apr 2025
Viewed by 287
Abstract
Instrumented insoles have created opportunities for patient monitoring via long-term recordings of ground reaction forces (GRFs). As the GRF curve is altered in patients after lower-extremity fracture, parameters defined on established curve landmarks often cannot be used to monitor the early rehabilitation process. [...] Read more.
Instrumented insoles have created opportunities for patient monitoring via long-term recordings of ground reaction forces (GRFs). As the GRF curve is altered in patients after lower-extremity fracture, parameters defined on established curve landmarks often cannot be used to monitor the early rehabilitation process. We aimed to screen several new GRF curve-based parameters for suitability and hypothesized an interrelation with days after surgery. In an observational longitudinal study, data were collected from 13 patients with tibial fractures during straight walking at hospital visits using instrumented insoles. Parametrized curves were fitted and regression analyses conducted to determine the best fit, reflected in the highest R2-value and lowest fitting error. A Wald Test with t-distribution was employed for statistical analysis. Strides were classified as regular or non-regular, and changes in this proportion were analyzed. Among the 12 parameters analyzed, those with the highest R2-values were the mean force between inflection points (R2 = 0.715, p < 0.001, t42 = 9.89), the absolute time between inflection points (R2 = 0.707, p < 0.001, t42 = 9.83), and the highest overall force (R2 = 0.722, p < 0.001, t42 = 10.05). There was a significant increase in regular strides on both injured (R2 = 0.427, p < 0.001, t42 = 5.83) and healthy (R2 = 0.506, p < 0.001, t42 = 6.89) sides. The proposed parameters and assessment of the regular stride ratio enable new options for analyses and monitoring during rehabilitation after tibial shaft fractures. They are robust to pathologic GRF curves, can be determined independently from spatiotemporal coherence, and thus might provide advantages over established methods. Full article
(This article belongs to the Special Issue Sensors for Human Activity Recognition: 3rd Edition)
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21 pages, 3325 KiB  
Article
Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique
by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler and Udo Frese
Sensors 2025, 25(5), 1468; https://doi.org/10.3390/s25051468 - 27 Feb 2025
Viewed by 548
Abstract
Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By [...] Read more.
Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. High-validation F1 scores were achieved by the best-performing models, particularly CNNs and InceptionTime, indicating their effectiveness in near-fall classification. The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. Nevertheless, our findings suggest the applicability of machine-learning-based STF prevention systems for workplace safety monitoring and, more generally, applications in fall mitigation. To further improve the accuracy and generalizability of the system, future research should investigate multimodal data integration and improved classification techniques. Full article
(This article belongs to the Special Issue Sensors for Human Activity Recognition: 3rd Edition)
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14 pages, 3207 KiB  
Article
A Usability Pilot Study of a Sensor-Guided Interactive System for Dexterity Training in Parkinson’s Disease
by Nic Krummenacher, Stephan M. Gerber, Manuela Pastore-Wapp, Michael Single, Stephan Bohlhalter, Tobias Nef and Tim Vanbellingen
Sensors 2025, 25(4), 1051; https://doi.org/10.3390/s25041051 - 10 Feb 2025
Viewed by 655
Abstract
This pilot study aimed to evaluate the usability of a new, feedback-based dexterity training system in people with Parkinson’s disease (PwPD) and healthy adults. Seven PwPD and seven healthy adults participated in the study. The System Usability Scale (SUS) and the Post-Study System [...] Read more.
This pilot study aimed to evaluate the usability of a new, feedback-based dexterity training system in people with Parkinson’s disease (PwPD) and healthy adults. Seven PwPD and seven healthy adults participated in the study. The System Usability Scale (SUS) and the Post-Study System Usability Questionnaire Version 3 (PSSUQ) were used to assess usability. Additionally, the feedback shown as a counter, detected through newly developed algorithms, was evaluated by comparing the device-detected repetitions during six exercises to those counted by a supervisor. High median SUS scores of 92.5 were obtained in both PwPD (IQR = 81.25–98.75) and healthy adults (IQR = 87.5–93.75, maximum score 100, minimum score 0). Similarly, high PSSUQ median scores were achieved after the session (1.14, IQR = 1.00–1.33, PD; 1.08, IQR = 1.00–1.58, healthy adults, maximum score 1, minimum score 7). PwPD completed 648 repetitions, with 551 (85%) correctly recognized by the system. For healthy adults, 883 out of 913 (97%) repetitions were classified as right. The present study showed high usability and high perceived user satisfaction for the new training system in all study participants. The system effectively detects exercise repetition rates but requires further refinement to enhance accuracy for specific pinch grip exercises. Full article
(This article belongs to the Special Issue Sensors for Human Activity Recognition: 3rd Edition)
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27 pages, 9185 KiB  
Article
Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine
by Saleha Kamal, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Hanan Aljuaid, Ahmad Jalal and Hui Liu
Sensors 2025, 25(1), 200; https://doi.org/10.3390/s25010200 - 1 Jan 2025
Cited by 1 | Viewed by 977
Abstract
Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in surveillance systems, healthcare environments, and many more. This has led researchers to develop coherent and robust systems that efficiently perform [...] Read more.
Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in surveillance systems, healthcare environments, and many more. This has led researchers to develop coherent and robust systems that efficiently perform HAR. Although there have been many efficient systems developed to date, still, there are many issues to be addressed. There are several elements that contribute to the complexity of the task, making it more challenging to detect human activities, i.e., (i) poor lightning conditions; (ii) different viewing angles; (iii) intricate clothing styles; (iv) diverse activities with similar gestures; and (v) limited availability of large datasets. However, through effective feature extraction, we can develop resilient systems for higher accuracies. During feature extraction, we aim to extract unique key body points and full-body features that exhibit distinct attributes for each activity. Our proposed system introduces an innovative approach for the identification of human activity in outdoor and indoor settings by extracting effective spatio-temporal features, along with a Multi-Class Support Vector Machine, which enhances the model’s performance to accurately identify the activity classes. The experimental findings show that our model outperforms others in terms of classification, accuracy, and generalization, indicating its efficient analysis on benchmark datasets. Various performance metrics, including mean recognition accuracy, precision, F1 score, and recall assess the effectiveness of our model. The assessment findings show a remarkable recognition rate of around 88.61%, 87.33, 86.5%, and 81.25% on the BIT-Interaction dataset, UT-Interaction dataset, NTU RGB + D 120 dataset, and PKUMMD dataset, respectively. Full article
(This article belongs to the Special Issue Sensors for Human Activity Recognition: 3rd Edition)
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18 pages, 2769 KiB  
Article
A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography
by Zhen Ding, Tao Hu, Yanlong Li, Longfei Li, Qi Li, Pengyu Jin and Chunzhi Yi
Sensors 2024, 24(18), 5949; https://doi.org/10.3390/s24185949 - 13 Sep 2024
Viewed by 1117
Abstract
Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning [...] Read more.
Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems. Full article
(This article belongs to the Special Issue Sensors for Human Activity Recognition: 3rd Edition)
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