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Human Activity Recognition Based on Sensors: Challenges and Perspectives

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2674

Special Issue Editor


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Guest Editor
Department of Computer Science and Mathematics, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Interests: artificial intelligence; human activity recognition; automated learning; guidance; assistive technologies; smart homes equipped with ubiquitous sensors

Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) is an increasingly active and interdisciplinary field of research that focuses on identifying and analyzing human actions through various sensing technologies. The ability to recognize what a person is doing, determine the current step of a task, and detect whether assistance is needed is fundamental for the development of intelligent and responsive systems. This capability is particularly valuable in applications where automation, real-time monitoring, and adaptive assistance can enhance human performance, safety, and well-being.

HAR leverages a variety of artificial intelligence techniques, including machine learning and deep learning, to process data collected from multiple sensing modalities. These include wearable sensors (e.g., accelerometers, gyroscopes, and physiological sensors), vision-based systems (e.g., cameras and depth sensors), and ambient sensors (e.g., motion detectors and pressure sensors). The fusion of these technologies enables a more accurate and context-aware understanding of human behavior in real-world environments.

The applications of HAR are vast and span multiple domains. In healthcare and ambient assisted living (AAL), HAR plays a crucial role in monitoring elderly individuals, detecting falls, and providing timely intervention in cases of health emergencies. In industrial settings, HAR can assist workers by identifying ergonomic risks, improving workplace safety, and optimizing task efficiency. The field also has significant applications in sports and fitness, where personalized coaching systems analyze movement patterns to enhance performance and prevent injuries.

Moreover, HAR is integral to the advancement of augmented and virtual reality, where it enhances user interaction by enabling gesture-based controls and immersive experiences. In security and surveillance, HAR is used to detect abnormal behaviors, identify potential threats, and improve public safety measures. Military and defense applications include soldier monitoring, combat simulation, and real-time threat assessments.

As HAR continues to evolve, new research directions are emerging, such as self-supervised learning for activity recognition, multimodal sensor fusion, and real-time activity tracking using edge computing. This Special Issue aims to compile cutting-edge research in the field of human activity recognition, highlighting recent advancements, innovative methodologies, and emerging applications that are shaping the future of this technology.

Prof. Dr. Bruno Bouchard
Guest Editor

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Keywords

  • human activity recognition (HAR)
  • sensor-based activity recognition
  • machine learning for HAR
  • wearable sensors
  • ambient assisted living (AAL)
  • multimodal sensor fusion
  • deep learning for activity recognition
  • real-time activity monitoring
  • gesture and motion recognition
  • smart surveillance and security

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Published Papers (2 papers)

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Research

17 pages, 3928 KB  
Article
Limited Interchangeability of Smartwatches and Lace-Mounted IMUs for Running Gait Analysis
by Theodor Meingast, Bryson Carrier, Amanda Melvin, Kenneth M. Kozloff, Alexandra F. DeJong Lempke and Adam S. Lepley
Sensors 2025, 25(17), 5553; https://doi.org/10.3390/s25175553 - 5 Sep 2025
Viewed by 1245
Abstract
Spatiotemporal running metrics such as cadence, stride length (SL), and ground contact time (GCT) are important for assessing performance and injury risk. However, such metrics are traditionally assessed using laboratory-based tools that are often inaccessible in applied settings. Wearable devices including smartwatches and [...] Read more.
Spatiotemporal running metrics such as cadence, stride length (SL), and ground contact time (GCT) are important for assessing performance and injury risk. However, such metrics are traditionally assessed using laboratory-based tools that are often inaccessible in applied settings. Wearable devices including smartwatches and lace-mounted inertial measurement units (IMUs) offer promising alternatives, yet cross-device agreement in reporting spatiotemporal variables remains unclear. This study evaluated agreement between a commercial smartwatch and lace-mounted IMUs across varied distances and environments in 65 physically active adults (33 female/32 male, height: 171.0 ± 8.9 cm; weight: 70.9 ± 15.2 kg). Participants completed indoor and outdoor runs (2.5 km, 5 km, 10 km, 20 km) wearing both devices simultaneously. Average cadence demonstrated acceptable agreement (MAPE = 4.1%, CCC = 0.66) and supported equivalence, particularly among males, during outdoor conditions, and longer run distances. In contrast, peak cadence showed weak correlation (MAPE = 5.3%, CCC = 0.29), and SL and GCT demonstrated poor agreement (MAPE = 14.9–19.0%, CCC = 0.30–0.39) across all conditions. While average cadence may serve as a metric for cross-device comparisons, especially for males, and longer-distance outdoor runs, other spatiotemporal metrics demonstrated poor agreement, limiting interchangeability. Understanding device-specific capabilities is essential when interpreting wearable-derived gait data. Further validation using gold-standard tools is needed to support accurate and applied use of wearable technologies. Full article
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15 pages, 968 KB  
Article
Validity of AI-Driven Markerless Motion Capture for Spatiotemporal Gait Analysis in Stroke Survivors
by Balsam J. Alammari, Brandon Schoenwether, Zachary Ripic, Neva Kirk-Sanchez, Moataz Eltoukhy and Lauri Bishop
Sensors 2025, 25(17), 5315; https://doi.org/10.3390/s25175315 - 27 Aug 2025
Viewed by 945
Abstract
Gait recovery after stroke is a primary goal of rehabilitation, therefore it is imperative to develop technologies that accurately identify gait impairments after stroke. Markerless motion capture (MMC) is an emerging technology that has been validated in healthy individuals. Our study aims to [...] Read more.
Gait recovery after stroke is a primary goal of rehabilitation, therefore it is imperative to develop technologies that accurately identify gait impairments after stroke. Markerless motion capture (MMC) is an emerging technology that has been validated in healthy individuals. Our study aims to evaluate the validity of MMC against an instrumented walkway system (IWS) commonly used to evaluate gait in stroke survivors. Nineteen participants performed three comfortable speed (CS) and three fastest speed (FS) walking trials simultaneously recorded with IWS and MMC system, KinaTrax (HumanVersion 8.2, KinaTrax Inc., Boca Raton, FL, USA). Pearson’s correlation coefficient and intraclass correlation coefficient (ICC (3,1), 95%CI) were used to evaluate the agreement and consistency between systems. Furthermore, Bland–Altman plots were used to estimate bias and Limits of Agreement (LoA). For both CS and FS, agreements between MMC and IWS were good to excellent in all parameters except for non-paretic single-limb support time (SLS), which revealed moderate agreement during CS. Additionally, stride width and paretic SLS showed poor agreement in both conditions. Biases eliminated systematic errors, with variable LoAs in all parameters during both conditions. Findings indicated high validity of MMC in measuring spatiotemporal gait parameters in stroke survivors. Further validity work is warranted. Full article
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