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Human Motion Perception for Intelligent Systems Awareness and Robotic Control
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Human Motion Perception for Intelligent Systems Awareness and Robotic Control

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Applications".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3107

Editors

Dr. Dimitrios Menychtas

E-Mail Website
Guest Editor
Information Technologies Institute of the Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece
Interests: robotic applications; human motion analysis; collaborative robots
Dr. Dimitrios Giakoumis

E-Mail Website
Guest Editor
Information Technologies Institute of the Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece
Interests: human–robot interaction; robot vision; service robot perception and cognition; activity and behavior analysis and modeling; safe and socially aware robot navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Redwan Alqasemi

E-Mail Website
Guest Editor
Center for Assistive Rehabilitation and Robotics Technologies (CARRT), Mechanical Engineering, University of South Florida, Tampa, FL 33620, USA
Interests: robotics; assistive technologies; virtual reality; user interfaces

Special Issue Information

Dear Colleagues,

A wealth of information is being transmitted with every motion of the human body. From the subtle motions during simple tasks that ensure efficient adaptation and robustness to high-intensity actions during sports, the characteristics that can be extracted contain multi-faceted information. Detecting meaningful information through motion capture systems, wearables, and more recently, AI methods has enabled a seamless communication between humans and intelligent systems. This has been transformative for healthcare, sports science, social sciences, rehabilitation and industry applications. In this abundance of motion sensing, the most fundamental question that remains unanswered is what information is meaningful and how it can be extracted. Indeed, there is no shortage of creative approaches to perceive human motion, isolate gestures and encode them as inputs to be processed. However, between the staggering versatility of the human kinematic chain, the biological differences in each person, and the different information each task needs, there is no one-size-fit-all method to detect motions, or to convey them to an intelligent system. For example, involuntary small motions during post-stroke rehabilitation contain crucial data, while, in an industrial context, cobots focusing on intention prediction treat small motions as noise to be discarded.

In this Special Issue, the goal is to explore and disseminate novel and creative approaches to extract meaningful information from voluntary and involuntary motions of the human body, based on the applications, and incorporate them to advanced intelligent systems in an ethical way, as well as map future directions and identify knowledge gaps. The scope includes, but is not limited, to the following:

  • Computer vision for joint detection
  • Novel methods for modelling the human body 
  • Novel sensors/methods to perceive motion
  • AI systems for semantic perception of motion
  • Deep-learning methods for motion prediction and intention classification
  • Intelligent control of prostheses
  • Control of supernumerary robotic limbs
  • AI-powered personalized rehabilitation/performance analysis
  • Collaborative robots (cobots)
  • Haptic interfaces

Dr. Dimitrios Menychtas
Dr. Dimitrios Giakoumis
Dr. Redwan Alqasemi
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly 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 1800 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 motion detection
  • computer vision
  • artificial intelligence
  • supernumerary robotic limbs
  • cobots

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Published Papers (3 papers)

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Research

30 pages, 7840 KB  
Article
Explainable Early Activity Recognition via Wearable Inertial Sensors for Human–Robot Collaboration in Agriculture
by Lefteris Benos, Erotokritos Skordilis, Remigio Berruto and Dionysis Bochtis
Information 2026, 17(9), 865; https://doi.org/10.3390/info17090865 - 7 Sep 2026
Viewed by 182
Abstract
In open-field agriculture, timely human activity recognition (HAR) is critical for anticipating worker actions and enabling proactive human–robot collaboration. This study developed an offline early HAR framework based on pre-segmented activity sequences. Long Short-Term Memory (LSTM) networks were used in conjunction with wearable [...] Read more.
In open-field agriculture, timely human activity recognition (HAR) is critical for anticipating worker actions and enabling proactive human–robot collaboration. This study developed an offline early HAR framework based on pre-segmented activity sequences. Long Short-Term Memory (LSTM) networks were used in conjunction with wearable inertial sensors mounted on the chest, cervical region, lumbar region, and right and left wrists. Accelerometer, gyroscope, and magnetometer signals were collected from 20 participants during an outdoor agricultural material-handling task, with a mobile ground robot serving as the receiving platform for the crate. Each activity was represented by cumulative prefixes from 30% to 100% of its duration. At the 30% observation ratio, the model achieved a macro-F1 score of 0.9314, compared with 0.9481 for the full sequence, corresponding to an absolute difference of 0.0167. Shapley Additive Explanations (SHAP) were also used to identify the body locations, sensor modalities, and signal channels that contributed most. The early decisions were mainly supported by sensors placed on the trunk, with wrist sensors providing complementary information, particularly for standing classification. Multimodal inertial information was also important. The most influential inputs were mainly gyroscope and magnetometer channels from the chest, cervical region, and lumbar region. In conclusion, the high performance at early observation ratios highlights the potential to support more adaptive and better-coordinated robot-assistance strategies. Full article
(This article belongs to the Special Issue Human Motion Perception for Intelligent Systems Awareness and Robotic Control)
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30 pages, 21630 KB  
Article
Hybrid Invariant Latent Feature Graph Transformer for Skeleton-Based Human Action Recognition
by Kabul Khudaybergenov and Avazjon Marakhimov
Information 2026, 17(8), 729; https://doi.org/10.3390/info17080729 - 28 Jul 2026
Viewed by 418
Abstract
Skeleton-based human action recognition is an important problem in applied vision systems, yet many existing approaches depend on a single skeleton descriptor or a single feature-learning mechanism. This restriction can weaken the representation of local body kinematics, long-range joint relations, and temporal dependencies [...] Read more.
Skeleton-based human action recognition is an important problem in applied vision systems, yet many existing approaches depend on a single skeleton descriptor or a single feature-learning mechanism. This restriction can weaken the representation of local body kinematics, long-range joint relations, and temporal dependencies within an action sequence. To address these limitations, this paper proposes HILF-GT (Hybrid Invariant Latent Feature Graph Transformer), a hybrid Graph Convolutional Network (GCN)-Transformer framework based on multiple spatio-temporal invariant latent features. The representation module constructs complementary structured tensors from skeleton graphs, inter-joint distances, adjacent-frame joint displacements, and inter-limb angles. Instead of transforming these descriptors into image-like maps for separate Convolutional Neural Network (CNN)-based classification, HILF-GT keeps their graph and temporal organization during learning. A local GCN branch models skeleton-aware kinematic patterns, whereas a graph-aware Transformer branch uses biased self-attention and cross-attention to capture dependencies among distant joints, frames, and latent-feature streams. A Perceiver-style latent bottleneck is further introduced to reduce the memory cost of global attention over frame-joint tokens. Experiments were conducted on four standard benchmark datasets, including NTU-RGB+D 60, NTU-RGB+D 120, NW-UCLA, and UTD-MHAD. The proposed method achieved 93.1% and 97.20% accuracy on the NTU-RGB+D 60 Cross-Subject and Cross-View protocols, 88.15% and 90.20% on the NTU-RGB+D 120 Cross-Subject and Cross-Setup protocols, 98.50% on NW-UCLA, and 97.50% on UTD-MHAD. Full article
(This article belongs to the Special Issue Human Motion Perception for Intelligent Systems Awareness and Robotic Control)
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14 pages, 2366 KB  
Article
Validating the Performance of VR Headset Eye-Tracking Using Gold Standard Eye-Tracker and MoCap System
by Russell Nathan Todd, Jian Gong, Amy Catherine Banic and Qin Zhu
Information 2026, 17(2), 143; https://doi.org/10.3390/info17020143 - 2 Feb 2026
Viewed by 1884
Abstract
The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel [...] Read more.
The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel paradigm using a physically moving object to evaluate the accuracy of dynamic smooth pursuit, a key oculomotor function in mental state assessment. We rigorously validated the performance of the HTC Vive Pro Eye’s integrated eye-tracker against the Tobii Pro Glasses 3 using a high-precision OptiTrack motion capture system as ground-truth for object position. Eight participants completed both 2D and 3D gaze-tracking tasks. In the 2D condition, they tracked a dot on a screen, while in the 3D condition, they tracked a physically moving object. The real-world object trajectories captured by OptiTrack were replicated within a VR environment. Gaze data from both the VR headset and the Tobii glasses were recorded simultaneously and compared to the OptiTrack baseline using Dynamic Time Warping (DTW) to quantify accuracy. Results revealed a task-dependent performance. In the 2D task, the Tobii glasses demonstrated significantly lower DTW distances, indicating superior accuracy. Conversely, in the 3D task, the VR headset significantly outperformed the glasses, showing a closer match to the real object trajectory. This suggests that while traditional eye-trackers excel in constrained 2D contexts, integrated VR eye-tracking is more accurate for naturalistic 3D gaze pursuit. We conclude that VR headset eye-tracking is not only a reliable but also a cost-effective tool for research, particularly offering enhanced performance for studies conducted within immersive 3D simulations. Full article
(This article belongs to the Special Issue Human Motion Perception for Intelligent Systems Awareness and Robotic Control)
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