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Multimodal Human Behavior Understanding in Human–AI Interaction: Sensor-Based Signal Processing and Interaction Techniques

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 189

Special Issue Editors

Dr. Changzeng Fu

E-Mail Website
Guest Editor
1. Sydney Smart Technology College, Northeastern University, Shenyang, China
2. Graduate School of Engineering Science, Osaka University, Toyonaka, Japan
Interests: affective computing; deep learning; human–robot interaction
Dr. Shiqi Zhao

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Northeastern University, Shenyang, China
Interests: brain–computer interface; AI accelerator; customized chip design
Dr. Yuliang Zhao

E-Mail Website
Guest Editor
School of Information Science and Engineering, Northeastern University, Shenyang, China
Interests: big data; Internet of Things; deep learning; smart sensors; BioMEMS (BioMEMS)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the contemporary era of human–AI interaction, the capacity to interpret human behavior through multimodal signals has emerged as a pivotal research frontier. This Special Issue, titled “Multimodal Human Behavior Understanding in Human–AI Interaction: Sensor-Based Signal Processing and Interaction Techniques”, is dedicated to advancing the scientific and technological paradigms that underpin the comprehension of human actions and emotions within interactive AI systems. By leveraging sophisticated signal processing techniques and innovative interaction methods, this Special Issue seeks to elucidate the complex interplay between humans and AI, thereby enhancing the efficacy and intuitiveness of these interactions. Contributions will explore the integration of emotional computing, multimodal human behavior understanding, and the application of wearable sensors and brain–computer interfaces, collectively aiming to foster a more seamless and responsive human–AI collaborative framework.

Dr. Changzeng Fu
Dr. Shiqi Zhao
Dr. Yuliang Zhao
Guest Editors

Manuscript Submission Information

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Keywords

  • multimodal signal processing
  • human behavior understanding
  • affective computing
  • cognitive computing
  • wearable sensors
  • brain–computer interfaces
  • human–AI interaction
  • signal processing techniques
  • interaction methods
  • real-time systems
  • user experience

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Published Papers (1 paper)

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Research

18 pages, 1683 KiB  
Article
Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting
by Chris Lochhead and Robert B. Fisher
Sensors 2025, 25(13), 4106; https://doi.org/10.3390/s25134106 (registering DOI) - 30 Jun 2025
Abstract
As gaitpathology assessment systems improve both in accuracy and efficiency, the prospect of using these systems in real healthcare applications is becoming more realistic. Although gait analysis systems have proven capable of detecting gait abnormalities in supervised tasks in laboratories and clinics, there [...] Read more.
As gaitpathology assessment systems improve both in accuracy and efficiency, the prospect of using these systems in real healthcare applications is becoming more realistic. Although gait analysis systems have proven capable of detecting gait abnormalities in supervised tasks in laboratories and clinics, there is comparatively little investigation into making such systems explainable to healthcare professionals who would use gait analysis in practice in home-based settings. There is a “black box” problem with existing machine learning models, where healthcare professionals are expected to “trust” the model making diagnoses without understanding its underlying reasoning. To address this applicational barrier, an end-to-end pipeline is introduced here for creating graph feature embeddings, generated using a bespoke Spatio-temporal Graph Convolutional Network and per-joint Principal Component Analysis. The latent graph embeddings produced by this framework led to a novel semi-supervised weighting function which quantifies and ranks the most important joint features, which are used to provide a description for each pathology. Using these embeddings with a K-means clustering approach, the proposed method also outperforms the state of the art by between 4.53 and 16% in classification accuracy across three datasets with a total of 14 different simulated gait pathologies from minor limping to ataxic gait. The resulting system provides a workable improvement to at-home gait assessment applications by providing accurate and explainable descriptions of the nature of detected gait abnormalities without need of prior labeled descriptions of detected pathologies. Full article
(This article belongs to the Special Issue Multimodal Human Behavior Understanding in Human–AI Interaction: Sensor-Based Signal Processing and Interaction Techniques)
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