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Advanced Sensors for Postural or Gait Stability Assessment—2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1029

Special Issue Editor


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Guest Editor
School of Electronic Engineering, Xidian University, Xi'an, China
Interests: sensor signal processing; positioning and navigation; wireless communication
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Special Issue Information

Dear Colleagues,

As society has developed, there is an increasing demand for human pose recognition and detection. The development of wearable sensors and artificial intelligence technology has helped to solve this problem. It can be widely applied in various fields such as medical treatment, rehabilitation, intelligent care, and autonomous positioning. This Special Issue aims to publish relevant academic papers to solve such problems, providing support for promoting the development of human society. Therefore, we sincerely invite scholars to actively submit papers on advanced sensor signal processing. These papers are expected to utilize artificial intelligence technology, but are not limited to this field. Relevant sensors for attitude detection or recognition are expected to be published.

Prof. Dr. Lingfeng Shi
Guest Editor

Manuscript Submission Information

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Keywords

  • gait recognition
  • wearable sensors
  • micro-electromechanical systems (MEMS)
  • posture detection
  • deep learning
  • convolutional neural network (CNN)
  • bidirectional long short term memory (LSTM)

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Published Papers (1 paper)

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Research

21 pages, 1895 KB  
Article
Condition-Wise Robustness of Skeleton-Based Gait Sex Classification Under Smartphone Use, Occlusion, and Speed Variations
by A Hyun Jung, Yujin Oh, Ye Eun Kong, Min-Hyung Choi and Se Dong Min
Appl. Sci. 2026, 16(4), 1830; https://doi.org/10.3390/app16041830 - 12 Feb 2026
Viewed by 567
Abstract
Skeleton-based gait sex classification can reduce reliance on appearance cues, yet its robustness under everyday walking disturbances remains under-quantified. Using PsyMo 2D pose sequences (90° side view), we render Common Objects in Context (COCO) keypoints into compact grayscale skeleton images, segment sequences into [...] Read more.
Skeleton-based gait sex classification can reduce reliance on appearance cues, yet its robustness under everyday walking disturbances remains under-quantified. Using PsyMo 2D pose sequences (90° side view), we render Common Objects in Context (COCO) keypoints into compact grayscale skeleton images, segment sequences into fixed-length 15-frame clips, and classify them with a 3D residual convolutional neural network (CNN) under a subject-wise split shared across four aggregated conditions: overall (A), occlusion/carrying disturbance (B), speed variation (C), and smartphone use (D). To avoid an arbitrary decision rule, we select a global operating threshold on the validation set by sweeping τ to maximize macro-F1, apply it unchanged to the held-out test set, and report a threshold-sensitivity check. Robustness is audited via condition-wise confusion matrices, subgroup precision/recall with 95% subject-level bootstrap confidence intervals, and subject-level probability overlap. To contextualize condition-dependent behavior, we quantify joint-group attribution shifts using Gradient-weighted Class Activation Mapping (Grad-CAM) and examine a coarse arm-swing proxy under smartphone use. Subject-level test accuracy ranged from 0.761 to 0.870 across conditions A–D, with uncertainty summarized by 95% subject-level bootstrap confidence intervals; performance was lowest in B, with increased male→female errors. Overall, these results provide a transparent audit-and-interpretation framework for assessing skeleton-based gait sex classification under realistic walking perturbations in practical evaluation scenarios. Full article
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