{
  "abstract": "Exoskeleton systems face significant challenges in maintaining reliable gait prediction under dynamically changing walking environments. Conventional learning-based methods often suffer from limited adaptability and high computational latency, especially when deployed on low-power embedded hardware. A neuromorphic computing framework based on a synaptic convolutional neural network with memory autapses (SCN-MA) is introduced for real-time gait environment recognition. A structured gait experiment was conducted to collect pneumatic mechanomyography (pMMG) and stretch signals under multiple walking conditions, forming a multimodal dataset for training and evaluation. The framework adopts multiple binary gait classification models trained in parallel, which enhances modularity and scalability. A reinforcement learning based ensemble policy is then trained to integrate the outputs of these models for final decision-making. The complete system is implemented on the Zynq-based FPGA platform, enabling end-to-end prediction with low latency and high energy efficiency. Experimental results confirm improved accuracy and adaptability over baseline models, supporting the use of neuromorphic architectures in exoskeleton systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology",
    "Inner Mongolia Autonomous Region Key Laboratory for Ecological Environment Collaborative Intelligence, Inner Mongolia Normal University",
    "School of Control Science and Engineering, Dalian University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jirou Feng"
    },
    {
      "affs": [
        1
      ],
      "name": "Shuai Hao"
    },
    {
      "affs": [
        0
      ],
      "name": "Junhwan Choi"
    },
    {
      "affs": [
        0
      ],
      "name": "Junhwi Cho"
    },
    {
      "affs": [
        2
      ],
      "name": "Wenyue Cui"
    },
    {
      "affs": [
        0
      ],
      "name": "Jung Kim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/619667prm37s9.bib",
  "cited_by": 0,
  "doi": "10.1186/s12984-026-02071-x",
  "fulltext_url": "https://doi.org/10.1186/s12984-026-02071-x",
  "id": "619667prm37s9",
  "issns": [
    "1743-0003"
  ],
  "landing_url": "https://doi.org/10.1186/s12984-026-02071-x",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC321-571"
    ],
    [
      "openalex",
      "2204|Prosthetics and Rehabilitation Robotics"
    ]
  ],
  "openalex": "W7171441788",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/619667prm37s9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:46Z",
      "native_id": "95b65d06301544ee8a5ae14e69e8c681",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s12984-026-02071-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC321-571",
      "subfield": "general"
    }
  ],
  "title": "Gait environments recognition via synaptic convolutional neural network with memory autapses",
  "title_text": "Gait environments recognition via synaptic convolutional neural network with memory autapses",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:46Z",
  "url": "https://research.draconitic.com/paper/619667prm37s9",
  "venue": "Journal of NeuroEngineering and Rehabilitation",
  "versions": []
}