{
  "annotation_id": "32a29047-8915-475d-8ef3-481b8a580188",
  "date_created": "2026-02-17T05:53:08.777000Z",
  "date_modified": "2026-02-17T05:53:08.777000Z",
  "file_hash": "859e096435d954f142fabf4ab4f8c68e45404fd0eb1abe90b3d29c5bb92bc928",
  "private": false,
  "record": {
    "abstract": "Accurate prediction of joint kinematics and kinetics is essential for advancing gait analysis and developing intelligent assistive systems such as prosthetics and exoskeletons. This study presents a hybrid LSTM-UKF framework for estimating ankle angle and ground reaction force (GRF) across varying walking speeds. A multimodal sensor fusion strategy integrates force plate data, knee angle, and GRF signals to enrich biomechanical context. Model performance was evaluated using RMSE and $R^2$ under subject-specific validation. The LSTM-UKF consistently outperformed standalone LSTM and UKF models, achieving up to 18.6\\% lower RMSE for GRF prediction at 3 km/h. Additionally, UKF integration improved robustness, reducing ankle angle RMSE by up to 22.4\\% compared to UKF alone at 1 km/h. These results underscore the effectiveness of hybrid architectures for reliable gait prediction across subjects and walking conditions.",
    "arxiv_id": "2601.06473",
    "authors": [
      "Mundla Narasimhappa",
      "Praveen Kumar"
    ],
    "categories": [
      "eess.SY",
      "cs.LG",
      "cs.SY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation",
    "url": "https://arxiv.org/abs/2601.06473",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "2457c656-6d9c-420a-84c9-03d4a123375e",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}