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<p>Medical intervention related to musculoskeletal (MSK) and orthopaedic conditions often requires an understanding of the loads experienced at the joints. A wide spectrum of exercise types and intensity levels can alter joint loading which is useful for clinical interventions related to mobility, balance, risk of fragility fracture, and post-surgical recovery (e.g., total hip arthroplasty). Currently, there is a lack of publicly available datasets on bone loading across varied intensities during gait and dynamic exercises (e.g., running, jumping, and hopping). In order to broaden the understanding of load-based physical therapy, we present a dataset comprising 40 healthy participants performing walking, running, countermovement jumps, squat jumps, unilateral hopping, and bilateral hopping across three intensity levels (high, moderate, and low). This dataset includes inertial measurement signals (IMU), joint kinematics and kinetics from MSK modelling in OpenSim, and tensile and compressive femoral neck strains from finite element analysis. Overall, the multimodal signals in this dataset support medical research and clinical interventions to enhance bone health, reduce fracture risk, and accelerate the rehabilitation process.</p>

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Dataset for rapid estimation of femoral neck loading during gait and dynamic exercises

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

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  • David Hollinger,
  • Zainab Altai,
  • Jason Moran,
  • Xiaojun Zhai,
  • Andrew Phillips,
  • Qichang Mei,
  • Bernard X. W. Liew

摘要

Medical intervention related to musculoskeletal (MSK) and orthopaedic conditions often requires an understanding of the loads experienced at the joints. A wide spectrum of exercise types and intensity levels can alter joint loading which is useful for clinical interventions related to mobility, balance, risk of fragility fracture, and post-surgical recovery (e.g., total hip arthroplasty). Currently, there is a lack of publicly available datasets on bone loading across varied intensities during gait and dynamic exercises (e.g., running, jumping, and hopping). In order to broaden the understanding of load-based physical therapy, we present a dataset comprising 40 healthy participants performing walking, running, countermovement jumps, squat jumps, unilateral hopping, and bilateral hopping across three intensity levels (high, moderate, and low). This dataset includes inertial measurement signals (IMU), joint kinematics and kinetics from MSK modelling in OpenSim, and tensile and compressive femoral neck strains from finite element analysis. Overall, the multimodal signals in this dataset support medical research and clinical interventions to enhance bone health, reduce fracture risk, and accelerate the rehabilitation process.