Dataset for Rapid Estimation of Femoral Neck Loading During Gait and Dynamic Exercises

Medical intervention related to musculoskeletal and orthopaedic conditions often requires an understanding of the loads experienced at the joints (e.g., ankle, knee, or hip). 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 3D inertial measurement signals (IMU), joint kinematics and kinetics from musculoskeletal modelling in OpenSim, and tensile and compressive femoral neck strains from finite element analysis. Broadly, this dataset enables applications in biomechanics, rehabilitation, and clinical research, supporting movement analysis, assistive device design, and musculoskeletal disorder assessment. The multimodal signals also advance fracture risk prediction, wearable sensing, and personalized medicine. Overall, this dataset supports medical research and clinical interventions to enhance bone health, reduce fracture risk, and accelerate the rehabilitation process.

与肌肉骨骼及骨科病症相关的医疗干预，往往需要明确关节（如踝关节、膝关节或髋关节）所承受的载荷情况。各类运动类型与强度水平均可改变关节载荷，这对与活动能力、平衡状态、脆性骨折风险及术后康复（如全髋关节置换术后）相关的临床干预具有重要应用价值。当前，公开可用的、覆盖步态与动态运动（如跑步、跳跃与单腿跳）中不同强度下骨骼载荷的数据集仍较为匮乏。为加深对基于载荷的物理治疗的认知，本研究发布了一套数据集，包含40名健康受试者完成三种强度（高、中、低）下的行走、跑步、反向跳、深蹲跳、单侧跳与双侧跳动作的相关数据。该数据集涵盖三维惯性测量单元（Inertial Measurement Unit, IMU）信号、来自OpenSim肌肉骨骼建模的关节运动学与动力学数据，以及通过有限元分析得到的股骨颈拉伸与压缩应变数据。总体而言，该数据集可应用于生物力学、康复医学与临床研究领域，为运动分析、辅助设备设计及肌肉骨骼疾病评估提供支撑。其多模态信号还可推动骨折风险预测、可穿戴传感技术与个性化医疗的发展。综上，本数据集可为旨在提升骨骼健康水平、降低骨折风险并加速康复进程的医学研究与临床干预提供有力支持。