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.