A Multimodal Dataset for Clinical Gait Analysis compromising Optical Motion Capture and Textual Gait Analyses from...

We present a multimodal dataset¹ for automated gait analysis in patients undergoing Total Knee Arthroplasty (TKA). The dataset combines quantitative motion capture data with expert clinical gait assessments in textual form. The dataset includes recordings from 23 patients during a standardized six-minute walking test (6MWT), a clinical protocol used to assess walking endurance and functional mobility. Of these patients, 15 were recorded both pre-operatively and six weeks post-operatively, while the remaining patients were recorded only pre-operatively. Motion data were acquired using a markerless SIMI Motion system and are provided as 3D keypoint trajectories and joint angle time series. In addition, parametric body representations based on the Skinned Multi-Person Linear (SMPL) model were obtained through a separate post-processing pipeline applied to the multi-view recordings. For each recording session, three to six clinical gait assessments were obtained through structured interviews with physiotherapists. These annotations describe gait characteristics, deviations, and compensatory mechanisms. The dataset enables research on linking biomechanical motion data with clinical descriptions, supporting applications such as automated gait assessment, anomaly detection, and analysis of early post-operative recovery.