Andreas, DWerner, DHou, ZDwivedi, AnanyCastellini, CBeckerle, P2026-06-232026-06-232025-08-28Andreas, D., Werner, D., Hou, Z., Dwivedi, A., Castellini, C., & Beckerle, P. (2025). A database of multimodal myography and hand kinematics during realistic daily life activities. Scientific Data, 12(1). https://doi.org/10.1038/s41597-025-05852-62052-4463https://hdl.handle.net/10289/18403This paper introduces MyoKi, a database capturing multimodal myography and hand kinematics during various realistic daily life activities. MyoKi emphasizes the complexity of real-world settings, addressing limitations of existing databases, which often reflect controlled laboratory conditions. The database includes two subsets of participants designed to evaluate different sensor configurations. Both subsets contain surface electromyography (sEMG) and inertial measurement unit (IMU) data, along with hand kinematics covering 18 finger and wrist joints. For the second subset, additional force myography (FMG) data was collected. The database captures hand movements of 35 participants performing 74 tasks, with varying arm orientations and movements involving different grips and motions. By offering detailed participant profiles and systematically categorizing each task, the MyoKi database enables in-depth exploration of task complexity, sensor influence, and the impact of demographic and anthropometric factors on control system performance. The database is designed to facilitate research in continuous hand control, enhancing the robustness and reliability of myoelectric devices for daily activities, moving towards user-friendly and effective control of robotic and prosthetic hands.enLicence for published version: Creative Commons Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/computer scienceJournal Article10.1038/s41597-025-05852-62052-44634605 Data Management and Data Science46 Information and Computing Sciences40 Engineering