A dataset of human hand kinematics and forearm muscle activation collected during the performance of a wide variety of activities of daily living (ADLs) is presented, with tagged characteristics of products and tasks. A total of 26 participants performed 161 ADLs, selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices). The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. The products and their arrangement were the same across subjects, and tasks were performed in a guided way. Data of more than 4100 ADLs is presented in this dataset as Matlab structures with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour. The dataset is accompanied with a custom data visualization application (ERGOMOVMUS) as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or subjects’ characteristics. v2.1 includes the following updates: - Statistical summary of the recordings both in .xlsx and .ods file format (v1.1 only included it in .xlsx file format) - Updated experiment details in "MOVMUS-UJI DATASET GUIDE.pdf".

This research was part of the project PGC2018-095606-B-C21, funded by MCIN/AEI/10.13039/ 501100011033 and "ERDF A way of making Europe".