Currently the dataset can be downloaded under this link (2.2 GB, compressed tar file): The Muscles in Time dataset will be published under a CC BY -NC 4.0 license as defined under Our data generation pipeline is licensed under Apache License V ersion 2.0 as defined under Data structure The structure of the provided MinT data is intentionally kept simple. The first and last 0.14 seconds are cut off since the muscle activation A short example on the musint package usage is displayed in Listing 2. In Figure 9 we provide additional information on the data analyzed provided with Muscles in Time. Total Capture makes up a small part of the dataset with exceptionally long sequences. Dataset provides the largest contribution with 3.2h of analyzed recordings. The muscle-driven simulation, based on the approach by Falisse et al .

Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some internal biomechanical variables pose significant obstacles to accurate personalized modelling. Furthermore, muscle activation estimation can be challenging due to the inherent redundancy of the musculoskeletal system, where multiple muscles drive a single joint. This study develops a whole-body musculoskeletal model for strength and conditioning training and calibrates relevant muscle parameters with an electromyography-based optimization method. By utilizing the personalized musculoskeletal model, muscle activation can be subsequently estimated to analyze the performance of exercises. Bench press and deadlift are chosen for experimental verification to affirm the efficacy of this approach.