{'fraction_non_alphanumeric': 0.01588628762541806, 'fraction_numerical': 0.0, 'mean_word_length': 5.3, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Helping hand: an anatomically accurate inverse dynamics solution for unconstrained hand motion

We present a realistic skeletal musculo-tendon model of the human hand and forearm. The model permits direct forward dynamics simulation, which accurately predicts hand and finger position given a set of muscle activations. We also present a solution to the inverse problem of determining an optimal set of muscle activations to achieve a given pose or motion; muscle fatigue, injury or atrophy can also be specified, yielding different control solutions that favour healthy muscle. As there can be many (or no) solutions to this inverse problem, we demonstrate how the space of possible solutions can be filtered to an optimal representative. Of particular note is the ability of our model to take a wide array of joint interdependence into account for both forward and inverse problems. Given kinematic postures, the model can be used to validate, predict or fill in missing motion and improve coarsely specified motion with anatomic fidelity. Lastly, we address the visualization and understanding of the dynamically changing and spatially compact musculature using various interaction techniques.