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A large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such invariant features have been observed at different levels of organization in the motor system, including the electromyographic, kinematic, and kinetic levels, and are thought to reflect fixed modules-named motor primitives-that the brain uses to simplify the construction of movement. However, motor primitives across space, time, and organization levels are often described with ad-hoc mathematical models that tend to be domain-specific. This, in turn, generates the need to use model-specific algorithms for the identification of both the motor primitives and additional model parameters. The lack of a comprehensive framework complicates the comparison and interpretation of the results obtained across different domains and studies. In this work, we take the first steps toward addressing these issues, by introducing a unifying framework for the modeling and identification of qualitatively different classes of motor primitives. Specifically, we show that a single model, the anechoic mixture model, subsumes many popular classes of motor primitive models. Moreover, we exploit the flexibility of the anechoic mixture model to develop a new class of identification algorithms based on the Fourier-based Anechoic Demixing Algorithm (FADA). We validate our framework by identifying eight qualitatively different classes of motor primitives from both simulated and experimental data. We show that, compared to established model-specific algorithms for the identification of motor primitives, our flexible framework reaches overall comparable and sometimes superior reconstruction performance. The identification framework is publicly released as a MATLAB toolbox (FADA-T, https://tinyurl.com/compsens) to facilitate the identification and comparison of different motor primitive models.

State-of-the-art motorized hand prostheses are endowed with actuators able to provide independent and proportional control of as many as six degrees of freedom (DOFs). The control signals are derived from residual electromyographic (EMG) activity, recorded concurrently from relevant forearm muscles. Nevertheless, the functional mapping between forearm EMG activity and hand kinematics is only known with limited accuracy. Therefore, no robust method exists for the reliable computation of control signals for the independent and proportional actuation of more than two DOFs. A common approach to deal with this limitation is to preprogram the prostheses for the execution of a restricted number of behaviors (e.g., pinching, grasping, and wrist rotation) that are activated by the detection of specific EMG activation patterns. However, this approach severely limits the range of activities users can perform with the prostheses during their daily living. In this work, we introduce a novel method, based on a long short-term memory (LSTM) network, to map forearm EMG activity onto hand kinematics online. Critically, unlike previous research efforts that tend to focus on simple and highly controlled motor tasks, we tested our method on a dataset of daily living activities (ADLs): the KIN-MUS UJI dataset. To the best of our knowledge, ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Remarkably, we show that our network is able to generalize to novel untrained ADLs. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.