A quantitative taxonomy of human hand grasps.

BACKGROUND: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. METHODS: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. RESULTS: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. CONCLUSIONS: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields.

Modeling the human knee for assistive technologies.

In this paper, we use motion capture technology together with an EMG-driven musculoskeletal model of the knee joint to predict muscle behavior during human dynamic movements. We propose a muscle model based on infinitely stiff tendons and show this allows speeding up 250 times the computation of muscle force and the resulting joint moment calculation with no loss of accuracy with respect to the previously developed elastic-tendon model. We then integrate our previously developed method for the estimation of 3-D musculotendon kinematics in the proposed EMG-driven model. This new code enabled the creation of a standalone EMG-driven model that was implemented and run on an embedded system for applications in assistive technologies such as myoelectrically controlled prostheses and orthoses.

A neuromusculoskeletal model of the human lower limb: towards EMG-driven actuation of multiple joints in powered orthoses.

This paper presents a novel neuromusculoskeletal (NMS) model of the human lower limb that uses the electromyo-graphic (EMG) signals from 16 muscles to estimate forces generated by 34 musculotendon actuators and the resulting joint moments at the hip, knee and ankle joints during varied contractile conditions. Our proposed methodology allows overcoming limitations on force computation shown by currently available NMS models, which constrain the operation of muscles to satisfy joint moments about one single degree of freedom (DOF) only (i.e. knee flexion-extension). The design of advanced human machine interfaces can benefit from the application of our proposed multi-DOF NMS model. The better estimates of the human internal state it provides with respect to single-DOF NMS models, will allow designing more intuitive human-machine interfaces for the simultaneous EMG-driven actuation of multiple joints in lower limb powered orthoses.