A Biomechanical Model for the Development of Myoelectric Hand Prosthesis Control Systems

Advanced myoelectric hand prostheses aim to reproduce as much of the human hand's functionality as possible. Development of the control system of such a prosthesis is strongly connected to its mechanical design; the control system requires accurate information on the prosthesis' structure and the surrounding environment, which can make development difficult without a finalized mechanical prototype. This paper presents a new framework for the development of electromyographic hand control systems, consisting of a prosthesis model based on the biomechanical structure of the human hand. The model's dynamic structure uses an ellipsoidal representation of the phalanges. Other features include underactuation in the fingers and thumb modeled with bond graphs, and a viscoelastic contact model. The model's functions are demonstrated by the execution of lateral and tripod grasps, and evaluated with regard to joint dynamics and applied forces. Finally, additions are suggested with which this model can be of use in mechanical design and patient training as well

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Lubrication model of a knee prosthesis, with non newtonian fluid and porous rough material

Tibial component of knee prostheses, made of ultra high molecular weight polyethylene (UHMWPE), experiences a high degree of wear and may be expected to last twelve years on average. In this work, a steady state one-dimensional lubrication model of a knee prosthesis is solved through a nu-merical technique based on the Finite Element Method. The model takes into account a non Newto-nian synovial fluid, its ultra filtration mechanism and the surface roughness of a porous elastic layer on the tibial component. The benefits of a porous compliant material placed at the top of the metallic tibial component are shown taking into account the stiffness and exudation capacity of the material and hyaluronic acid concentration of synovial fluid.Fil: Berli, Marcelo Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Campana, Diego Martin. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Ubal, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Di Paolo, José. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentin

Temporal structure in neuronal activity during working memory in Macaque parietal cortex

A number of cortical structures are reported to have elevated single unit firing rates sustained throughout the memory period of a working memory task. How the nervous system forms and maintains these memories is unknown but reverberating neuronal network activity is thought to be important. We studied the temporal structure of single unit (SU) activity and simultaneously recorded local field potential (LFP) activity from area LIP in the inferior parietal lobe of two awake macaques during a memory-saccade task. Using multitaper techniques for spectral analysis, which play an important role in obtaining the present results, we find elevations in spectral power in a 50--90 Hz (gamma) frequency band during the memory period in both SU and LFP activity. The activity is tuned to the direction of the saccade providing evidence for temporal structure that codes for movement plans during working memory. We also find SU and LFP activity are coherent during the memory period in the 50--90 Hz gamma band and no consistent relation is present during simple fixation. Finally, we find organized LFP activity in a 15--25 Hz frequency band that may be related to movement execution and preparatory aspects of the task. Neuronal activity could be used to control a neural prosthesis but SU activity can be hard to isolate with cortical implants. As the LFP is easier to acquire than SU activity, our finding of rich temporal structure in LFP activity related to movement planning and execution may accelerate the development of this medical application.Comment: Originally submitted to the neuro-sys archive which was never publicly announced (was 0005002

Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function

Optimal Design and Control of a Lower-Limb Prosthesis with Energy Regeneration

The majority of amputations are of the lower limbs. This correlates to a particular need for lower-limb prostheses. Many common prosthesis designs are passive in nature, making them inefficient compared to the natural body. Recently as technology has progressed, interest in powered prostheses has expanded, seeking improved kinematics and kinetics for amputees. The current state of this art is described in this thesis, noting that most powered prosthesis designs do not consider integrating the knee and the ankle or energy exchange between these two joints. An energy regenerative, motorized prosthesis is proposed here to address this gap. After preliminary data processing is discussed, three steps toward the realization of such a system are completed. First, the design, optimization, and evaluation of a knee joint actuator are presented. The final result is found to be consistently capable of energy regeneration across a single stride simulation. Secondly, because of the need for a prosthesis simulation structure mimicking the human system, a novel ground contact model in two dimensions is proposed. The contact model is validated against human reference data. Lastly, within simulation a control method combining two previously published prosthesis controllers is designed, optimized, and evaluated. Accurate tracking across all joints and ground reaction forces are generated, and the knee joint is shown to have human-like energy absorption characteristics. The successful completion of these three steps contributes toward the realization of an optimal combined knee-ankle prosthesis with energy regeneratio