Biologically Inspired Modelling for the Control of Upper Limb Movements: From Concept Studies to Future Applications

Modelling is continuously being deployed to gain knowledge on the mechanisms of motor control. Computational models, simulating the behaviour of complex systems, have often been used in combination with soft computing strategies, thus shifting the rationale of modelling from the description of a behaviour to the understanding of the mechanisms behind it. In this context, computational models are preferred to deterministic schemes because they deal better with complex systems. The literature offers some striking examples of biologically inspired modelling, which perform better than traditional approaches when dealing with both learning and adaptivity mechanisms. Can these theoretical studies be transferred into an application framework? That is, can biologically inspired models be used to implement rehabilitative devices? Some evidences, even if preliminary, are presented here, and support an affirmative answer to the previous question, thus opening new perspectives

MEASURING REGULARITY OF FINE UPPER LIMB MOVEMENTS WITH A HAPTIC PLATFORM FOR MOTOR LEARNING AND REHABILITATION

Robot-assisted systems for arm training are being increasingly used to target moderate-to-severe upper limb impairments in rehabilitation facilities, while hand fine motor skills are seldom being targeted by these machines. This manuscript describes and tests the feasibility of a system based on a haptic interface aimed to complement the efficacy of robotic training in the rehabilitation and motor learning associated with upper extremities movements. End-effector kinematics associated with different trajectory tasks performed by 11 healthy adults were used to extract measures of smoothness, under different testing conditions that included the presence or absence of visual and haptic feedback, the use of dominant vs. non dominant hand, different shapes (crosses and circles), and the verse with which movements were done. The normalized mean square jerk, extracted from the system together with specific speed parameters, was able to capture differences in regularity between the different shapes (MSJratio significantly higher when drawing crosses, p < 1.0 E-4), and that haptic feedback significantly influences this smoothness measure (MSJratio significantly higher when haptic feedback is present, p < 5.0 E-4). The proposed system may be used as a means to monitor the progress of movement regularity in robot-mediated therapy, and the results obtained experimentally highlight the influence of haptic feedback on the smoothness of finalized upper extremity fine movements

MEASURING REGULARITY OF FINE UPPER LIMB MOVEMENTS WITH A HAPTIC PLATFORM FOR MOTOR LEARNING AND REHABILITATION

Robot-assisted systems for arm training are being increasingly used to target moderate-to-severe upper limb impairments in rehabilitation facilities, while hand fine motor skills are seldom being targeted by these machines. This manuscript describes and tests the feasibility of a system based on a haptic interface aimed to complement the efficacy of robotic training in the rehabilitation and motor learning associated with upper extremities movements. End-effector kinematics associated with different trajectory tasks performed by 11 healthy adults were used to extract measures of smoothness, under different testing conditions that included the presence or absence of visual and haptic feedback, the use of dominant vs. non dominant hand, different shapes (crosses and circles), and the verse with which movements were done. The normalized mean square jerk, extracted from the system together with specific speed parameters, was able to capture differences in regularity between the different shapes (MSJratio significantly higher when drawing crosses, p < 1.0 E-4), and that haptic feedback significantly influences this smoothness measure (MSJratio significantly higher when haptic feedback is present, p < 5.0 E-4). The proposed system may be used as a means to monitor the progress of movement regularity in robot-mediated therapy, and the results obtained experimentally highlight the influence of haptic feedback on the smoothness of finalized upper extremity fine movements

USING PVDF FILMS AS FLEXIBLE PIEZOELECTRIC GENERATORS FOR BIOMECHANICAL ENERGY HARVESTING

In this paper, a commercial polymeric piezoelectric film, the polyvinylidene fluoride (PVDF) was used to harvest electrical energy during the execution of five locomotion activities (walking, going down and up the stairs, jogging and running). The PVDF film transducer was placed into a tight suit in proximity of four body joints (shoulder, elbow, knee and ankle). The RMS values of the power output measured during the five activities were in the range 0.1 – 10 µW depending on the position of the film transducer on the body. This amount of electrical power allows increasing the operation time of wearable systems, and it may be used to prolong the monitoring of human vital signals for personalized health, wellness, and safety applications

Real time event-based segmentation to classify locomotion activities through a single inertial sensor

We propose an event-based dynamic segmentation technique for the classification of locomotion activities, able to detect the mid-swing, initial contact and end contact events. This technique is based on the use of a shank-mounted inertial sensor incorporating a tri-axial accelerometer and a tri-axial gyroscope, and it is tested on four different locomotion activities: walking, stair ascent, stair descent and running. Gyroscope data along one component are used to dynamically determine the window size for segmentation, and a number of features are then extracted from these segments. The event-based segmentation technique has been compared against three different fixed window size segmentations, in terms of classification accuracy on two different datasets, and with two different feature sets. The dynamic event-based segmentation showed an improvement in terms of accuracy of around 5% (97% vs. 92% and 92% vs. 87%) and 1-2% (89% vs. 87% and 97% vs. 96%) for the two dataset, respectively, thus confirming the need to incorporate an event-based criterion to increase performance in the classification of motion activities

A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

A biologically inspired neural network controller for ballistic arm movements

<p>Abstract</p> <p>Background</p> <p>In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.</p> <p>Methods</p> <p>The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans.</p> <p>Results</p> <p>The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians.</p> <p>Curvature values are similar to those encountered in experimental measures with humans.</p> <p>The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector.</p> <p>Conclusion</p> <p>The proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.</p