An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation.

OBJECTIVE: A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom. METHODS: We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection. RESULTS: The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor. CONCLUSION: The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics. SIGNIFICANCE: Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

STIMGRASP: A Home-Based Functional Electrical Stimulator for Grasp Restoration in Daily Activities.

Thousands of people currently suffer from motor limitations caused by SCI and strokes, which impose personal and social challenges. These individuals may have a satisfactory recovery by applying functional electrical stimulation that enables the artificial restoration of grasping after a muscular conditioning period. This paper presents the STIMGRASP, a home-based functional electrical stimulator to be used as an assistive technology for users with tetraplegia or hemiplegia. The STIMGRASP is a microcontrolled stimulator with eight multiplexed and independent symmetric biphasic constant current output channels with USB and Bluetooth communication. The system generates pulses with frequency, width, and maximum amplitude set at 20 Hz, 300 µs/phase, and 40 mA (load of 1 kΩ), respectively. It is powered by a rechargeable lithium-ion battery of 3100 mAh, allowing more than 10 h of continuous use. The development of this system focused on portability, usability, and wearability, resulting in portable hardware with user-friendly mobile app control and an orthosis with electrodes, allowing the user to carry out muscle activation sequences for four grasp modes to use for achieving daily activities.

A Hybrid 3D Printed Hand Prosthesis Prototype Based on sEMG and a Fully Embedded Computer Vision System.

This study presents a new approach for an sEMG hand prosthesis based on a 3D printed model with a fully embedded computer vision (CV) system in a hybrid version. A modified 5-layer Smaller Visual Geometry Group (VGG) convolutional neural network (CNN), running on a Raspberry Pi 3 microcomputer connected to a webcam, recognizes the shape of daily use objects, and defines the pattern of the prosthetic grasp/gesture among five classes: Palmar Neutral, Palmar Pronated, Tripod Pinch, Key Grasp, and Index Finger Extension. Using the Myoware board and a finite state machine, the user's intention, depicted by a myoelectric signal, starts the process, photographing the object, proceeding to the grasp/gesture classification, and commands the prosthetic motors to execute the movements. Keras software was used as an application programming interface and TensorFlow as numerical computing software. The proposed system obtained 99% accuracy, 97% sensitivity, and 99% specificity, showing that the CV system is a promising technology to assist the definition of the grasp pattern in prosthetic devices.

Selection of suitable hand gestures for reliable myoelectric human computer interface.

BACKGROUND: Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. METHODS: Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. RESULTS: When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion. CONCLUSION: This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.

sEMG feature evaluation for identification of elbow angle resolution in graded arm movement.

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

Sistema de monitorizaçäo da força de preensäo visando a reabilitaçäo dos membros superiores de tetraplégicos / Monitoring system of grasp force aiming the tetraplegic upper limb rehabilitation

O presente trabalho propõe um sistema para a monitoração da força de preensão durante a manipulação de objetos, parâmetro este de grande importância para o controle artificial dos movimentos da mão de tetraplégicos. O transdutor, propriamente dito, corresponde a uma luva de lycra dotada de sensores de força licalizados em pontos estratégicos, constituindo uma alternativa prática e de baixo custo, que viabiliza o seu uso cotidiano. O sistema foi aplicado em 30 sujeitos normais, visando a avaliação do dispositivo como fornecedor do feedback de força bem como a caracterização da função normal, apresentando-se como uma alternativa promissora para aplicações clínicas.

Sistema de monitorizaçäo da posiçäo dos dedos durante a manipulaçäo de objetos visando o controle do movimento de preensäo de tetraplégicos / Monitoring system of finger position during object manipulation aiming the control of tetraplegic grasp movement

Para o controle artificial da preensão de tetraplégicos, um dos parâmetros relevantes para a caracterização do movimento é a posição dos dedos em cada instante. Propõe-se no presente trabalho a implementação de um sistema capaz de monitorar este parâmetro durante a manipulação de objetos. O transdutor, propriamente dito, corresponde a uma luva de lycra dotada de um sensor de deslocamento localizado sobre a articulação de interesse, constituindo uma alternativa prática e de baixo custo. Os testes preliminares de calibração mostraram a potencialidade do transdutor como fornecedor do "feedback" de posição mas, também, a necessidade de otimização do sistema visando sua aplicação clínica

Abstract - Finger position is a very important parameter for artificial grasp control of a tetraplegic. This work suggests a system for monitoring this parameter during object manipulation. The transducer is composed by a Jycra glove with a displacement sensor attached to the joint position. lt is a low cost system, being easy to use and cosmetically acceptable. Preliminary tests have shown the transducer potential as a position feedback supplier, but also the need for design improvements towards clinicai application