Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time

2021 ◽  
Author(s):  
Chen Chen ◽  
Yang Yu ◽  
Xinjun Sheng ◽  
Dario Farina ◽  
Xiangyang Zhu
Keyword(s):  
Real Time ◽  
Motor Unit ◽  
Hand Movements ◽  
Proportional Control

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

scholarly journals Online proprioception feeds plasticity of arm representation following tool-use in healthy aging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Salam Bahmad ◽  
Luke E. Miller ◽  
Minh Tu Pham ◽  
Richard Moreau ◽  
Romeo Salemme ◽  
...  
Keyword(s):  
Motor Control ◽  
Real Time ◽  
Tool Use ◽  
Healthy Aging ◽  
The Body ◽  
Hand Movements ◽  
Old Adults ◽  
Kinematic Pattern ◽  
Before And After ◽  
Proprioceptive Inputs

Abstract Following tool-use, the kinematics of free-hand movements are altered. This modified kinematic pattern has been taken as a behavioral hallmark of the modification induced by tool-use on the effector representation. Proprioceptive inputs appear central in updating the estimated effector state. Here we questioned whether online proprioceptive modality that is accessed in real time, or offline, memory-based, proprioception is responsible for this update. Since normal aging affects offline proprioception only, we examined a group of 60 year-old adults for proprioceptive acuity and movement’s kinematics when grasping an object before and after tool-use. As a control, participants performed the same movements with a weight—equivalent to the tool—weight-attached to their wrist. Despite hampered offline proprioceptive acuity, 60 year-old participants exhibited the typical kinematic signature of tool incorporation: Namely, the latency of transport components peaks was longer and their amplitude reduced after tool-use. Instead, we observed no kinematic modifications in the control condition. In addition, online proprioception acuity correlated with tool incorporation, as indexed by the amount of kinematics changes observed after tool-use. Altogether, these findings point to the prominent role played by online proprioception in updating the body estimate for the motor control of tools.

scholarly journals WIRELESS REAL TIME PROPORTIONAL CONTROL SYSTEM

2013 ◽  
pp. 15-20
Author(s):  
UJJWALA G. BORATE ◽  
PROF. R.T. PATIL
Keyword(s):  
Low Power ◽  
Real Time ◽  
Low Cost ◽  
Ultra Low Power ◽  
Proportional Control ◽  
Star Network ◽  
Precise Control ◽  
Zigbee Technology ◽  
Temperature And Humidity ◽  
Transceiver Module

This system provides low power consuming and low cost wireless sensor network. This system provides a real time temperature and humidity. It also gives proportional control action. This system consists of TI’s MSP430 microcontroller which consumes ultra low power and improves the overall system performance. The Sensorion’s SHT 11 sensor is used to measure temperature and humidity. Sensor SHT 11 consumes low power and gives the fully calibrated digital output. Zigbee technology is used for wireless communication. Zigbee is low power consuming transceiver module. It operates within the ISM 2.4 GHz frequency band. AT and API command modes configure module parameters. RF data rate is 250 kbps. To achieve the proportional control triac and MOC 3022 are used. The star network topology is implemented. The temperature of earth goes on increasing due to global warming, deforestation, pollution, etc. Due to this the temperature of atmosphere also increases which is harmful and dangerous for many systems. This system provides precise control of temperature and humidity in Green House, Art Galleries and Industries.

Gesture Recognition by Analyzing Muscle Activity

2014 ◽  
Vol 618 ◽  
pp. 410-414
Author(s):  
Aleksey Levanov ◽  
Alexander Alfimcev
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Gesture Recognition ◽  
Muscle Activity ◽  
Simple Game ◽  
Point Of View ◽  
Hand Movements ◽  
Software System ◽  
Practical Point ◽  
Multiple User

By analyzing muscle activity with the BioPlux device multiple user gestures can be recognized. Gestures are independently selected from each other. Relevance of the topic from the practical point of view is determined by the need to create a software system that can use sign language interface in real time. The main objective of this study is to create a simple game that can be controlled by using different hand movements.

Robotic training for hand movements during manual welding with real-time alarm feedback

2015 ◽  
Vol 42 (6) ◽  
pp. 554-564
Author(s):  
Mustafa Suphi Erden ◽  
Aude Billard
Keyword(s):  
Real Time ◽  
Smooth Curve ◽  
Work Load ◽  
Training System ◽  
Hand Movements ◽  
Robotic Training ◽  
Content Type ◽  
Position Data ◽  
Flashing Light ◽  
Tool Position

Purpose – The purpose of this study is to develop a robotic training system for the hand movements during manual welding. The system provides real-time notice-feedback with sound or light alarms, whenever the welding hand vibrates beyond the nominal level observed with professional welders. Design/methodology/approach – The large variations of hand movements are detected by monitoring the deviation of the tool position from a smooth curve estimated in real time by a Kalman filter. An alarm is generated in the form of a flashing light or beep sound whenever the deviations exceed a predetermined threshold. The performance of hand movements is measured in terms of the variations of the position data. Twelve novice and five professional welders took part in the experiments and answered a questionnaire that assessed the usability and work load of the system. Findings – Compared to the sound alarms, the light alarms resulted in a larger and statistically significant decrease in the variation of hand movements of the novice welders and brought the level of variation close to that of the professional welders. The alarms did not result in a significant decrease in the variation of hand movements of the professional welders. The responses to the questionnaire indicated that both professional and novice welders found the system useful and they did not experience any significant work load. Social implications – The system developed in this study can ease the training of novice welders, by speeding up the learning and reducing the need for human tutors. Originality/value – This study is first to provide real-time notice-feedback for training while manual welding, based on a comparison of the performances of novice and professional welders.

scholarly journals Real-time visuomotor behavior and electrophysiology recording setup for use with humans and monkeys

2018 ◽  
Vol 120 (2) ◽  
pp. 539-552 ◽  
Author(s):  
Marcel Jan de Haan ◽  
Thomas Brochier ◽  
Sonja Grün ◽  
Alexa Riehle ◽  
Frédéric V. Barthélemy
Keyword(s):  
Real Time ◽  
Reference Frame ◽  
Large Scale ◽  
Visual Stimuli ◽  
Vertical Plane ◽  
Calibration Method ◽  
Hand Movements ◽  
Eye Tracker ◽  
Visuomotor Behavior ◽  
Hand Coordination

Large-scale network dynamics in multiple visuomotor areas is of great interest in the study of eye-hand coordination in both human and monkey. To explore this, it is essential to develop a setup that allows for precise tracking of eye and hand movements. It is desirable that it is able to generate mechanical or visual perturbations of hand trajectories so that eye-hand coordination can be studied in a variety of conditions. There are simple solutions that satisfy these requirements for hand movements performed in the horizontal plane while visual stimuli and hand feedback are presented in the vertical plane. However, this spatial dissociation requires cognitive rules for eye-hand coordination different from eye-hand movements performed in the same space, as is the case in most natural conditions. Here we present an innovative solution for the precise tracking of eye and hand movements in a single reference frame. Importantly, our solution allows behavioral explorations under normal and perturbed conditions in both humans and monkeys. It is based on the integration of two noninvasive commercially available systems to achieve online control and synchronous recording of eye (EyeLink) and hand (KINARM) positions during interactive visuomotor tasks. We also present an eye calibration method compatible with different eye trackers that compensates for nonlinearities caused by the system's geometry. Our setup monitors the two effectors in real time with high spatial and temporal resolution and simultaneously outputs behavioral and neuronal data to an external data acquisition system using a common data format. NEW & NOTEWORTHY We developed a new setup for studying eye-hand coordination in humans and monkeys that monitors the two effectors in real time in a common reference frame. Our eye calibration method allows us to track gaze positions relative to visual stimuli presented in the horizontal workspace of the hand movements. This method compensates for nonlinearities caused by the system’s geometry and transforms kinematics signals from the eye tracker into the same coordinate system as hand and targets.

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