scholarly journals Effect of user adaptation on prosthetic finger control with an intuitive myoelectric decoder

2019 ◽  
Author(s):  
Agamemnon Krasoulis ◽  
Sethu Vijayakumar ◽  
Kianoush Nazarpour
Keyword(s):  
Real Time ◽  
Position Control ◽  
Myoelectric Control ◽  
Continuous Control ◽  
Activation Patterns ◽  
User Adaptation ◽  
Short Term Adaptation ◽  
Prosthesis Control ◽  
Prosthetic Finger ◽  
Finger Control

ABSTRACTMachine learning-based myoelectric control is regarded as an intuitive paradigm, because of the mapping it creates between muscle co-activation patterns and prosthesis movements that aims to simulate the physiological pathways found in the human arm. Despite that, there has been evidence that closed-loop interaction with a classification-based interface results in user adaptation, which leads to performance improvement with experience. Recently, there has been a focus shift towards continuous prosthesis control, yet little is known about whether and how user adaptation affects myoelectric control performance in dexterous, intuitive tasks. We investigate the effect of short-term adaptation with independent finger position control by conducting real-time experiments with 10 able-bodied and two transradial amputee subjects. We demonstrate that despite using an intuitive decoder, experience leads to significant improvements in performance. We argue that this is due to the lack of an utterly natural control scheme, which is mainly caused by differences in the anatomy of human and artificial hands, movement intent decoding inaccuracies, and lack of proprioception. Finally, we extend previous work in classification-based and wrist continuous control by verifying that offline analyses cannot reliably predict real-time performance, thereby reiterating the importance of validating myoelectric control algorithms with real-time experiments.

scholarly journals Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

2020 ◽  
Author(s):  
Agamemnon Krasoulis ◽  
Kianoush Nazarpour
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Real Time Control ◽  
Artificial Joints ◽  
Time Control ◽  
Offline Analysis ◽  
Prosthesis Control ◽  
Multi Class Classification ◽  
Prosthetic Finger ◽  
Finger Control

ABSTRACTThe ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Although such methods have produced highly-accurate results in offline analyses, their success in real-time prosthesis control settings has been rather limited. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent decoding based on multi-label, multi-class classification. At each moment in time, our algorithm classifies movement action for each available DOF into one of three categories: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.

scholarly journals Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Agamemnon Krasoulis ◽  
Kianoush Nazarpour
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Real Time Control ◽  
Artificial Joints ◽  
Time Control ◽  
Offline Analysis ◽  
Intent Prediction ◽  
Multi Class Classification ◽  
Prosthetic Finger ◽  
Finger Control

Abstract The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Unfortunately, such methods have thus far met with limited success. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent prediction based on multi-output, multi-class classification. At each moment in time, our algorithm decodes movement intent for each available DOF into one of three classes: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.

Closed-loop control of a prosthetic finger via evoked proprioceptive information

2021 ◽  
Author(s):  
Luis Vargas ◽  
He (Helen) Huang ◽  
Yong Zhu ◽  
Xiaogang Hu
Keyword(s):  
Closed Loop ◽  
Position Control ◽  
Sensory Information ◽  
Proprioceptive Feedback ◽  
Closed Loop Control ◽  
Proprioceptive Information ◽  
Continuous Control ◽  
Velocity Control ◽  
Loop Control ◽  
Prosthetic Finger

Abstract Objective. Proprioceptive information plays an important role for recognizing and coordinating our limb’s static and dynamic states relative to our body or the environment. In this study, we determined how artificially evoked proprioceptive feedback affected the continuous control of a prosthetic finger. Approach. We elicited proprioceptive information regarding the joint static position and dynamic movement of a prosthetic finger via a vibrotactor array placed around the subject’s upper arm. Myoelectric signals of the finger flexor and extensor muscles were used to control the prosthesis, with or without the evoked proprioceptive feedback. Two control modes were evaluated: the myoelectric signal amplitudes were continuously mapped to either the position or the velocity of the prosthetic joint. Main Results. Our results showed that the evoked proprioceptive information improved the control accuracy of the joint angle, with comparable performance in the position- and velocity-control conditions. However, greater angle variability was prominent during position-control than velocity-control. Without the proprioceptive feedback, the position-control tended to show a smaller angle error than the velocity-control condition. Significance. Our findings suggest that closed-loop control of a prosthetic device can potentially be achieved using non-invasive evoked proprioceptive feedback delivered to intact participants. Moreover, the evoked sensory information was integrated during myoelectric control effectively for both control strategies. The outcomes can facilitate our understanding of the sensorimotor integration process during human-machine interactions, which can potentially promote fine control of prosthetic hands.

scholarly journals An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay

2020 ◽  
Author(s):  
Barbara Barros Carlos ◽  
Tommaso Sartor ◽  
Andrea Zanelli ◽  
Gianluca Frison ◽  
Wolfram Burgard ◽  
...  
Keyword(s):  
Time Delay ◽  
Real Time ◽  
Position Control ◽  
Communication Time

Real time ECG artifact removal for myoelectric prosthesis control

2007 ◽  
Vol 28 (4) ◽  
pp. 397-413 ◽  
Author(s):  
Ping Zhou ◽  
Blair Lock ◽  
Todd A Kuiken
Keyword(s):  
Real Time ◽  
Artifact Removal ◽  
Myoelectric Prosthesis ◽  
Prosthesis Control

scholarly journals Oxygen Monitoring Equipment for Sewage-Sludge Composting and Its Application to Aeration Optimization

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4017
Author(s):  
Guodi Zheng ◽  
Yuewei Wang ◽  
Xiankai Wang ◽  
Junxing Yang ◽  
Tongbin Chen
Keyword(s):  
Real Time ◽  
Oxygen Concentration ◽  
Consumption Rate ◽  
Continuous Control ◽  
Test Results ◽  
Monitoring Equipment ◽  
Decomposition Of Organic Matter ◽  
Good Repeatability ◽  
Oxygen Monitoring ◽  
Time Required

Oxygen is an important parameter for organic-waste composting, and continuous control of the oxygen in a composting pile may be beneficial. The oxygen consumption rate can be used to measure the degree of biological oxidation and decomposition of organic matter. However, without having a real-time online device to monitor oxygen levels in the composting pile, the adjustment and optimization of the composting process cannot be directly implemented. In the present study, we researched and developed such a system, and then tested its stability, reliability, and characteristics. The test results showed that the equipment was accurate and stable, and produced good responses with good repeatability. The equilibrium time required to detect oxygen concentration in the composting pile was 50 s, and the response time for oxygen detection was less than 2 s. The equipment could monitor oxygen concentration online and in real time to optimize the aeration strategy for the compost depending on the concentration indicated by the oxygen-measuring equipment.

Sign in / Sign up

Export Citation Format

Share Document