A novel energy-motion model for continuous sEMG decoding: from muscle energy to motor pattern

10.36227/techrxiv.12248765.v2 ◽

2020 ◽

Author(s):

Gang Liu ◽

Lu Wang ◽

Jing Wang

Keyword(s):

Potential Energy ◽

Motor Pattern ◽

Training Data ◽

Hand Movements ◽

Coupling Mechanism ◽

Real Time Control ◽

Time Control ◽

Single Finger ◽

Energy Mode ◽

Semg Signals

Background: <a>At present, the gesture recognition using sEMG signals requires vast amounts of training data or limits to a few hand movements. This paper presents a novel dynamic energy model that can decode continuous hand actions with</a> force information, by training small amounts of sEMG data. Method: As activating the forearm muscles, the corresponding fingers are moving or tend to move (namely exerting force). The moving fingers store kinetic energy, and the fingers with moving trends store potential energy. The kinetic and potential energy of fingers is dynamically allocated due to the adaptive-coupling mechanism of five-fingers in actual motion. At this certain moment, the sum of the two energies is constant. We regarded energy mode with the same direction of acceleration of each finger, but likely different movements, as the same one, and divided hand movements into ten energy modes. Independent component analysis and machine learning methods were used to model associations between sEMG signals and energy mode, to determine the hand action, including speed and force adaptively. This theory imitates the self-adapting mechanism in the actual task; thus, ten healthy subjects were recruited, and three experiments mimicking activities of daily living were designed to evaluate the interface: (1) decoding untrained configurations, (2) decoding the amount of single-finger energy, and (3) real-time control. Results:(1) Participants completed the untrained hand movements (100 /100, p < 0.0001). (2) The test of pricking balloon with a needle tip was designed with significantly better than chance (779 /1000, p < 0.0001).(3) The test of punching a hole in the plasticine on the balloon was with over 95% success rate (97.67±5.04 %, p <0.01). Conclusion: The model can achieve continuous hand actions with force information, by training small amounts of sEMG data, which reduces trained complexity.

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A novel energy-motion model for continuous sEMG decoding: from muscle energy to motor pattern

10.36227/techrxiv.12248765.v1 ◽

2020 ◽

Author(s):

Gang Liu ◽

Lu Wang ◽

Jing Wang

Keyword(s):

Real Time ◽

Degrees Of Freedom ◽

Motor Pattern ◽

Human Hand ◽

Motion Model ◽

Real Time Control ◽

Prosthetic Hands ◽

Time Control ◽

Energy Mode ◽

Muscle Energy

Myoelectric prosthetic hands create the possibility for amputees to control their prosthetics like native hands. However, user acceptance of the extant myoelectric prostheses is low. Unnatural control, lack of sufficient feedback, and insufficient functionality are cited as primary reasons. Recently, although many multiple degrees-of-freedom (DOF) prosthetic hands and tactile-sensitive electronic skins have been developed, no non-invasive myoelectric interfaces can decode both forces and motions for five-fingers independently and simultaneously. This paper proposes a myoelectric interface based on energy allocation and fictitious forces hypothesis by mimicking the natural neuromuscular system. The energy-based interface uses a kind of continuous “energy mode” in the level of the entire hand. According to tasks itself, each energy mode can adaptively and simultaneously implement multiple hand motions and exerting continuous forces for a single finger. Also, a few learned energy modes could extend to the unlearned energy mode, highlighting the extensibility of this interface. We evaluate the proposed system through off-line analysis and operational experiments performed on the expression of the unlearned hand motions, the amount of finger energy, and real-time control. With active exploration, the participant was proficient at exerting just enough energy to five fingers on “fragile” or “heavy” objects independently, proportionally, and simultaneously in real-time. The main contribution of this paper is proposing the bionic energy-motion model of hand: decoding a few muscle-energy modes of the human hand (only ten modes in this paper) map massive tasks of bionic hand.

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Interactive Real-Time Control System for The Artificial Hand

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.16.1.8 ◽

2020 ◽

Vol 16 (1) ◽

pp. 1-10

Author(s):

Hanadi Jaber ◽

Mofeed rashid ◽

Luigi Fortuna

Keyword(s):

Real Time ◽

Artificial Limbs ◽

Svm Classifier ◽

Average Intensity ◽

Real Time Control ◽

Time Control ◽

Spatial Features ◽

Density Surface ◽

Artificial Hand ◽

Semg Signals

In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.

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THE INFLUENCE OF TRAINING DATA SELECTION ON PERFORMANCE OF NEURAL NETWORKS FOR CONTROL OF NON-LINEAR SYSTEMS**Presented at 1992 IFAC/IFIP/IMACS International Symposium on Artificial Intelligence in Real-Time Control, Delft, the Netherlands, June 16–18, 1992

Artificial Intelligence in Real-Time Control 1992 ◽

10.1016/b978-0-08-041898-8.50015-3 ◽

1993 ◽

pp. 55-60

Author(s):

A.B. Bendtsen ◽

Niels Jensen

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

The Netherlands ◽

International Symposium ◽

Training Data ◽

Data Selection ◽

Real Time Control ◽

Time Control ◽

Non Linear Systems ◽

Training Data Selection

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A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

Methods of Information in Medicine ◽

10.1055/s-0038-1634619 ◽

1995 ◽

Vol 34 (05) ◽

pp. 475-488

Author(s):

B. Seroussi ◽

J. F. Boisvieux ◽

V. Morice

Keyword(s):

Real Time ◽

Linear Time ◽

Treatment Plan ◽

Control Architecture ◽

Real Time Control ◽

Time Representation ◽

Time Control ◽

Continuous Support ◽

Definition Of ◽

Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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