Two Methods of EMG Analysis for the Purpose of Exoskeletons and Robotic Rehabilitation Devices

Zielińska, Teresa; Wang, Jikun

doi:10.1007/978-3-319-78963-7_15

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Those patterns are used for producing the motor control commands [28,21]. Therefore, the system is capable to predict the next required position and drives accordingly the actuators [34,36,6]. It is possible under the assumption that the user is able to produce clear (not blurred) EMG signals.…”

Section: ǯɩ äƌǜˁřǜǔɂȭ řȭƞ ȧɂǜǔɂȭ ƿƭȭƭʁřǜǔɂȭƕ ǜǒƭ ȧřǔȭ ǌƭřǜˁʁƭʊ ǯɩɍɩ ăǜřǜƭ ɂǌ ǜǒƭ řʁǜmentioning

confidence: 99%

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The Research on Exoskeletons with Focus on the Locomotion Support

Wang¹,

Lyu²

2020

PAR

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ŎƬ˘˹ŘǚŘ ʊǔǄ ȭŘ ǘɁʁ˘˿ʊǜŘȭǔƬ ˘ Řʁǜ˿ǘˁȦˁ ȭŘ ˹ŘʁˁȭǘŘƋǒ ǚǔƋƬȭƋǖǔ ,ʁƬŘǜǔ˸Ƭ ,ɁȧȧɁȭʊ Ę˘ȭŘȭǔƬ ŘˁǜɁʁʊǜ˹Ř ʯƖ1 . IntroductionThe concept of an artificial exoskeleton is inspired by biology. Many living creatures possess external skeletons covering their soft bodies, the classic example are insects. Animal exoskeletons are not only introducing the rigidity of the body but make the safe covering of soft organs, moreover through reconfiguration of the rigid segments, the reshaping of the body, that supports the locomotion, is obtained. Interesting example are the worms covered by segmented shell which can take a ball like form for rolling down the inclined surfaces (see Fig. 1)The human exoskeletons we are not yet reached such advanced stage however those devices have quite long research history, and the recent challenges in this field are impressing. The study on human exoskeletons started in 19 th century. The first exoskeleton-like devices were invented by Nicholas Yagin [26] (1890). These devices offered for the wearer the faster running and higher jumping ability. The era of modern exoskeletons started in late seventies of 20 th century together with the progress in digital control systems and sensing technologies [25].Exoskeletons are regarded as a wearable robots or a wearable intelligent devices. They can be categorized as a full-body, upper-body and lower-body supporters. The exoskeletons are used in rehabilitation, they prevent of motion system injuries, they increase the power or enhance the human motion abilities. Moreover, they are used as intelligent prosthesis for amputees, ÄˁǜɁʁ ǘɁʁƬʊɡɁȭƞˁǖŭƋ˿ƕ ǔǘˁȭ ĶŘȭǷƙ ǖǔǘˁȭƖ˹ŘȭǷźɡ˹ƖƬƞˁƖɡǚ Äʁǜ˿ǘˁȦ ʁƬƋƬȭ˘Ɂ˹Řȭ˿ ȭŘƞƬʊȦŘȭ˿ ɍʯƖ̋ʹƖʹ̋ʹ̋ ʁƖƙ ɡʁ˘˿ǖǄǜ˿ ƞɁ ƞʁˁǘˁ ʹƶƖ̋ǮƖʹ̋ʹ̋ ʁƖ ďǒƬ õƬʊƬŘʁƋǒ Ɂȭ F˾ɁʊǘƬǚƬǜɁȭʊ ˹ǔǜǒ hɁƋˁʊ Ɂȭ ǜǒƬ ³ɁƋɁȧɁǜǔɁȭ ăˁɡɡɁʁǜ Jikun Wang ĶŘʁʊŘ˹ Ęȭǔ˸Ƭʁʊǔǜ˿ Ɂǌ ďƬƋǒȭɁǚɁǷ˿ƙ hŘƋˁǚǜ˿ Ɂǌ ÀƬƋǒŘǜʁɁȭǔƋʊƙ ȭʊǜǔǜˁǜƬ Ɂǌ ÄˁǜɁȧŘǜǔƋ ,ɁȭǜʁɁǚ Řȭƞ õɁŽɁǜǔƋʊƙ ʋ˹Ɩ Äȭƞʁ˘ƬǖŘ *ɁŽɁǚǔ ƶƙ ̋ʹȊǣʹǣ ĶŘʁʊŘ˹ƙ ñɁǚŘȭƞ Linwei Lyu ďǔŘȭǖǔȭ Ęȭǔ˸Ƭʁʊǔǜ˿ Ɂǌ ďƬƋǒȭɁǚɁǷ˿ƙ ÂŘǜǔɁȭŘǚ <ƬȧɁȭʊǜʁŘǜǔɁȭ ,ƬȭǜƬʁ ǌɁʁ F˾ɡƬʁǔȧƬȭǜŘǚ ÀƬƋǒŘȭǔƋŘǚ Řȭƞ FǚƬƋǜʁǔƋŘǚ FȭǷǔȭƬƬʁǔȭǷ FƞˁƋŘǜǔɁȭƙ ďǔŘȭǖǔȭ °Ƭ˿ ǚŘŽɁʁŘǜɁʁ˿ ǌɁʁ Äƞ˸ŘȭƋƬƞ ÀƬƋǒŘǜʁɁȭǔƋ ă˿ʊǜƬȧ <ƬʊǔǷȭ Řȭƞ ȭǜƬǚǚǔǷƬȭǜ ,ɁȭǜʁɁǚƙ ďǔŘȭǖǔȭ ʯ̋̋ʯƶǮƙ ,ǒǔȭŘ

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Section: ǯɩ äƌǜˁřǜǔɂȭ řȭƞ ȧɂǜǔɂȭ ƿƭȭƭʁřǜǔɂȭƕ ǜǒƭ ȧřǔȭ ǌƭřǜˁʁƭʊ ǯɩɍɩ ăǜřǜƭ ɂǌ ǜǒƭ řʁǜmentioning

confidence: 99%

“…Such gait is also used for detecting the motion impairments and the recovery processes. The details of the methods are given in [36].…”

Section: ǯɩʹɩ Fà ʊǔƿȭřǚʊ řȭřǚ˿ʊǔʊ ǌɂʁ Fà žřʊƭƞ ƌɂȭǜʁɂǚƕ ʁƭʊƭřʁƌǒ ƭ˾řȧɡǚƭmentioning

confidence: 99%

The Research on Exoskeletons with Focus on the Locomotion Support

Wang¹,

Lyu²

2020

PAR

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“…This is carried out using advanced biosignal processing and pattern recognition techniques. 16 Artificial neural networks (ANNs) are mainly used for processing biosignals and for complex hand gesture recognition and classification tasks. 17,18 The ability to learn and the ability to replicate random non-linear functions of input and extremely regularize the structure of ANNs make them suitable for pattern recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

Vasanthi

Jayasree

2020

Proc Inst Mech Eng H

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The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.

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Uses of artificial intelligence and robotics in healthcare systems

Mushiri

Moyo

2023

Healthcare Systems Design of Intelligent Testing Centers

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Two Methods of EMG Analysis for the Purpose of Exoskeletons and Robotic Rehabilitation Devices

Cited by 3 publications

References 7 publications

The Research on Exoskeletons with Focus on the Locomotion Support

The Research on Exoskeletons with Focus on the Locomotion Support

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

Uses of artificial intelligence and robotics in healthcare systems

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