Robot control systems using bio-potential signals

Sasaki, Minoru; Matsushita, Kojiro; Rusyidi, Muhammad Ilhamdi; Laksono, Pringgo Widyo; Muguro, Joseph; Suhaimi, Muhammad Syaiful Amri bin; Njeri, Waweru

doi:10.1063/5.0000624

Cited by 7 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, to reduce the burden of not only long-term care recipients but also caregivers, support for the independence of long-term care recipients is required. The development of welfare robots to support individuals who have difficulty in daily living is attracting attention, especially among those needing longterm care [4]- [6]. Welfare robots are a prospective solution that seeks to restore individualism and self-reliance of the care-receivers through mobility, feeding, and environment control, among others.…”

Section: Introductionmentioning

confidence: 99%

Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave

Nakajima

Rusydi

Ramadhani

et al. 2022

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Welfare robots, as a category of robotics, seeks to improve the quality of life of the elderly and patients by availing a control mechanism to enable the participants to be self-dependent. This is achieved by using man-machine interfaces that manipulate certain external processes like feeding or communicating. This research aims to realize a man-machine interface using brainwave combined with object recognition applicable to patients with locked-in syndrome. The system utilizes a camera with pretrained object-detection system that recognizes the environment and displays the contents in an interface to solicit a choice using P300 signals. Being a camera-based system, field of view and luminance level were identified as possible influences. We designed six experiments by adapting the arrangement of stimuli (triangular or horizontal) and brightness/colour levels. The results showed that the horizontal arrangement had better accuracy than the triangular method. Further, colour was identified as a key parameter for the successful discrimination of target stimuli. From the paper, the precision of discrimination can be improved by adopting a harmonized arrangement and selecting the appropriate saturation/brightness of the interface.

show abstract

Section: Introductionmentioning

confidence: 99%

Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave

Nakajima

Rusydi

Ramadhani

et al. 2022

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…There are several areas of applications of EMG, such as disease diagnosis, rehabilitation evaluation, and control strategy for the assistive device. EMG provides rich information obtained by muscle contractions [1][2][3][4][5][6][7][8][9]. Recent research developments in the field of robots have led to robotic arm control with very complex mechanical capabilities, sensor technology, and control algorithms that do not necessarily make it easier for users to intuitively control robots [2][3][4]10].…”

Section: Introductionmentioning

confidence: 99%

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

et al. 2021

Self Cite

View full text Add to dashboard Cite

This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.

show abstract

“…The non-intrusive nature of sEMG and its ease of modulation contributes considerably to much of its usage in research. Integration of sEMG into different daily activities ranging from prosthetics [14], robotic control [15][16][17][18], wheelchair control [19,20], and more is also possible for this reason. In addition, EMG has been used as a serious game controlreferred to as exergaming [21][22][23]-and the general premise of using EMG-controlled games is that motor skills acquired through them will be translated into useful skills, such as rehabilitation or prosthetic controls.…”

Section: Introductionmentioning

confidence: 99%

Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

Muguro

Laksono

Rahmaniar

et al. 2021

Signals

Self Cite

View full text Add to dashboard Cite

In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities.

show abstract

Robot control systems using bio-potential signals

Cited by 7 publications

References 10 publications

Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave

Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

Contact Info

Product

Resources

About