Robust Features for Elbow Joint Angle Estimation Based on Electromyography

Triwiyanto, Triwiyanto; Wahyunggoro, Oyas; Nugroho, Hanung Adi; Herianto, -

doi:10.18517/ijaseit.8.5.6495

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…However, as an alternative to the pre-processing algorithms for contaminant type identification and interference removal, some researchers aim to propose techniques for EMG signal recovering (Machado et al 2019;De Moura & Balbinot 2018) and system robustness improvement to contaminants (McCool et al 2015;Teh & Hargrove 2021;Triwiyanto et al 2018). Machado et al (2019) and De Moura and Balbinot (2018) presented virtual sensor-based strategies to recover the information of a contaminated channel.…”

Section: Analysis Of the Presence Of Contaminants In The Semg Signalmentioning

confidence: 99%

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

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Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

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Section: Analysis Of the Presence Of Contaminants In The Semg Signalmentioning

confidence: 99%

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

View full text Add to dashboard Cite

show abstract

“…The accuracy obtained in predicting the angle is ranging from 10 to 30°. However, the weakness in the study is that the [53]. In addition to using the Hill muscle model [54]- [56] to predict arm joint angle or force, several other researchers used machine learning [57]- [60] for the development of upper limb exoskeleton devices.…”

Section: Introductionmentioning

confidence: 99%

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

Triwiyanto¹,

Caesarendra

Abdullayev

et al. 2023

Journal of Robotics and Control

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Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.

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Robust Features for Elbow Joint Angle Estimation Based on Electromyography

Cited by 2 publications

References 14 publications

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

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