Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

Song, Jun‐Yeob; Zhu, Aibin; Tu, Yao; Huang, Hu; Arif, Muhammad Affan; Shen, Zhitao; Zhang, Xiaodong; Cao, Guangzhong

doi:10.3390/app10103358

Cited by 23 publications

(19 citation statements)

References 17 publications

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“…Different from the structure of a single neural network layer, there are four interaction layers in LSTM, which interact in a very special way. LSTM is an artificial RNN architecture for deep learning [ 26 ].…”

Section: Construction Of Prediction Models and Scheme Designmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

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Section: Construction Of Prediction Models and Scheme Designmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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“…When the number of elements in sampling time sequence S is greater than that in template time sequence T in the database, the linear reduction method is used, for example, when T = [1, 3, 5] and S = [0.8, 1.4, 3.2, 3.9, 5.1], then S becomes [0.8, 3.2, 5.1] after being processed by the linear reduction method; when the number of elements in the sampling sequence S is less than the number of elements in the template sequence T in the database, the Lagrangian linear interpolation method is used to expand the sampling sequence, as shown in Eqs. ( 10)- (12). The performance of processing with the Lagrange-LR algorithm is shown in Fig.…”

Section: K)clear() K++ Endmentioning

confidence: 99%

“…(11) Song et al studied the impact of surface electromyography (sEMG) signals on the accuracy of human motion pattern recognition, and used their designed multilayer perceptron and long short-term memory (LSTM) neural network to identify seven common human motion modes in daily life. (12) The use of EMG can overcome the space limitation problem caused by image technology. However, the installation of commercial EMG equipment and the attachment of the equipment are complicated, and the user is susceptible to sweat.…”

Section: Introductionmentioning

confidence: 99%

Human Motion Mode Recognition Based on Multi-parameter Fusion of Wearable Inertial Module Unit and Flexible Pressure Sensor

Zuyi¹,

Zheng²,

Liu³

2022

Sensors and Materials

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Aiming at the rapid recognition of human motion modes required in the intelligent control algorithm of exoskeleton robots, in this paper, on the basis of the characteristics of inertial data and pressure data collected by smart terminals carried by pedestrians, a dynamic block matching algorithm based on kinematics (DBMK) using motion mode recognition is proposed. This algorithm involves signal extraction and motion feature matching discrimination. More specifically, it first uses the method of periodic signal capture in adaptive motion mode to capture the heel touch event from the signal collected by a flexible pressure sensor mounted on the heel, and extracts the corresponding periodic signal. Finally, the DBMK algorithm uses a self-made lower limb motion information acquisition system to obtain human motion angle data. After kinematics preprocessing, the distance correlation coefficient based on Pearson weight proposed in this paper is used to identify the current human motion model category. The DBMK algorithm was used to identify five common human motion modes from the output data of inertial module units and flexible pressure sensors, and experimental results show that the proposed DBMK algorithm has an accuracy of 90.86% for the recognition of the five common motion modes.

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“…The simplest approach is On-Off Control, which relies on pattern recognition to separate human intention into a several classes [106], [107]. The predefined trajectory is replayed when a class is detected.…”

Section: F Semg-based Controlmentioning

confidence: 99%

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Cao²,

Zhu

2021

IEEE Access

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Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.

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Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

Cited by 23 publications

References 17 publications

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Human Motion Mode Recognition Based on Multi-parameter Fusion of Wearable Inertial Module Unit and Flexible Pressure Sensor

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

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