[Retracted] Multimodal Sensor Motion Intention Recognition Based on Three‐Dimensional Convolutional Neural Network Algorithm

Abstract

With the development of microelectronic technology and computer systems, the research of motion intention recognition based on multimodal sensors has attracted the attention of the academic community. Deep learning and other nonlinear neural network models have a wide range of applications in big data sets. We propose a motion intention recognition algorithm based on multimodal long-term and short-term spatiotemporal feature fusion. We divide the target data into multiple segments and use a three-dimensional convolutional neural network to extract the short-term spatiotemporal features. The three types of features of the same segment are fused together and input into the LSTM network for time-series modeling to further fuse the features to obtain multimodal long-term spatiotemporal features with higher discrimination. According to the lower limb movement pattern recognition model, the minimum number of muscles and EMG signal characteristics required to accurately recognize the movement state of the lower limbs are determined. This minimizes the redundant calculation cost of the model and ensures the real-time output of the system results.

Construction of Curriculum Ideological and Political Collaborative Education Mechanism Based on Edge Computing and Neural Network Algorithm

“…The guiding position of the Marxist College has been established, and the secretary of the Party Branch of the Marxist College is appointed by the school's party committee standing member, party committee propaganda department head, and party committee, minister of the United Front Work Department concurrently. The Party Committee Propaganda Department is the organizational department of the school's ideological education management and theoretical study [ 21 ].…”

Integrating intention-based systems in human-robot interaction: a scoping review of sensors, algorithms, and trust

“…In Wen and Wang (2021) , a lower-limb motion intention recognition algorithm is proposed that utilizes multimodal long-term and short-term spatiotemporal feature fusion for accurate recognition. The input used for the algorithm is sEMG data, and the proposed algorithm consists of a 3D CNN for extracting short term spatiotemporal features in segments, Le Net and shape context to extract features of the target motion trajectory, and an LSTM network for time-series modeling of the extracted features.…”

“…The second most commonly used sensors are IMUs and sEMG sensors. IMUs are often found in exoskeleton designs and commercialized armbands, such as the Myo armband ( Ren et al, 2018 ; Cote-Allard et al, 2019 ; Su et al, 2019 ; Gardner et al, 2020 ; Viekash et al, 2021 ; Wang, 2021 ), while sEMG sensors are mainly used to measure upper-limb movements with armbands ( Cote-Allard et al, 2019 ; Young et al, 2019 ; Chen et al, 2020a ; Chen et al, 2020b ) and lower-limb movements with sEMG electrodes ( Kilic and Dogan, 2017 ; Kopke et al, 2020 ; Coker et al, 2021 ; Feleke et al, 2021 ; Viekash et al, 2021 ; Wen and Wang, 2021 ). IMUs, which measure body acceleration and angular rate, offer the advantage of being unobtrusive, portable, and relatively easy to use, making them ideal for real-time, dynamic motion tracking.…”

Retracted: Multimodal Sensor Motion Intention Recognition Based on Three‐Dimensional Convolutional Neural Network Algorithm

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”