The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method

Zhang, Yanyan; Wang, Gang; Teng, Chaolin; Sun, Zhongjiang; Wang, Jue

doi:10.1155/2014/781769

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…The acquired and filtered sEMG signals were decomposed using four-level decomposition [ 30 ] as described above. Having the highest signal frequency being 500 Hz and a total number of frequency bands of with , the lowest frequency range was 0–31.25 Hz.…”

Section: Methodsmentioning

confidence: 99%

Co-contraction characteristics of lumbar muscles in patients with lumbar disc herniation during different types of movement

Omisore

et al. 2018

BioMed Eng OnLine

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BackgroundMuscular performance is an important factor for the mechanical stability of lumbar spine in humans, in which, the co-contraction of lumbar muscles plays a key role. We hypothesized that when executing different daily living motions, the performance of the lumbar muscle co-contraction stabilization mechanism varies between patients with lumbar disc herniation (LDH) and healthy controls. Hence, in this study, co-contraction performance of lumbar muscles between patients with LDH and healthy subjects was explored to check if there are significant differences between the two groups when performing four representative movements.MethodsTwenty-six LDH patients (15 females, 11 males) and a control group of twenty-eight subjects (16 females, 12 males) were recruited. Surface electromyography (EMG) signals were recorded from the external oblique, lumbar multifidus, and internal oblique/transversus abdominis muscles during the execution of four types of movement, namely: forward bending, backward bending, left lateral flexion and right lateral flexion. The acquired EMG signals were segmented, and wavelet decomposition was performed followed by reconstruction of the low-frequency components of the signal. Then, the reconstructed signals were used for further analysis. Co-contraction ratio was employed to assess muscle coordination and compare it between the LDH patients and healthy controls. The corresponding signals of the subjects in the two groups were compared to evaluate the differences in agonistic and antagonistic muscle performance during the different motions. Also, sample entropy was applied to evaluate complexity changes in lumbar muscle recruitment during the movements.ResultsSignificant differences between the LDH and control groups were found in the studied situations (p < 0.05). During the four movements considered in this study, the participants of the LDH group exhibited a higher level of co-contraction ratio, lower agonistic, and higher antagonistic lumbar muscle activity (p < 0.01) than those of the control group. Furthermore, the co-contraction ratio of LDH patients was dominated by the antagonistic muscle activity during the movements, except for the forward bending motion. However, in the healthy control group, the agonistic muscle activity contributed more to the co-contraction ratio with an exception for the backward bending motion. Conversely, the sample entropy value was significantly lower for agonistic muscles of LDH group compared to the control group (p < 0.01) while the entropy value was significantly greater in antagonistic muscles (p < 0.01) during the four types of movement, respectively.ConclusionsLumbar disc herniation patients exhibited numerous variations in the evaluated parameters that reflect the co-contraction of lumbar muscles, the agonistic and antagonistic muscle activities, and their respective sample entropy values when compared with the healthy control group. These variations could be due to the compensation mechanism that was required to stabilize the spine. The resu...

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Section: Methodsmentioning

confidence: 99%

Co-contraction characteristics of lumbar muscles in patients with lumbar disc herniation during different types of movement

Omisore

et al. 2018

BioMed Eng OnLine

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“…Most of previous works in the field of control systems of prostheses focused on classifying hand movements during data acquisition [4], [5], [6], [7]. They shared the common approach to perform the classification task.…”

Section: Introductionmentioning

confidence: 99%

Early prediction of future hand movements using sEMG data

Koch

Phan

Maass

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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We study in this work the feasibility of early prediction of hand movement based on sEMG signals to overcome the time delay issue of the conventional classification. Opposed to the classification task, the objective of early prediction is to predict a hand movement that is going to occur in the future given the information up to the current time point. The ability of early prediction may allow a hand prosthesis control system to compensate for the time delay and, as a result, improve the usability. Experimental results on the Ninapro database show that we can predict up to 300 ms ahead in the future while the prediction accuracy remains very close to that of the standard classification, i.e. it is just marginally lower. Furthermore, historical data prior the current time window is shown to be very important to improve performance, not only for the prediction but also the classification task.

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“…The EEG in the eyes-closed and eyes-open state recorded on the position of the visual cortex was discriminated by the power in the alpha-band due to the alpha-band power obviously rose in the eyes-closed state [27]- [29]. X indicates the filtered EEG recorded with 3 electrodes (O1, O2, Oz) on the visual cortex [30].…”

Section: ) Detection Of Eyes Closed and Openmentioning

confidence: 99%

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

et al. 2020

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Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement. INDEX TERMS Quadcopter control system, motor imagery, steady-state visual evoked potential, off-line optimization, human-machine interaction.

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The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method

Cited by 11 publications

References 28 publications

Co-contraction characteristics of lumbar muscles in patients with lumbar disc herniation during different types of movement

Co-contraction characteristics of lumbar muscles in patients with lumbar disc herniation during different types of movement

Early prediction of future hand movements using sEMG data

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

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