A Research about the Mental Fatigue of using an Intelligent Artificial Limb based on Functional Near Infrared Spectrum Technique

Zhang, Yong; Yang, Junyou; Bai, Ding; Wang, Yina

doi:10.1109/iisr.2018.8535608

Cited by 2 publications

(3 citation statements)

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“…This phenomenon directly reflects the huge fluctuation of the HbO and HbR. The classification accuracy was significantly improved when real mobility-impaired patients employed behavior transfer [30,33]. To the best of our knowledge, this is the first study to investigate the highest classification performance based on statistical feature combinations applied to the feedback of a fatigue analysis in MRS.…”

Section: Discussionmentioning

confidence: 92%

“…The healthy subjects a, b, and c imitate people with a weak motion capability; however, they have the physical strength and confidence to complete the transfer independently. Thus, there is a smaller difference than for subjects d and e between the two types of difficulty-level behaviors at the beginning stage [33][34][35][36]. Especially for subject e, the weak physical condition makes it extremely difficult for him to complete the autonomous behavior transfer.…”

Section: The Validation Of Mental Fatigue Modelmentioning

confidence: 94%

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Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

et al. 2020

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The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user’s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS.

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Section: Discussionmentioning

confidence: 92%

Section: The Validation Of Mental Fatigue Modelmentioning

confidence: 94%

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

et al. 2020

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“…The fatigue can induce many severe problems, such as signal quality declining, recognition ability deterioration and even risk of photosensitive epileptic seizures [118], pushing SSVEPbased BCI systems to higher development [119]. Zhang et al [120] studied how much metal fatigue subjects have through the change of oxygen saturation obtained by near-infrared spectrum approach when they use an intelligent artificial limb. Some researches [61], [121] attempted to reduce subjects fatigue by employing visual stimuli in higher frequencies, however, they cannot be adaptive according to the state of mental fatigue.…”

Section: Mental Fatiguementioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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A Research about the Mental Fatigue of using an Intelligent Artificial Limb based on Functional Near Infrared Spectrum Technique

Cited by 2 publications

References 1 publication

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

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