Optimal design and control of a hand exoskeleton

Orlando, M. Felix; Akolkar, Himanshu; Dutta, Ashish; Saxena, Anupam; Behera, Laxmidhar

doi:10.1109/ramech.2010.5513211

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…However, for this study, the raw data from only 204 gradiometers were used before being band-passed into 3 frequency bands, i.e. alpha(α) band (8)(9)(10)(11)(12), beta(β) band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and alpha and beta combined (α + β) band (8-30 Hz) using a two pass Butterworth filter. Selecting 204 gradiometers provides a higher signal-tonoise ratio (SNR) as compared to 102 magnetometers, as gradiometers are more sensitive to changes in cortical activations.…”

Section: Data Processingmentioning

confidence: 99%

“…After the temporal filtering in mu( [8][9][10][11][12] Hz) and beta( [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz) bands and spatial filtering as shown in Figure 3, and finally taking the log variance using (8), we obtain the feature vector.…”

Section: Common Spatial Patterns (Csp)mentioning

confidence: 99%

“…Motor Imagery (MI) practice through brain-computer interface (BCI) has been found to be useful as a therapeutic substitute for standard rehabilitation practices for poststroke patients [1,2], and an alternative approach for interaction with the environment [3,4,5,6,7] for people with severe movement disability. For poststroke rehabilitation, the patient is required to voluntarily practice activities of daily living (ADL) with very high focus [2,8,9,10]. Advancements in BCI based technologies have shown promising results in terms of focused interaction for stroke patients [11].…”

Section: Introductionmentioning

confidence: 99%

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Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

et al. 2020

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Objective: Magnetoencephalography (MEG) based Brain-Computer Interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach: MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation (CC), ReliefF (RF), Random Forest (RandF), and Infinite Latent Feature Selection (ILFS) were applied across six binary tasks in three different frequency bands) was evaluated in this study on two state-ofthe-art features i.e. bandpower and CSP. Main results: All four methods provided a statistically significant increase in classification accuracy (CA) compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12Hz), beta (13-30Hz), or broadband (α+β) (8-30Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the number of channels significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15-105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the number of channels will help to decrease the computation cost and maintain numerical stability in cases of low trial numbers. Significance: The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.

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Section: Data Processingmentioning

confidence: 99%

Section: Common Spatial Patterns (Csp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

et al. 2020

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“…Human fingertip path during grasping and extension tasks is non-circular [34]. The simplest mechanism which generates this path is a planar four-bar linkage.…”

Section: E Dmentioning

confidence: 99%

Hand-Exoskeleton Assisted Progressive Neurorehabilitation Using Impedance Adaptation Based Challenge Level Adjustment Method

Chowdhury

Nishad

Meena

et al. 2019

IEEE Trans. Haptics

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This paper presents an underactuated design of a robotic hand exoskeleton, and a challenge based neurorehabilitation strategy. The exoskeleton is designed to reproduce natural human fingertip paths during extension and grasping, keeping minimal kinematic complexity. It facilitates an impedance adaptation based trigged assistance control strategy by switching between active non-assist and passive assistance modes. In the active non-assist mode, the exoskeleton motion follows the applied fingertip forces based on an impedance model. If the applied fingertip forces are inadequate, the passive assistance mode is triggered. The impedance parameters are updated at regular intervals based on the user performance, to implement a challenge based rehabilitation strategy. A six-week long hand therapy, conducted on four chronic stroke patients, resulted in significant (p-value<0.05) increase in force generation capacity and decrease (p-value<0.05) in the required assistance. Also, there was a significant (p-value<0.05) increase in the system impedance parameters which adequately challenged the patients. The change in the Action-Research-Arm-Test (ARAT) scores from baseline was also found to be significant (p-value<0.05) and beyond the minimal clinically important difference (MCID) limit. Thus the results prove that the proposed control strategy with has the potential to be a clinically effective solution for personalized rehabilitation of poststroke hand functionality.

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“…Algumas aplicações com AGs podem ser vistas no seguintes trabalhos: (Ren et al, 2008) e (Orlando et al, 2010) apresentam o desenvolvimento de AGs para realizar planejamento de trajetória de dispositivos como braços robóticos com diversas juntas. Os trabalhos (Jun e Qingbao, 2010) e (Jianguo et al, 2010) apresentam o desenvolvimento de AGs para realizar navegação robótica.…”

Section: Algoritmo 31 Algoritmo Genético Típicounclassified

Estratégias inteligentes aplicadas em robôs móveis autônomos e em coordenação de grupos de robôs

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Optimal design and control of a hand exoskeleton

Cited by 16 publications

References 11 publications

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

Hand-Exoskeleton Assisted Progressive Neurorehabilitation Using Impedance Adaptation Based Challenge Level Adjustment Method

Estratégias inteligentes aplicadas em robôs móveis autônomos e em coordenação de grupos de robôs

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