Developing tactile feedback for wearable presentation

Abstract In two experiments with four and two groups of healthy subjects, a novel motor task, the voluntary abduction of the right big toe, was trained. This task cannot usually be performed without training and is therefore ideal for the study of elementary motor learning. A systematic variation of proprioceptive, tactile, visual, and EMG feedback was used. In addition to peripheral measurements such as the voluntary range of motion and EMG output during training, a three-channel EEG was recorded over Cz, C3, and C4. The movement-related brain potential during distinct periods of the training was analyzed as a central nervous parameter of the ongoing learning process. In experiment I, we randomized four groups of 12 subjects each (group P: proprioceptive feedback; group PT: proprioceptive and tactile feedback; group PTV: proprioceptive, tactile, and visual feedback; group PTEMG: proprioceptive, tactile, and EMG feedback). Best training results were reported from the PTEMG and PTV groups. The movement-preceding cortical activity, in the form of the amplitude of the readiness potential at the time of EMG onset, was greatest in these two groups. Results of experiment II revealed a similar effect, with a greater training success and a higher electrocortical activation under additional EMG feedback compared to proprioceptive feedback alone. Sensory EMG feedback as evaluated by peripheral and central nervous measurements appears to be useful in motor training and neuromuscular re-education.

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A touch screen interface design with tactile feedback for practical applications

International Journal of Space-Based and Situated Computing ◽

10.1504/ijssc.2013.051974 ◽

2013 ◽

Vol 3 (1) ◽

pp. 8 ◽

Cited By ~ 5

Author(s):

Hiroaki Nishino ◽

Ryotaro Goto ◽

Yuki Fukakusa ◽

Jiaqing Lin ◽

Tsuneo Kagawa ◽

...

Keyword(s):

Interface Design ◽

Touch Screen ◽

Tactile Feedback ◽

Practical Applications

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Changes in Sensorimotor Cortical Activation in Children Using Prostheses and Prosthetic Simulators

Brain Sciences ◽

10.3390/brainsci11080991 ◽

2021 ◽

Vol 11 (8) ◽

pp. 991

Author(s):

Christopher Copeland ◽

Mukul Mukherjee ◽

Yingying Wang ◽

Kaitlin Fraser ◽

Jorge M. Zuniga

Keyword(s):

Near Infrared ◽

Primary Motor Cortex ◽

Tactile Feedback ◽

Cortical Activation ◽

Typically Developing ◽

Neural Responses ◽

Motor Tasks ◽

Functional Near Infrared Spectroscopy ◽

Limb Reduction ◽

Motor Dexterity

This study aimed to examine the neural responses of children using prostheses and prosthetic simulators to better elucidate the emulation abilities of the simulators. We utilized functional near-infrared spectroscopy (fNIRS) to evaluate the neural response in five children with a congenital upper limb reduction (ULR) using a body-powered prosthesis to complete a 60 s gross motor dexterity task. The ULR group was matched with five typically developing children (TD) using their non-preferred hand and a prosthetic simulator on the same hand. The ULR group had lower activation within the primary motor cortex (M1) and supplementary motor area (SMA) compared to the TD group, but nonsignificant differences in the primary somatosensory area (S1). Compared to using their non-preferred hand, the TD group exhibited significantly higher action in S1 when using the simulator, but nonsignificant differences in M1 and SMA. The non-significant differences in S1 activation between groups and the increased activation evoked by the simulator’s use may suggest rapid changes in feedback prioritization during tool use. We suggest that prosthetic simulators may elicit increased reliance on proprioceptive and tactile feedback during motor tasks. This knowledge may help to develop future prosthesis rehabilitative training or the improvement of tool-based skills.

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Feedback Modalities in Brain–Computer Interfaces: A Systematic Review

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641283 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1186-1190

Author(s):

Wakana Ishihara ◽

Karen Moxon ◽

Sheryl Ehrman ◽

Mark Yarborough ◽

Tina L. Panontin ◽

...

Keyword(s):

Systematic Review ◽

Visual Feedback ◽

Auditory Feedback ◽

Brain Computer Interface ◽

Tactile Feedback ◽

Proprioceptive Feedback ◽

Computer Interface ◽

Computer Interfaces ◽

Feedback Modalities ◽

Improving Accuracy

This systematic review addresses the plausibility of using novel feedback modalities for brain–computer interface (BCI) and attempts to identify the best feedback modality on the basis of the effectiveness or learning rate. Out of the chosen studies, it was found that 100% of studies tested visual feedback, 31.6% tested auditory feedback, 57.9% tested tactile feedback, and 21.1% tested proprioceptive feedback. Visual feedback was included in every study design because it was intrinsic to the response of the task (e.g. seeing a cursor move). However, when used alone, it was not very effective at improving accuracy or learning. Proprioceptive feedback was most successful at increasing the effectiveness of motor imagery BCI tasks involving neuroprosthetics. The use of auditory and tactile feedback resulted in mixed results. The limitations of this current study and further study recommendations are discussed.

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Ergonomics analysis based on intention inference

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210191 ◽

2021 ◽

pp. 1-16

Author(s):

First A. Wenbo Huang ◽

Second B. Changyuan Wang ◽

Third C. Hongbo Jia

Keyword(s):

Eye Movement ◽

Recognition Rate ◽

Tactile Feedback ◽

Movement Direction ◽

Support Vector ◽

Eeg Signals ◽

Intention Recognition ◽

Baseline Removal ◽

Inference Methods ◽

Intention Inference

Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition.

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