RESUMEN

The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49% ± 3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82% ± 4.66% and 88.48% ± 5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.

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RESUMEN

In view of the importance of neck strength training and the lack of adequate training equipment, this study designed a new oscillating hydraulic trainer (OHT) of neck based on oscillating hydraulic damper. We used surface electromyography (sEMG) and subjective ratings to evaluate the neck OHT and compared the results with a simple hat trainer (HATT) and traditional weight trainer (TWT) to verify the feasibility and validity of the OHT. Under similar exercise conditions, 12 subjects performed a set of neck flexion and extension exercise with these 3 trainers. The sEMG signals of targeted muscles were collected in real time, and subjects were asked to complete subjective evaluations of product usability after exercise. The results showed that the root mean square (RMS%) of sEMG indicated that the OHT could provide two-way resistance and train the flexors and extensors simultaneously. The overall degree of muscle activation with OHT was higher than that with the other two trainers in one movement cycle. In terms of resistance characteristics exhibited by the sEMG waveform, duration (D) with OHT was significantly longer than HATT and TWT when exercising at a high speed, while Peak Timing (PT) was later. The ratings of product usability and performing usability of OHT were remarkably higher than that of HATT and TWT. Based on the above results, the OHT was proved to be more suitable for strength training, such as neck muscles, which were getting more attention gradually, but lacked mature and special training equipment.

RESUMEN

Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers' ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.

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