Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020

Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.

Brain Computer Interface-EEG based Imagined Word Prediction Using Convolutional Neural Network Visual Stimuli for Speech Disability

Brain Computer Interface (BCI) is one of the fast-growing technological trends, which finds its applications in the field of the healthcare sector. In this work, 16 electrodes of Electroencephalography (EEG) placed according to the 10-20 electrode system are used to acquire the EEG signals. A BCI with EEG based imagined word prediction using Convolutional Neural Network (CNN) is modeled and trained to recognize the words imagined through the EEG brain signal, where the CNN model Alexnet and Googlenet are able to recognize the words due to visual stimuli namely, up, down, right, left and up to ten words. The performance metrics are improved with the Morlet Continuous wavelet transform applied at the pre-processing stage, with seven extracted features such as mean, standard deviation, skewness, kurtosis, bandpower, root mean square, and Shannon entropy. Based on the testing, Alexnet transfer learning model performed better as compared to Googlenet transfer learning model, as it achieved an accuracy of 90.3%, recall, precision, and F1 score of 91.4%, 90%, and 90.7% respectively for seven extracted features. However, the performance metrics decreased when the number of extracted features was reduced from seven to four, to 83.8%, 84.4%, 82.9%, and 83.6% respectively. This high accuracy further paves the way to future work on cross participant analysis, plan to involve a larger number of participants for testing and to enhance the deep learning neural networks to create the system developed to be suitable for EEG based mobile applications, which helps to identify what the words are imagined to be uttered by the speech-disabled persons.

Secure Communication Scheme for Brain-Computer Interface Systems Based on High-Dimensional Hyperbolic Sine Chaotic System

Brain-Computer Interface (BCI) is a direct communication pathway between the brain and the external environment without using peripheral nerves and muscles. This emerging topic is suffering from serious issues such as malicious tampering and privacy leakage. To address this issue, we propose a novel communication scheme for BCI Systems. In particular, this scheme first utilizes high-dimensional chaotic systems with hyperbolic sine nonlinearity as the random number generator, then decorrelation operation is used to remove the physical characteristics of the output sequences. Finally, each of the sequences is applied in differential chaos shift keying (DCSK). Since each output sequence corresponds to a unique electrode, the communication data of different electrodes will not interfere with each other. Compared with popular multi-user DSCK schemes using Walsh code sequences, this scheme does not require the channel data of all electrodes while decoding. Therefore, this scheme has higher efficiency. Experimental results on communication data indicate that the proposed scheme can provide a high level of security.

Human Pointing Motion during Interaction with an Autonomous Blimp

We investigate the interaction between a human and a miniature autonomous blimp using a wand as pointing device. The wand movement generated by the human is followed by the blimp through a tracking controller.The Vector Integration to Endpoint (VITE) model, previously applied to human-computer interface (HCI), has been applied to model the human generated wand movement when interacting with the blimp. We show that the closed-loop human-blimp dynamics are exponentially stable. Similar to HCI using computer mouse, overshoot motion of the blimp has been observed. The VITE model can be viewed as a special reset controller used by the human to generate wand movements that effectively reduce the overshoot of blimp motion. Moreover, we have observed undershoot motion of the blimp due to its inertia, which does not appear in HCI using computer mouse. The asymptotic stability of the human-blimp dynamics is beneficial towards tolerating the undershoot motion of the blimp.

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.