Yinfeng Fang

Bio: Yinfeng Fang is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Pattern recognition (psychology) & Computer science. The author has an hindex of 14, co-authored 44 publications receiving 705 citations. Previous affiliations of Yinfeng Fang include Zhejiang University of Technology & University of Portsmouth.

Multi-Modal Sensing Techniques for Interfacing Hand Prostheses: A Review

Abstract: This paper provides a comprehensive survey of current state of the bio-sensing technologies focusing on hand motion capturing and its application to interfacing hand prostheses. These sensing techniques include electromyography, sonomyography, mechnomyography, electroneurography, electroencephalograhy, electrocorticography, intracortical neural interfaces, near infrared spectroscopy, magnetoencephalography, and functional magnetic resonance imaging. Relevant approaches that interpret bio-signals in the view of prosthetic hand manipulation are discussed as well. Multi-modal sensory fusion provides a new strategy in this area, and the latest multi-modal sensing techniques are surveyed. This paper also outlines the new challenges and directions: 1) exploration of robust sensing technology; 2) multi-modal sensory fusion; 3) online signal processing and learning algorithms; and 4) bio-feedbacks.

How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder

Abstract: Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) ...

A multichannel surface EMG system for hand motion recognition

Abstract: Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

Non-Invasive Stimulation-Based Tactile Sensation for Upper-Extremity Prosthesis: A Review

Abstract: An ideal hand prosthesis should provide satisfying functionality based on reliable decoding of the user’s intentions and deliver tactile feedback in a natural manner. The absence of tactile feedback impedes the functionality and efficiency of dexterous hand prostheses, which leads to a high rejection rate from prostheses users. Thus, it is expected that integration of tactile feedback with hand prostheses will improve the manipulation performance and enhance perceptual embodiment for users. This paper reviews the state-of-the-art of non-invasive stimulation-based tactile sensation for upper-extremity prostheses, from the physiology of the human skin, to tactile sensing techniques, non-invasive tactile stimulation, and an emphasis on electrotactile feedback. The paper concludes with a detailed discussion of recent applications, challenging issues, and future developments.

Interface Prostheses With Classifier-Feedback-Based User Training

Abstract: It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.

Swarm Intelligence Algorithms for Feature Selection: A Review

Abstract: The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data To be able to learn from data, the dimensionality of the data should be reduced first Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories We propose a unified SI framework and use it to explain different approaches to FS Different methods, techniques, and their settings are explained, which have been used for various FS aspects The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem

Personalized machine learning for robot perception of affect and engagement in autism therapy

Abstract: Robots have the potential to facilitate future therapies for children on the autism spectrum. However, existing robots are limited in their ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Their inference challenge is made even harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in children with autism, we used the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children's affective states and engagement during robot-assisted autism therapy. Instead of using the traditional one-size-fits-all ML approach, we personalized our framework to each child using their contextual information (demographics and behavioral assessment scores) and individual characteristics. We evaluated this framework on a multimodal (audio, video, and autonomic physiology) data set of 35 children (ages 3 to 13) with autism, from two cultures (Asia and Europe), and achieved an average agreement (intraclass correlation) of ~60% with human experts in the estimation of affect and engagement, also outperforming nonpersonalized ML solutions. These results demonstrate the feasibility of robot perception of affect and engagement in children with autism and have implications for the design of future autism therapies.

Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring.

Abstract: Skin-interfaced medical devices are critically important for diagnosing disease, monitoring physiological health and establishing control interfaces with prosthetics, computer systems and wearable robotic devices. Skin-like epidermal electronic technologies can support these use cases in soft and ultrathin materials that conformally interface with the skin in a manner that is mechanically and thermally imperceptible. Nevertheless, schemes so far have limited the overall sizes of these devices to less than a few square centimetres. Here, we present materials, device structures, handling and mounting methods, and manufacturing approaches that enable epidermal electronic interfaces that are orders of magnitude larger than previously realized. As a proof-of-concept, we demonstrate devices for electrophysiological recordings that enable coverage of the full scalp and the full circumference of the forearm. Filamentary conductive architectures in open-network designs minimize radio frequency-induced eddy currents, forming the basis for structural and functional compatibility with magnetic resonance imaging. We demonstrate the use of the large-area interfaces for the multifunctional control of a transhumeral prosthesis by patients who have undergone targeted muscle-reinnervation surgery, in long-term electroencephalography, and in simultaneous electroencephalography and structural and functional magnetic resonance imaging.

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors.

Abstract: Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly ( p.