People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people’s emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation.

/pdf/review-of-studies-on-emotion-recognition-and-judgment-based-itjryv2x.pdf

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

/pdf/hand-exoskeleton-design-and-human-machine-interaction-3jrdl2ko.pdf

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

Many forms of Intangible Culture Heritage (ICH) are fast disappearing. The Lantern Festival is an important part of Chinese tradition and a critical ICH of Taiwan. However, few young people attend ICH activities. Given the importance of ICH education, this study describes what participants learned after lantern exhibitions that combined Augmented Reality (AR), mobile websites, and Paper-based Exhibition Brochures. This study evaluated what a cohort of college students (n = 115) learned about ICH through experiments and questionnaires. The results show that the public understood ICH better through digital technology. All participants thought that applying AR to ICH was good for the public to gain more information on lantern culture, although it took a lot of time to learn the new technology. The results also encouraged the application of AR to engage in ICH education. Therefore, a large number of digital technologies that are popular among young people should be used as part of ICH education in order to improve their cultural awareness and learning motivation. 

Design and Evaluation for Improving Lantern Culture Learning Experience with Augmented Reality

Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights.

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for the measurement of electrical brain activity which is extensively used in some areas of clinical practice and psychological/psychiatric research, such as epilepsy, sleep, emotion, and brain computer interfaces. Although EEG sensor do not provide actual brain localizations of the activity sources, they allow to study brain functional connectivity. In this paper we review current application of a specific family of computational methods, the Graph Neural Networks (GNN) to the analysis of EEG data. GNNs appear to be well suited to EEG data modeling as they deal with signals whose domain is defined by a graph instead of a regular lattice in Euclidean space. Readings of EEG electrodes fall in this category, hence the increasing research activity on the application of GNNs to EEG data. 

A review of Graph Neural Networks for Electroencephalography data analysis

Abstract We developed an intelligent shadow play system (ShadowTouch) based on multi-dimensional interactive perception technologies to let user interact with the shadow play figure naturally. ShadowTouch included a shadow play hardware subsystem and a multi-dimensional interactive software subsystem. In shadow play hardware subsystem, each joint of the shadow play figure was actuated by the connecting rod which was rotated through one steering gear. In multi-dimensional interactive software subsystem, a somatosensory interaction module synchronized motions between shadow play figure and human body based on skeleton point detection, and an automatic choreography module generated the motions automatically for the shadow play figure based on musical emotion recognition. The proposed system improved the immersive interaction experience and let users feel involved during the performance of shadow play, and the motions of shadow play figure (steering gear motion angles) could be saved digitally to further be applied for the reactivation and inheritance of traditional culture.

An Intelligent Shadow Play System With Multi-dimensional Interactive Perception

Evaluation of the effect of music on idea generation using electrocardiography and electroencephalography signals

Motor disorder of upper limbs has seriously affected the daily life of the patients with hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) system using a hybrid control approach based on motor imagery (MI) and object detection for upper-limb motion assistance. SRL system included an SRL hardware subsystem and a hybrid control software subsystem. The system obtained the patient’s motion intention through MI electroencephalogram (EEG) recognition method based on graph convolutional network (GCN) and gated recurrent unit network (GRU) to control the left and right movements of SRL, and the object detection technology was used together for a quick grasp of target objects to compensate for the disadvantages when using MI EEG alone like fewer control instructions and lower control efficiency. Offline training experiment was designed to obtain subjects’ MI recognition models and evaluate the feasibility of the MI EEG recognition method; online control experiment was designed to verify the effectiveness of our wearable SRL system. The results showed that the proposed MI EEG recognition method (GCN+GRU) could effectively improve the MI classification accuracy (90.04% $\pm ~2.36$ %) compared with traditional methods; all subjects were able to complete the target object grasping tasks within 23 seconds by controlling the SRL, and the highest average grasping success rate achieved 90.67% in bag grasping task. The SRL system can effectively assist people with upper-limb motor disorder to perform upper-limb tasks in daily life by natural human-robot interaction, and improve their ability of self-help and enhance their confidence of life.

Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection

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Papers

An Intelligent Shadow Play System With Multi-dimensional Interactive Perception

Evaluation of the effect of music on idea generation using electrocardiography and electroencephalography signals

Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection