Neuro-motor controlled wearable augmentations: current research and emerging trends.

Wearable augmentations (WAs) designed for movement and manipulation, such as exoskeletons and supernumerary robotic limbs, are used to enhance the physical abilities of healthy individuals and substitute or restore lost functionality for impaired individuals. Non-invasive neuro-motor (NM) technologies, including electroencephalography (EEG) and sufrace electromyography (sEMG), promise direct and intuitive communication between the brain and the WA. After presenting a historical perspective, this review proposes a conceptual model for NM-controlled WAs, analyzes key design aspects, such as hardware design, mounting methods, control paradigms, and sensory feedback, that have direct implications on the user experience, and in the long term, on the embodiment of WAs.

Analyzing Arabic Handwriting Style through Hand Kinematics.

Handwriting style is an important aspect affecting the quality of handwriting. Adhering to one style is crucial for languages that follow cursive orthography and possess multiple handwriting styles, such as Arabic. The majority of available studies analyze Arabic handwriting style from static documents, focusing only on pure styles.

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.

Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications.

EEG correlates to perceived urgency elicited by vibration stimulation of the upper body.

Conveying information effectively while minimizing user distraction is critical to human-computer interaction. As the proliferation of audio-visual communication pushes human information processing capabilities to the limit, researchers are turning their attention to haptic interfaces. Haptic feedback has the potential to create a desirable sense of urgency that allows users to selectively focus on events/tasks or process presented information with minimal distraction or annoyance.

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.

. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals.

Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach.

Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts.

An ensemble deep-learning approach for single-trial EEG classification of vibration intensity.

. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedback. Convolutional neural networks (CNNs), which are among the most widely used deep learning techniques, have demonstrated their effectiveness in extracting EEG features for the classification of different cognitive functions, including the perception of vibration intensity that is often experienced during human-computer interaction.

EEG-Based Machine Learning Models to Evaluate Haptic Delay: Should We Label Data Based on Self-Reporting or Physical Stimulation?

Reliable haptic interfaces augment human-computer interaction via simulated tactile and kinesthetic feedback. As haptic technologies advance, user experience evaluation becomes more crucial. Conventionally, self-reporting is used to evaluate haptic experiences; however, it could be inconsistent or imprecise due to human error.

Assessment of EEG-based functional connectivity in response to haptic delay.

Haptic technologies enable users to physically interact with remote or virtual environments by applying force, vibration, or motion haptic interfaces. However, the delivery of timely haptic feedback remains a challenge due to the stringent computation and communication requirements associated with haptic data transfer. Haptic delay disrupts the realism of the user experience and interferes with the quality of interaction.

An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data.

Haptic technologies are becoming increasingly valuable in Human-Computer interaction systems as they provide means of physical interaction with a remote or virtual environment. One of the persistent challenges in tele-haptic systems, communicating haptic information over a computer network, is the synchrony of the delivered haptic information with the rest of the sensory modalities. Delayed haptic feedback can have serious implications on the user performance and overall experience.

Midfrontal theta power encodes the value of haptic delay.

The use of haptic technologies in modern life scenarios is becoming the new normal particularly in rehabilitation, medical training, and entertainment applications. An evident challenge in haptic telepresence systems is the delay in haptic information. How humans perceive delayed visual and audio information has been extensively studied, however, the same for haptically delayed environments remains largely unknown.

Midfrontal theta oscillation encodes haptic delay.

Haptic technologies aim to simulate tactile or kinesthetic interactions with a physical or virtual environment in order to enhance user experience and/or performance. However, due to stringent communication and computational needs, the user experience is influenced by delayed haptic feedback. While delayed feedback is well understood in the visual and auditory modalities, little research has systematically examined the neural correlates associated with delayed haptic feedback.

FaceGuard: A Wearable System To Avoid Face Touching.

Most people touch their faces unconsciously, for instance to scratch an itch or to rest one's chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one's face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour.

Screen Printed Passives and Interconnects on Bio-Degradable Medical Hydrocolloid Dressing for Wearable Sensors.

The healthcare system is undergoing a noticeable transformation from a reactive, post-disease treatment to a preventive, predictive continuous healthcare. The key enabler for such a system is a pervasive wearable platform. Several technologies have been suggested and implemented as a wearable platform, but these technologies either lack reliability, manufacturing practicability or pervasiveness.