4,118 research outputs found
Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges
In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices
Rehabilitation Engineering
Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions
In the rapidly advancing field of multi-modal machine learning (MMML), the
convergence of multiple data modalities has the potential to reshape various
applications. This paper presents a comprehensive overview of the current
state, advancements, and challenges of MMML within the sphere of engineering
design. The review begins with a deep dive into five fundamental concepts of
MMML:multi-modal information representation, fusion, alignment, translation,
and co-learning. Following this, we explore the cutting-edge applications of
MMML, placing a particular emphasis on tasks pertinent to engineering design,
such as cross-modal synthesis, multi-modal prediction, and cross-modal
information retrieval. Through this comprehensive overview, we highlight the
inherent challenges in adopting MMML in engineering design, and proffer
potential directions for future research. To spur on the continued evolution of
MMML in engineering design, we advocate for concentrated efforts to construct
extensive multi-modal design datasets, develop effective data-driven MMML
techniques tailored to design applications, and enhance the scalability and
interpretability of MMML models. MMML models, as the next generation of
intelligent design tools, hold a promising future to impact how products are
designed
The development of fully automated RULA assessment system based on Computer Vision
The purpose of this study was to develop an automated, RULA-based posture assessment system using a deep learning algorithm to estimate RULA scores, including scores for wrist posture, based on images of workplace postures. The proposed posture estimation system reported a mean absolute error (MAE) of 2.86 on the validation dataset obtained by randomly splitting 20% of the original training dataset before data augmentation. The results of the proposed system were compared with those of two experts’ manual evaluation by computing the Intraclass correlation coefficient (ICC), which yielded index values greater than 0.75, thereby confirming good agreement between manual raters and the proposed system. This system will reduce the time required for postural evaluation while producing highly reliable RULA scores that are consistent with those generated by manual approach. Thus, we expect that this study will aid ergonomic experts in conducting RULA-based surveys of occupational postures in workplace conditions
Force-Aware Interface via Electromyography for Natural VR/AR Interaction
While tremendous advances in visual and auditory realism have been made for
virtual and augmented reality (VR/AR), introducing a plausible sense of
physicality into the virtual world remains challenging. Closing the gap between
real-world physicality and immersive virtual experience requires a closed
interaction loop: applying user-exerted physical forces to the virtual
environment and generating haptic sensations back to the users. However,
existing VR/AR solutions either completely ignore the force inputs from the
users or rely on obtrusive sensing devices that compromise user experience.
By identifying users' muscle activation patterns while engaging in VR/AR, we
design a learning-based neural interface for natural and intuitive force
inputs. Specifically, we show that lightweight electromyography sensors,
resting non-invasively on users' forearm skin, inform and establish a robust
understanding of their complex hand activities. Fuelled by a
neural-network-based model, our interface can decode finger-wise forces in
real-time with 3.3% mean error, and generalize to new users with little
calibration. Through an interactive psychophysical study, we show that human
perception of virtual objects' physical properties, such as stiffness, can be
significantly enhanced by our interface. We further demonstrate that our
interface enables ubiquitous control via finger tapping. Ultimately, we
envision our findings to push forward research towards more realistic
physicality in future VR/AR.Comment: ACM Transactions on Graphics (SIGGRAPH Asia 2022
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