Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer’s surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as “acoustic touch” to provide a wearable spatial audio solution for assisting people who are blind in finding objects. In contrast to traditional systems, this technique uses smart glasses to sonify objects into distinct sound auditory icons when the object enters the device’s field of view. We developed a wearable Foveated Audio Device to study the efficacy and usability of using acoustic touch to search, memorize, and reach items. Our evaluation study involved 14 participants, 7 blind or low-visioned and 7 blindfolded sighted (as a control group) participants. We compared the wearable device to two idealized conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user’s cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.

Navigation is a coordinated and goal-oriented movement through the environment, in which vision is an integral part of acquiring spatial information. People with blindness and vision impairment rely on alternative senses to navigate in daily life. Auditory cues are critical in building a spatial representation and effective navigation. Our work investigates the neurophysiological response to explore how auditory cues can shape a person's spatial representation and memory of an environment. In our experiment, a person who is blind was presented with verbal and non-verbal cues, either at close-reaching distance or globally. Electroencephalography, walking trajectories and tactile drawing data were collected to evaluate spatial representation and memory formed by different auditory cues and the role of theta oscillations in such relationships. Our results indicated a consistent increase in theta power at turning and starting points, a higher cognitive load with verbal strategies, and enhanced path recollection with global strategies. These findings emphasise the role of theta as a key indicator of cognitive workload and spatial memory processing. The preliminary results contribute to a broader understanding of the underlying processing mechanism of auditory information processing to support spatial navigation.

Active navigation is essential in everyday life and refers to the combination of cognition (spatial mapping, path planning, and decision making) and motor-sensory execution (moving and sensing environment). For people who are blind or have low-vision, auditory and tactile sensory augmentation is critical to active navigation. In assistive technologies, binaural spatial audio rendering is widely adopted. However, the most effective methods to support fluent spatial navigation are still being studied. For example, in a previous study, we demonstrated the feasibility of using spatialized earcons to support a shorelining task. In this work, we use the same shorelining task to explore various forms of spatial earcon presentation with a focus on standardization and effectiveness. We also explore the development of an intuitive auditory grammar for spatial and contextual cues. We conduct psychophysical experiments and present experimental measures such as performance time and accuracy, heart-rate variability, and the NASA task load index.

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.