The Aerial Guide Dog: A Low-Cognitive-Load Indoor Electronic Travel Aid for Visually Impaired Individuals

Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study proposes a cognitive burden-free electronic travel aid for individuals with visual impairments. Utilizing human instinctive compliance in response to external force, we introduce the "Aerial Guide Dog", a helium balloon aerostat drone designed for indoor guidance, which leverages gentle tugs in real time for directional guidance, ensuring a seamless and intuitive guiding experience. The introduced Aerial Guide Dog has been evaluated in terms of directional guidance and path following in the pilot study, focusing on assessing its accuracy in orientation and the overall performance in navigation. Preliminary results show that the Aerial Guide Dog, utilizing Ultra-Wideband (UWB) spatial positioning and Measurement Unit (IMU) angle sensors, consistently maintained minimal deviation from the targeting direction and designated path, while imposing negligible cognitive burdens on users while completing the guidance tasks.

Biomechanical Consequences of Using Passive and Active Back-Support Exoskeletons during Different Manual Handling Tasks

The aim of this study was to assess, for both men and women, the consequences of using different back-support exoskeletons during various manual material tasks (MMH) on the activity of back muscles and trunk kinematics. Fifteen men and fourteen women performed MMH involving a 15 kg load (a static task, a symmetric lifting task, and an asymmetric lifting task). Four exoskeleton conditions were tested: without equipment (CON) and with three exoskeletons passive (P-EXO), and active (A-EXO1 and A-EXO2)). The electromyographic activity of the lower trapezius (TZ), latissimus dorsi (LD), erector spinae (ES), gluteus maximus (GM), and biceps femoris (BF) muscles was recorded. Trunk kinematics were evaluated to provide average thoracic, lumbar, and hip angles. The use of the P-EXO decreased the activity of LD, GM, and BF from -12 to -27% (p < 0.01) compared to CON, mostly during the static task. The A-EXO1 and A-EXO2 reduced the muscle activity of all studied muscles from -7 to -62% (p < 0.01) compared to CON and from -10 to -52% (p < 0.005) compared to the P-EXO, independently of the modalities of the experimental tasks. A statistical interaction between the sex and exoskeleton was only observed in a few rare conditions. Occupational back-support exoskeletons can reduce trunk extensor muscle activity compared to no equipment being used. However, these reductions were modulated by the exoskeleton technology (passive vs. active), design (weight and anthropomorphism), and the modalities of the task performed (static vs. dynamic). Our results also showed that the active exoskeletons could modify the trunk kinematics.

A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users' LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices' control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.