The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies.

Research into new solutions for wearable assistive devices for the visually impaired is an important area of assistive technology (AT). This plays a crucial role in improving the functionality and independence of the visually impaired, helping them to participate fully in their daily lives and in various community activities. This study presents a bibliometric analysis of the literature published over the last decade on wearable assistive devices for the visually impaired, retrieved from the Web of Science Core Collection (WoSCC) using CiteSpace, to provide an overview of the current state of research, trends, and hotspots in the field. The narrative focuses on prominent innovations in recent years related to wearable assistive devices for the visually impaired based on sensory substitution technology, describing the latest achievements in haptic and auditory feedback devices, the application of smart materials, and the growing concern about the conflicting interests of individuals and societal needs. It also summarises the current opportunities and challenges facing the field and discusses the following insights and trends: (1) optimization of the transmission of haptic and auditory information while multitasking; (2) advance research on smart materials and foster cross-disciplinary collaboration among experts; and (3) balance the interests of individuals and society. Given the two essential directions, the low-cost, stand-alone pursuit of efficiency and the high-cost pursuit of high-quality services that are closely integrated with accessible infrastructure, the latest advances will gradually allow more freedom for ambient assisted living by using robotics and automated machines, while using sensor and human–machine interaction as bridges to promote the synchronization of machine intelligence and human cognition.

As automated driving system (ADS) technology is adopted in wheelchairs, clarity on the vehicle’s imminent path becomes essential for both users and pedestrians. For users, understanding the imminent path helps mitigate anxiety and facilitates real-time adjustments. For pedestrians, this insight aids in predicting their next move when near the wheelchair. This study introduces an on-ground projection-based shared eHMI approach for autonomous wheelchairs. By visualizing imminent motion intentions on the ground by integrating real and virtual elements, the approach quickly clarifies wheelchair behaviors for all parties, promoting proactive measures to reduce collision risks and ensure smooth wheelchair driving. To explore the practical application of the shared eHMI, a user interface was designed and incorporated into an autonomous wheelchair simulation platform. An observation-based pilot study was conducted with both experienced wheelchair users and pedestrians using structured questionnaires to assess the usability, user experience, and social acceptance of this interaction. The results indicate that the proposed shared eHMI offers clearer motion intentions display and appeal, emphasizing its potential contribution to the field. Future work should focus on improving visibility, practicality, safety, and trust in autonomous wheelchair interactions.

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.

This work introduces a high-performance, quadruped-assistive-robot expandable platform with wheel–leg mode transformation functions. The robot platform is designed for transporting goods in residential areas such as apartments, private houses, and office buildings. It is capable to move fast on flat ground on wheels or use legs to move in other places, especially for moving on and off residential staircases and wheelchair accessible ramps. To achieve higher load capacity and combine the knee joint with the drive wheel, we designed a compact torso–leg structure, driving the lower link through a ligament-like structure. Because the distance between the wheel and the torso is short, the mass centroid drops and the force arm caused by the load is reduced; the designed sample robot is capable to transport uniform mass loads up to 15 kg while keeping it affordable. The proposed ligament-like transmission structure also ensures the torso’s even gesture and load capability in its walking mode. Gait motion planning, finite element analysis, and task-oriented simulation have been conducted to prove its applicability and feasibility when given a heavy load to transport across flat and staired scenarios.