Robot-supported interventions for joint attention (JA) in autistic children have shown encouraging outcomes, yet most remain confined to stationary robots in indoor settings, limiting opportunities for skill generalization and broader developmental benefits. We introduce an intervention that employs a quadruped robot dog as a peer-like partner for JA training across both indoor and outdoor environments. In this intervention, the robot dog directs children’s attention to distributed targets in the environment and initiates JA trials. A four-week pre-post exploratory study with six autistic children demonstrated improvements in JA performance and indications of transfer to daily social communication. Spontaneous behaviors such as motor imitation (crawling) and novel social interactions with the robot also emerged, suggesting potential for broader developmental gains. These findings provide initial evidence for the efficacy of mobile robot-supported JA interventions in naturalistic contexts and offer implications for future design.

The rapid growth of IoT applications has driven the demand for high-accuracy indoor localization. However, existing mainstream solutions have distinct limitations: while sensors such as vision, inertial, and LiDAR provide high-precision relative motion tracking, their inherent cumulative drift compromises long-term robustness; meanwhile, high-precision technologies like UWB rely on expensive, dedicated infrastructure deployment. The emergence of 5G networks offers a unique solution by utilizing its existing communication base stations as positioning anchors. Leveraging its wideband signals, 5G has the potential to provide the absolute position references needed to correct relative errors without significant additional hardware costs, thereby overcoming the limitations of the aforementioned techniques. However, 5G-based positioning technologies face challenges, such as multipath interference, clock synchronization errors, and degraded performance during terminal mobility. To address these limitations, we propose a novel, tightly-coupled 5G Positioning Reference Signal (PRS) and Inertial Measurement Unit (IMU) fusion framework. The core of our approach is a two-stage architecture. First, a front-end signal error estimator models the joint estimation of clock offset and integer ambiguity as a Mixed-Integer Nonlinear Programming (MINLP) problem. Crucially, it leverages IMU-predicted kinematics as a strong physical constraint to effectively identify and suppress measurements contaminated by multipath. Subsequently, a back-end, tightly-coupled Error-State Kalman Filter (ESKF) fuses the Time-Differenced Carrier Phase (TDCP) and Time Difference of Arrival (TDOA) measurements from the front-end with inertial navigation data. Simulation results conducted under 3GPP standard scenarios demonstrate that the proposed framework achieves a positioning accuracy improvement of approximately 40% in centimeter-level accuracy, in line-of-sight (LOS) environments with tactical IMU equipment. This provides a promising solution for 5G-based indoor localization systems. In real-world indoor field tests characterized by multipath effects, our proposed EE-ESKF algorithm achieved a three-dimensional root mean square error (RMSE) of 0.55 meters, which is 33.6% higher than the standard ESKF algorithm.

Elderly people with speech impairments often face challenges in engaging in meaningful social communication, particularly when using Augmentative and Alternative Communication (AAC) tools that primarily address basic needs. Moreover, effective chats often rely on personal memories, which is hard to extract and reuse. We introduce SocializeChat, an AAC tool that generates sentence suggestions by drawing on users’ personal memory records. By incorporating topic preference and interpersonal closeness, the system reuses past experience and tailors suggestions to different social contexts and conversation partners. SocializeChat not only leverages past experiences to support interaction, but also treats conversations as opportunities to create new memories, fostering a dynamic cycle between memory and communication. A user study shows its potential to enhance the inclusivity and relevance of AAC-supported social interaction.