An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent further deterioration, such as amblyopia and even permanent vision loss. To satisfy this need, this work showcases a novel strabismus screening method based on a wearable eye-tracking device combined with an artificial intelligence (AI) algorithm. To identify the minor and occasional inconsistencies in strabismus patients during the binocular coordination process, which are usually seen in early-stage patients and rarely recognized in current studies, the system captures temporally and spatially continuous high-definition infrared images of the eye during wide-angle continuous motion, and is effective in inducing intermittent strabismus. Based on the collected eye motion information, 16 features of the oculomotor process with strong physiological interpretations, which help biomedical staff understand and evaluate results generated later, are calculated through the introduction of pupil-canthus vectors. These features can be normalized, and reflect individual differences. After these features are processed by the random forest (RF) algorithm, this method experimentally yields 97.1% accuracy in strabismus detection in 70 people under diverse indoor testing conditions, validating the high accuracy and robustness of the method, and implying that the method has strong potential to support widespread and highly accurate strabismus screening.

Amyotrophic Lateral Sclerosis (ALS) patients suffer a great inconvenience in their daily lives due to the gradual loss of their motion abilities. In order to help ALS patients regain their self-care abilities at a certain level, an eye-tracking and brain-computer interface (BCI) based human-environment interactive (HEI) system is proposed. Through a single-channel EEG recorder and a pair of eye-tracking glasses, the user’s attention levels and gaze points are detected. Based on the two types of intention information, the target objects are recognized by a YOLOv5 (You Only Look Once v5) model. Then according to voice instructions, users can control the household electric appliances. The system performed a high success rate of 89.3% experimentally, providing a reliable and capable assistive solution for ALS patients.

At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Moreover, the lack of integrated solutions capable of simultaneously monitoring motor recovery and providing intelligent assistance in home environments hampers rehabilitation outcomes. Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation. A plantar pressure insole equipped with a machine learning pipeline classifies users into motor recovery stages with up to 94% accuracy, enabling quantitative tracking of walking patterns during daily activities. An optional head-mounted eye-tracking module, together with ambient sensors such as cameras and microphones, supports seamless hands-free control of household devices with an average latency under 1 s with consistent operation. These data streams are fused locally via a hierarchical Internet of Things (IoT) architecture, ensuring low latency and data privacy. An embedded large language model (LLM) agent, Auto-Care, continuously interprets multimodal data to provide real-time interventions—issuing personalized reminders, adjusting environmental conditions, and notifying caregivers. Implemented in a post-stroke context, this integrated smart home platform increased mean user satisfaction from $3.9~\pm ~0.8$ in conventional home environments to $8.4~\pm ~0.6$ with the full system (n = 20). Beyond stroke, the system offers a scalable, patient-centered framework with potential for long-term use in broader neurorehabilitation and aging-in-place applications.