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.

Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active rehabilitation methods can respond to patients’ motor intentions in real-time and significantly increase motivation and engagement, leading to efficient utilization of critical recovery windows and better rehabilitation outcomes. In this review, we focus on the physiological basis of active rehabilitation, including mechanisms of neuroplasticity, and discuss recent advances in intent detection and feedback devices. We also examine treatment options for different stages of stroke recovery, providing a comprehensive reference for engineers to design optimized rehabilitation techniques and for clinicians to select appropriate rehabilitation protocols. These developments create new opportunities to improve the lives of stroke patients and offer greater hope for their recovery.

BACKGROUND: Dual-task impairment severely limits functional recovery post-stroke. This diagnostic accuracy study aimed to develop an eye-tracking-based system for objectively quantifying ankle-cognitive integration deficits in stroke survivors. METHODS: This diagnostic accuracy study was conducted from January 2022 to October 2023 at Tsinghua Changgung Hospital in Beijing. A total of 20 healthy adults (mean age 53.15±6.26 years) participated in the study. In addition, 30 patients with a history of stroke (mean age 64.13±8.16 years, 8 females, disease duration 9.12±6.60 weeks) participated in a standardised dual-task evaluation. The novel system utilised 17 parameters, encompassing ankle kinematics (range of motion, velocity) and eye tracking (gaze duration, sweep latency), which were measured simultaneously during the cognitive motor task. Reliability was assessed by intragroup correlation coefficients (ICC), while criterion validity was assessed using 12 clinical evaluation metrics, including Spearman correlation with Montreal Cognitive Assessment (MOCA) scores and dual-task cost (DTC) percentages. RESULTS: The system demonstrated that 88.2% of the evaluation parameters exhibited high consistency, with 55.8% showing a moderate correlation with clinical benchmark scales (p<0.05). Notably, MOCA, DTC%, and TUG-subtraction task duration were identified as key indicators of dual-task ability (P<0.05), while the Self-Rating Anxiety Scale showed lower sensitivity. Furthermore, ankle motion parameters exhibited a strong correlation with balance and fall risk (P<0.05), effectively serving as predictors of motor function recovery and fall risk in stroke patients. CONCLUSIONS: This multimodal system reliably quantifies post-stroke dual-task deficits, with ankle kinematics and eye-tracking metrics serving as sensitive biomarkers for balance recovery and fall risk stratification. Findings advocate integrating objective dual-task metrics into neurorehabilitation protocols to optimize functional outcomes. (ChiCTR2300067640; URL: https://www.chictr.org.cn/showproj.html?proj=188211 ).

Human–machine interfaces based on force touch panels have attracted enormous attention due to the merits of the high human–machine interaction efficiency. Many studies have been devoted to diverse force touch technologies. Broad applications in terms of both actual use and research have been developed, such as 3D touch and force-based keystroke authentication. The fruitful results are based on the assumption that users’ touch habits remain unchanged over time; thus, a stationary customized force-sensing model can be built. However, for long-term use, users’ touch habits change due to time-drifting and specific events, causing a decrease in the performance of stationary force-sensing models. To address this issue, a rectified artificial neural network for long-term force sensing in piezoelectric touch panels is presented in this paper. With additional information on the touching time and the occurrence of specific events, the force level predictions were rectified, achieving an accuracy of 97.62% for a long-term data set. The proposed technique enables customized force sensing for long-term use and enhances the human–machine interactive efficiency.

Haptic feedback devices provide a sense of immersion that makes them play a key role in the domain of human-environment interactivities, such as in telemedicine, teleoperation, and online education. However, widely used unimodal or limited-modal approaches, such as vibrotactile, electrotactile, force-feedback, thermal, and emerging mid-air ultrasound/magnetorheological systems, are merely capable of simulating few virtual materials, exhibit a deficiency in fidelity, and necessitate substantial training expenditures. To address these limitations, we propose a multivirtual object recognition system based on multimodal haptic feedback and develop an evaluation and training scheme. The system is based on electrohaptic, vibrohaptic, and thermohaptic, acquires four haptic perception modalities, roughness, sharpness, hardness, and temperature, and simulates the haptic sensation of eight different materials. In experiments, the fidelity alignment of haptic feelings of real and virtual objects is 85.8%, and after averaged 2.8 trainings, the recognition accuracy reaches 100%. Our research not only improves the realism of haptic feedback but also significantly reduces the training cost, providing a more cost-effective and practical solution for user experience enhancement in virtual reality (VR) and meta-universe applications.