IEEE Trans Biomed Circuits Syst. 2020 Dec;14(6):1299-1310. doi: 10.1109/TBCAS.2020.3027242. Epub 2020 Dec 31.
The tracking of eye gesture movements using wearable technologies can undoubtedly improve quality of life for people with mobility and physical impairments by using spintronic sensors based on the tunnel magnetoresistance (TMR) effect in a human-machine interface. Our design involves integrating three TMR sensors on an eyeglass frame for detecting relative movement between the sensor and tiny magnets embedded in an in-house fabricated contact lens. Using TMR sensors with the sensitivity of 11 mV/V/Oe and ten <1 mm embedded magnets within a lens, an eye gesture system was implemented with a sampling frequency of up to 28 Hz. Three discrete eye movements were successfully classified when a participant looked up, right or left using a threshold-based classifier. Moreover, our proof-of-concept real-time interaction system was tested on 13 participants, who played a simplified Tetris game using their eye movements. Our results show that all participants were successful in completing the game with an average accuracy of 90.8%.
使用基于隧道磁电阻(TMR)效应的自旋电子传感器的可穿戴技术来跟踪眼部运动,无疑可以改善行动和身体不便人士的生活质量,从而实现人机接口。我们的设计包括在眼镜架上集成三个 TMR 传感器,用于检测传感器与嵌入在内部制造的隐形眼镜中的微小磁铁之间的相对运动。使用灵敏度为 11 mV/V/Oe 的 TMR 传感器和透镜内的十个小于 1 毫米的嵌入式磁铁,实现了采样频率高达 28 Hz 的眼部运动系统。当参与者使用基于阈值的分类器向上看、向右或向左看时,成功地对三个离散的眼部运动进行了分类。此外,我们的实时交互系统的概念验证在 13 名参与者上进行了测试,他们使用眼部运动玩简化版的俄罗斯方块游戏。我们的结果表明,所有参与者都成功地完成了游戏,平均准确率为 90.8%。