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<p>Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial intelligence and mobile computing, deep learning algorithms for computer vision-based eye tracking have become feasible for smartphones. This paper presents a real-time smartphone eye-tracking system built upon a deep neural network trained on a dataset of 7.4 million facial images. The tracking performance of the system was benchmarked against an industrial gold-standard EyeLink eye tracker using a reasonably large sample (<i>N</i> = 32). The benchmark test showed that, while the smartphone eye-tracking system was less precise (0.177° vs. 0.028°), its tracking accuracy was comparable to the EyeLink tracker (1.32° vs. 1.20°). To evaluate whether the smartphone eye-tracking system is sensitive enough for real-world application, a field test involving 98 volunteers assessed depressive symptoms using three simple visual tasks on a smartphone: fixation stability, free-viewing, and smooth pursuit. The results showed that using the smartphone eye-tracking system can achieve an accuracy of 76.67% in predicting depressive symptoms. These results demonstrate that smartphone eye-tracking can deliver quality data and has potential in scientific and clinical applications.</p>

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Smartphone eye-tracking with deep learning: Data quality and field testing

  • 期刊论文

  • 发表日期：

    2025年6月18日

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  • Gancheng Zhu,
  • Zehao Huang,
  • Xiaoting Duan,
  • Shuai Zhang,
  • Rong Wang,
  • Yongkai Li,
  • Zhiguo Wang

摘要

Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial intelligence and mobile computing, deep learning algorithms for computer vision-based eye tracking have become feasible for smartphones. This paper presents a real-time smartphone eye-tracking system built upon a deep neural network trained on a dataset of 7.4 million facial images. The tracking performance of the system was benchmarked against an industrial gold-standard EyeLink eye tracker using a reasonably large sample (N = 32). The benchmark test showed that, while the smartphone eye-tracking system was less precise (0.177° vs. 0.028°), its tracking accuracy was comparable to the EyeLink tracker (1.32° vs. 1.20°). To evaluate whether the smartphone eye-tracking system is sensitive enough for real-world application, a field test involving 98 volunteers assessed depressive symptoms using three simple visual tasks on a smartphone: fixation stability, free-viewing, and smooth pursuit. The results showed that using the smartphone eye-tracking system can achieve an accuracy of 76.67% in predicting depressive symptoms. These results demonstrate that smartphone eye-tracking can deliver quality data and has potential in scientific and clinical applications.