Embedded Systems & Robotics, TCS Research and Innovation, Tata Consultancy Services, Kolkata, India.
PLoS One. 2018 Jun 1;13(6):e0196348. doi: 10.1371/journal.pone.0196348. eCollection 2018.
Eye tracking is one of the most widely used technique for assessment, screening and human-machine interaction related applications. There are certain issues which limit the usage of eye trackers in practical scenarios, viz., i) need to perform multiple calibrations and ii) presence of inherent noise in the recorded data. To address these issues, we have proposed a protocol for one-time calibration against the "regular" or the "multiple" calibration phases. It is seen that though it is always desirable to perform multiple calibration, the one-time calibration also produces comparable results and might be better for individuals who are not able to perform multiple calibrations. In that case, "One-time calibration" can also be done by a participant and the calibration results are used for the rest of the participants, provided the chin rest and the eye tracker positions are unaltered. The second major issue is the presence of the inherent noise in the raw gaze data, leading to systematic and variable errors. We have proposed a signal processing chain to remove these two types of errors. Two different psychological stimuli-based tasks, namely, recall-recognition test and number gazing task are used as a case study for the same. It is seen that the proposed approach gives satisfactory results even with one-time calibration. The study is also extended to test the effect of long duration task on the performance of the proposed algorithm and the results confirm that the proposed methods work well in such scenarios too.
眼动追踪是评估、筛选和人机交互相关应用中最广泛使用的技术之一。在实际场景中，眼动追踪器的使用受到某些限制，例如：i）需要进行多次校准，ii）记录数据中存在固有噪声。为了解决这些问题，我们提出了一种针对“常规”或“多次”校准阶段的一次性校准协议。虽然多次校准总是可取的，但一次性校准也能产生可比的结果，并且对于无法进行多次校准的个体可能更好。在这种情况下，“一次性校准”也可以由参与者完成，只要下巴托和眼动追踪器的位置不变，校准结果就可以用于其他参与者。第二个主要问题是原始注视数据中存在固有噪声，导致系统和可变误差。我们提出了一种信号处理链来消除这两种类型的误差。我们使用了两种基于不同心理刺激的任务，即回忆识别测试和数字凝视任务，作为相同的案例研究。结果表明，即使进行一次性校准，所提出的方法也能得到令人满意的结果。该研究还扩展到测试长时间任务对所提出算法性能的影响，结果证实所提出的方法在这种情况下也能很好地工作。
