用生理信号对比机器人与纸质说明书对装配任务的影响。
Multimodal Sensing and Machine Learning to Compare Printed and Verbal Assembly Instructions Delivered by a Social Robot
- 融合心率和皮肤电活动信号，用深度学习区分指令形式。
- 多模态数据使分类准确率提升超20%，且训练更快。
- 适合人机交互、工业设计及可用性评估研究者阅读。
本文比较了工人在使用纸质说明书与由社交机器人传递的指令进行手动装配任务时的表现差异。实验中通过采集被试者的血容量脉搏（BVP）和皮肤电活动（EDA）等生理信号，并结合NASA任务负荷指数（TLX）问卷调查结果进行分析。进一步将生理信号与TLX评分映射，以预测任务负荷。针对两类分类问题，比较了卷积神经网络（CNN）与长短期记忆网络（LSTM）模型性能。结果显示，基于CNN的多模态方法（同时使用BVP与EDA）相较于仅使用BVP提升约8.38%，较仅使用EDA提升约20.49%；LSTM模型使用多模态数据时也分别比单一信号提升8.38%和6.70%。总体而言，CNN在区分纸制与机器人指令的生理分类中优于LSTM，准确率高出7.72%；且在数分钟内即可实现平均17.83%更高的分类效果，优于LSTM模型。
原文摘要 · Abstract (English)
In this paper, we compare a manual assembly task communicated to workers using both printed and robot-delivered instructions. The comparison was made using physiological signals (blood volume pulse (BVP) and electrodermal activity (EDA)) collected from individuals during an experimental study. In addition, we also collected responses of individuals using the NASA Task Load Index (TLX) survey. Furthermore, we mapped the collected physiological signals to the responses of participants for NASA TLX to predict their workload. For both the classification problems, we compare the performance of Convolutional Neural Networks (CNNs) and Long-Short-Term Memory (LSTM) models. Results show that for our CNN-based approach using multimodal data (both BVP and EDA) gave better results than using just BVP (approx. 8.38% more) and EDA (approx 20.49% more). Our LSTM-based model too had better results when we used multimodal data (approx 8.38% more than just BVP and 6.70% more than just EDA). Overall, CNNs performed better than LSTMs for classifying physiologies for paper vs robot-based instruction by 7.72%. The CNN-based model was able to give better classification results (approximately 17.83% more on an average across all responses of the NASA TLX) within a few minutes of training compared to the LSTM-based models.
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