Clinical Psychology Service, Health Department, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy.
Department of Industrial Engineering, University of Florence, 50121 Florence, Italy.
Sensors (Basel). 2022 Apr 8;22(8):2861. doi: 10.3390/s22082861.
Emotion recognition skills are predicted to be fundamental features in social robots. Since facial detection and recognition algorithms are compute-intensive operations, it needs to identify methods that can parallelize the algorithmic operations for large-scale information exchange in real time. The study aims were to identify if traditional machine learning algorithms could be used to assess every user emotions separately, to relate emotion recognizing in two robotic modalities: static or motion robot, and to evaluate the acceptability and usability of assistive robot from an end-user point of view.
Twenty-seven hospital employees (M = 12; F = 15) were recruited to perform the experiment showing 60 positive, negative, or neutral images selected in the International Affective Picture System (IAPS) database. The experiment was performed with the Pepper robot. Concerning experimental phase with Pepper in active mode, a concordant mimicry was programmed based on types of images (positive, negative, and neutral). During the experimentation, the images were shown by a tablet on robot chest and a web interface lasting 7 s for each slide. For each image, the participants were asked to perform a subjective assessment of the perceived emotional experience using the Self-Assessment Manikin (SAM). After participants used robotic solution, Almere model questionnaire (AMQ) and system usability scale (SUS) were administered to assess acceptability, usability, and functionality of robotic solution. Analysis wasperformed on video recordings. The evaluation of three types of attitude (positive, negative, andneutral) wasperformed through two classification algorithms of machine learning: k-nearest neighbors (KNN) and random forest (RF).
According to the analysis of emotions performed on the recorded videos, RF algorithm performance wasbetter in terms of accuracy (mean ± sd = 0.98 ± 0.01) and execution time (mean ± sd = 5.73 ± 0.86 s) than KNN algorithm. By RF algorithm, all neutral, positive and negative attitudes had an equal and high precision (mean = 0.98) and F-measure (mean = 0.98). Most of the participants confirmed a high level of usability and acceptability of the robotic solution.
RF algorithm performance was better in terms of accuracy and execution time than KNN algorithm. The robot was not a disturbing factor in the arousal of emotions.
情绪识别技能预计将成为社交机器人的基本特征。由于面部检测和识别算法是计算密集型操作，因此需要确定可以并行化算法操作的方法，以便实时进行大规模信息交换。本研究旨在确定传统的机器学习算法是否可以单独用于评估每个用户的情绪，以及在两种机器人模式（静态或运动机器人）中识别情绪，并从最终用户的角度评估辅助机器人的可接受性和可用性。
招募了 27 名医院员工（男性 12 名；女性 15 名）进行实验，展示了从国际情感图片系统（IAPS）数据库中选择的 60 张正性、负性或中性图片。实验是在 Pepper 机器人上进行的。在 Pepper 处于主动模式的实验阶段，根据图像类型（正性、负性和中性）编程了一致的模仿。在实验过程中，机器人胸部的平板电脑和网页界面上显示每张幻灯片持续 7 秒。对于每张图片，参与者都被要求使用自我评估情绪量表（SAM）对感知到的情绪体验进行主观评估。在参与者使用机器人解决方案后，使用 Almere 模型问卷（AMQ）和系统可用性量表（SUS）评估机器人解决方案的可接受性、可用性和功能性。对视频记录进行了分析。通过两种机器学习分类算法：k-最近邻（KNN）和随机森林（RF），对三种态度（正性、负性和中性）进行评估。
根据对录制视频进行的情绪分析，RF 算法在准确性（平均值±标准差=0.98±0.01）和执行时间（平均值±标准差=5.73±0.86 秒）方面的性能优于 KNN 算法。通过 RF 算法，所有中性、正性和负性态度的精度（平均值=0.98）和 F 度量（平均值=0.98）都相等且很高。大多数参与者都确认了机器人解决方案的高度可用性和可接受性。
RF 算法在准确性和执行时间方面的性能优于 KNN 算法。机器人在引起情绪方面不是一个干扰因素。
