Benedicto-Rodríguez Gema, Bosch Facundo, Juan Carlos G, Bonomini Maria Paula, Fernández-Caballero Antonio, Fernandez-Jover Eduardo, Ferrández-Vicente Jose Manuel
Universidad Politécnica de Cartagena, Murcia 30202, Spain.
Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina.
Int J Neural Syst. 2025 Jul;35(7):2550026. doi: 10.1142/S0129065725500261. Epub 2025 Apr 16.
Social robots are increasingly being used in therapeutic contexts, especially as a complement in the therapy of children with Autism Spectrum Disorder (ASD). Because of this, the aim of this study is to understand how children with ASD perceive and interpret the gestures made by the robot Pepper versus human instructor, which can also be influenced by verbal communication. This study analyzes the impact of both conditions (verbal and nonverbal communication) and types of gestures (conversational and emotional) on gesture recognition through the study of the accuracy rate and examines the physiological responses of children with the Empatica E4 device. The results reveal that verbal communication is more accessible to children with ASD and neurotypicals (NT), with emotional gestures being more interpretable than conversational gestures. The Pepper robot was found to generate lower responses of emotional arousal compared to the human instructor in both ASD and neurotypical children. This study highlights the potential of robots like Pepper to support the communication skills of children with ASD, especially in structured and predictable nonverbal gestures. However, the findings also point to challenges, such as the need for more reliable robotic communication methods, and highlight the importance of changing interventions tailored to individual needs.
社交机器人越来越多地被用于治疗场景,尤其是作为自闭症谱系障碍(ASD)儿童治疗的一种补充手段。因此,本研究的目的是了解ASD儿童如何感知和解读机器人Pepper与人类指导者所做出的手势,这也可能受到言语交流的影响。本研究通过研究准确率来分析言语和非言语交流这两种条件以及对话式和情感式这两种手势类型对手势识别的影响,并使用Empatica E4设备检测儿童的生理反应。结果显示,ASD儿童和神经典型(NT)儿童更容易理解言语交流,情感手势比对话式手势更易于解读。研究发现,在ASD儿童和神经典型儿童中,与人类指导者相比,Pepper机器人引发的情绪唤醒反应更低。本研究凸显了像Pepper这样的机器人在支持ASD儿童沟通技能方面的潜力,尤其是在结构化和可预测的非言语手势方面。然而,研究结果也指出了一些挑战,比如需要更可靠的机器人通信方法,并强调了根据个体需求调整干预措施的重要性。