Khasnobish Anwesha, Pal Monalisa, Sardar Dwaipayan, Tibarewala D N, Konar Amit
School of Bioscience and Engineering, Jadavpur University, Raja S.C. Mullick Road, Kolkata, West Bengal 700032 India.
Department of Electronics and Telecommunication Engineering, Jadavpur University, Raja S.C. Mullick Road, Kolkata, West Bengal 700032 India.
Cogn Neurodyn. 2016 Aug;10(4):327-38. doi: 10.1007/s11571-016-9386-0. Epub 2016 Apr 19.
This work is a preliminary study towards developing an alternative communication channel for conveying shape information to aid in recognition of items when tactile perception is hindered. Tactile data, acquired during object exploration by sensor fitted robot arm, are processed to recognize four basic geometric shapes. Patterns representing each shape, classified from tactile data, are generated using micro-controller-driven vibration motors which vibrotactually stimulate users to convey the particular shape information. These motors are attached on the subject's arm and their psychological (verbal) responses are recorded to assess the competence of the system to convey shape information to the user in form of vibrotactile stimulations. Object shapes are classified from tactile data with an average accuracy of 95.21 %. Three successive sessions of shape recognition from vibrotactile pattern depicted learning of the stimulus from subjects' psychological response which increased from 75 to 95 %. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increase the system efficacy. The tactile sensing module and vibrotactile pattern generating module are integrated to complete the system whose operation is analysed in real-time. Thus, the work demonstrates a successful implementation of the complete schema of artificial tactile sensing system for object-shape recognition through vibrotactile stimulations.
这项工作是一项初步研究，旨在开发一种替代通信渠道，以便在触觉感知受阻时传递形状信息，帮助识别物品。通过装有传感器的机器人手臂在物体探索过程中获取的触觉数据，经过处理以识别四种基本几何形状。从触觉数据中分类得到的代表每种形状的模式，由微控制器驱动的振动电机生成，这些电机通过振动触觉刺激用户，以传达特定的形状信息。这些电机附着在受试者的手臂上，并记录他们的心理（言语）反应，以评估系统以振动触觉刺激的形式向用户传达形状信息的能力。从触觉数据中对物体形状进行分类，平均准确率为95.21%。从振动触觉模式进行的三轮连续形状识别显示，受试者的心理反应对刺激的学习效果从75%提高到了95%。这一观察结果证实了用户在多轮测试中对振动触觉刺激的学习，这反过来又提高了系统的效能。触觉传感模块和振动触觉模式生成模块集成在一起，构成了一个完整的系统，并对其运行进行实时分析。因此，这项工作展示了通过振动触觉刺激实现物体形状识别的人工触觉传感系统完整方案的成功实施。
