稳健性(进化)
机器人
人工智能
计算机科学
接口(物质)
感知器
实施
一般化
MATLAB语言
人工神经网络
机器学习
人机交互
操作系统
基因
最大气泡压力法
数学分析
生物化学
气泡
并行计算
化学
程序设计语言
数学
作者
Marta C. Mora,José V. García-Ortiz,Joaquín Cerdá-Boluda
出处
期刊:Sensors
[MDPI AG]
日期:2024-03-23
卷期号:24 (7): 2063-2063
摘要
The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.
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