Paper Info
Title
CNN-Based Detection and Classification of Grasps Relevant for Worker Support Scenarios Using sEMG Signals of Forearm Muscles
Abstract
Intuitive man-machine interaction is one of the main challenges towards a wider use of exoskeletons as assistive systems to reduce fatigue and alleviate the risk of injuries of workers during manual handling or overhead work. In this context reliable human grasp detection and classification is a basic but key component to estimate current state and context. While hand gesture recognition using surface electromyography (sEMG) has been addressed in a number of studies, there has been only little research regarding the detection and classification using sEMG of grasps relevant for worker support scenarios, which is the scope of the present study. Specifically, we investigated detection and classification of one-handed power grasps (relevant for tool manipulation) and two-handed grasps (typical while handling crates and boxes) using the instantaneous sEMG vector of the forearm muscles activity acquired using a consumer-grade wearable sensor bracelet. Detection and classification was performed by convolutional neural networks (CNN) trained using datasets including multiple grasps, positions, sessions and users. The accuracy of grasp detection and classification was evaluated in different scenarios with increasing level of signal variability, including sample cross-validation, inter-session scenario and inter-person scenario. We found that high accuracy of grasp detection can be achieved, even in the inter-person scenario. For one-handed grasps, reasonable accuracy of classification of the object manipulated by the user was possible in the inter-person scenario. For two-handed grasps, reasonable accuracy of grasp type classification was possible for some of the grasps even in the inter-person case. These results, achieved with a simple, low-profile acquisition device, show the potential of proposed approach in the context of worker support scenarios.
Year
DOI
Venue
2018
10.1109/SMC.2018.00035
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Keywords
Field
DocType
worker support,grasp detection,grasp classification,CNN,EMG,forearm muscles
GRASP,Convolutional neural network,Computer science,Wearable computer,Gesture recognition,Artificial intelligence,Exoskeleton,Machine learning
Conference
ISSN
ISBN
Citations 
1062-922X
978-1-5386-6651-7
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Christophe Maufroy100.34
Daniel Bargmann200.34