Abstract
Using motion information of the upper limb to control the prosthetic hand has become a hotspot of current research. The operation of the prosthetic hand must also be coordinated with the user’s intention. Therefore, identifying action intention of the upper limb based on motion information of the upper limb is key to controlling the prosthetic hand. Since a wearable inertial sensor bears the advantages of small size, low cost, and little external environment interference, we employ an inertial sensor to collect angle and angular velocity data during movement of the upper limb. Aiming at the action classification for putting on socks, putting on shoes and tying shoelaces, this paper proposes a recognition model based on the Dynamic Time Warping (DTW) algorithm of the motion unit. Based on whether the upper limb is moving, the complete motion data are divided into several motion units. Considering the delay associated with controlling the prosthetic hand, this paper only performs feature extraction on the first motion unit and the second motion unit, and recognizes action on different classifiers. The experimental results reveal that the DTW algorithm based on motion unit bears a higher recognition rate and lower running time. The recognition rate reaches as high as 99.46%, and the average running time measures 8.027 ms. In order to enable the prosthetic hand to understand the grasping intention of the upper limb, this paper proposes a Generalized Regression Neural Network (GRNN) model based on 10-fold cross-validation. The motion state of the upper limb is subdivided, and the static state is used as the sign of controlling the prosthetic hand. This paper applies a 10-fold cross-validation method to train the neural network model to find the optimal smoothing parameter. In addition, the recognition performance of different neural networks is compared. The experimental results show that the GRNN model based on 10-fold cross-validation exhibits a high accuracy rate, capable of reaching 98.28%. Finally, the two algorithms proposed in this paper are implemented in an experiment of using the prosthetic hand to reproduce an action, and the feasibility and practicability of the algorithm are verified by experiment.
Highlights
The human hand is one of the most important organs of the human body
Based on the above analysis, this paper proposes a Dynamic Time Warping (DTW) algorithm based on motion unit
The motion data of the upper limb were collected by inertial sensors, and the action intentions of the upper limb were analyzed and recognized
Summary
The human hand is one of the most important organs of the human body. It can perform tasks which include grasping, kneading and other actions according to human intentions. This can lead to upper limb amputation and loss of the ability to perform daily activities, such as putting on shoes, dressing, and eating. A lack of hand functionality presents significant challenges to the daily lives of handicapped individuals. An artificial manipulator represents a kind of artificial hand with some appearance features and functions of the human hand, which can simulate the action of the human hand, and has become an effective extension of human limb function. A significant application field of artificial manipulators is the assistance of the handicapped [1,2].
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