Abstract
To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.
Highlights
The soft suit exoskeleton robot has drawn wide attention in recent years
The great majority of soft exoskeletons are only made available for single locomotion mode, which makes the wearer uncomfortable when walking on stairs and ramps
We propose a recognition method with historical information based on neural network to recognize different terrain, which solves the problem of the single control of the current single terrain of the soft exoskeleton
Summary
The soft suit exoskeleton robot has drawn wide attention in recent years. It has widely used in fields of both military and civil life to enhance people’s walking ability and relieve people’s fatigue under the condition of heavy load and long-time walking [1]. In the control system of the soft suit exoskeleton, human motion intention recognition plays an important role [2,3,4,5]. Recognition delay is still one of the greatest challenges in the sense system soft exoskeleton, in the recognition of different terrain. In the control of the soft exoskeleton robot, it is necessary to recognize the motion pattern under different terrain
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