Abstract
The development of an assistive robot to assist human beings in walking normally is a difficult task. One of the main challenges lies in understanding the intention to walk, as an initial phase before walking commences. In this work, we classify the human gait cycle based on data from an inertial moment unit sensor and information on the angle of the hip joint and use the results as initial signals to produce a suitable assistive torque for a lower limb exoskeleton. A neural network module is used as a prediction module to identify the intention to walk based on the gait cycle. A decision tree method is implemented in our system to generate the assistive torque, and a prediction of the human gait cycle is used as a reference signal. Real-time experiments are carried out to verify the performance of the proposed method, which can differentiate between various types of walking. The results show that the proposed method is able to predict the intention to walk as an initial phase and is also able to provide an assistive torque based on the information predicted for this phase.
Highlights
Research into wearable assistive robots has recently become popular, and one example of such a robot is the exoskeleton
It can be seen from the figure that the design consists of several parts: a PC and battery dock, slide frames, a hip joint, represented as a motor driver and Maxon DC motors, and the layer frames as a mechanical support from the thigh to knee. e support frame between the thigh and the knee joint does not actively move and follows the natural movement of the thigh, since no actuators are mounted on the frame. e PC central controller and the battery are located in the dock; in this study, an ordinary PC was used, meaning that our prototype was limited in its movement by the length of the connector. e prototype for this robot was 3D printed using PLA material
A Maxon 408057 motor was chosen as the actuator for the hip joint of the robot. e robot was constructed with no conversion gear, so that the maximum torque produced by the actuator was only about 0.227 Nm. e prototype of this robot can be seen in Figure 1(b), which shows all of the components mounted on the robot and its placement on the user’s body. e inertial moment unit (IMU) sensor is located on the chest of the user, while the encoder sensors are placed on the Maxon motors on each hip joint of the robot
Summary
Research into wearable assistive robots has recently become popular, and one example of such a robot is the exoskeleton. E human walking gait cycle and the CoP information were used as input signals to the system in order to generate assistive torque based on the user’s intention to move. Is gait synchronizer was used to generate, recognize, synchronize, and segment the actual walking gait, which provided a method of indirectly perceiving intention Most researchers in this area have focused on the walking motion when developing an exoskeleton, in order to ensure that a suitable assistive torque is provided. In order to generate a suitable assistive torque, the walking intention of the user needs to be considered, and to achieve this, information on the gait cycle can be used in the detection of motion. Information on the gait cycle is generated based on data from an IMU sensor placed on the chest of the user and information on the angle of the hip joint. A neural network (NN) module is used to perceive the intention to walk based on the gait and to separate it into several frames. e initial walking prediction is used as input information in order to generate assistive torque using the decision tree method
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