Abstract
The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase.
Highlights
Robotic technology for physical human-robot interaction has the potential to improve human locomotion
Gait training robots, such as ALEX and Lokomat, have an interaction force field controller, which allows patients to walk in a matter that is different from the desired trajectory determined for a healthy person [4,5,6]
We developed a prediction algorithm of minimum toe clearance using the angular information of the lower limb joints [17]
Summary
Robotic technology for physical human-robot interaction has the potential to improve human locomotion. A more accurate toe clearance prediction method that uses wearable sensors to obtain the input data is required for robotic assistance. Previous studies have investigated computational technology, such as accelerometers [19, 20], gyroscopes [21, 22], and IMUs [23], for the detection of foot-contact state using wearable sensors and machine learning strategies implementing support vector machines (SVM) [24], linear discriminant analysis (LDA) [25], Gaussian mixture model (GMM) [26], and hidden Markov model (HMM) [27, 28] None of these methods can detect the characteristic points of phase change in the angular trajectory. We investigated the relationship between the number of training data and the prediction accuracy, and we evaluated the prediction algorithm to investigate whether our method could more accurately predict the toe clearance and detect the lower value of toe clearance
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