Sensing information forecasting for Power Assist Walking Legs based on time series analysis

Abstract

The Power Assist Walking Legs (PAWL) is an autonomous exoskeleton robot which is designed for assisting activities of daily life. In order to improve the dynamic response of the exoskeleton robot, a novel sensing information forecasting algorithm is proposed based on the time series analysis. The algorithm is built up with the autoregressive (AR) model, the recursive least square (RLS) method and the final prediction error (FPE) criterion. The method of RLS is used to make the on-line parameters estimation, and the FPE criterion is used to select the order of AR model. Because of the real-time requirement, the forecasting algorithm is designed to be used on-line and to make predictions of force sensor's information to ensure the real-time quality of the whole system. According to requirements, the algorithm can be categorized into two types: one step forecasting method and multi-step forecasting method. Meanwhile, we make some correlative simulations and experiments, and the experiments demonstrate the sensing information forecasting algorithm can predict the value and the trend of the sensing signal, the results of simulations and experiments illustrate the validity and effectiveness of the algorithm.

Towards Automated Monitoring and Forecasting of Probabilistic Quality Properties in Open Source Software (OSS): A Striking Hybrid Approach

“…Besides, time series analysis has been successfully applied in various fields and application domains including: forecasting the number of changes in Eclipse project [29], network anomaly detection [30], information forecasting for Power Assist Walking Legs (PAWL) [31], and more recently analyzing Open Source Software projects [32]. However, the latter work simply used time series analysis to study the time dependence of open source software activities.…”

Multi-Channel Fusion Model of Perception System in Lower Extremity Exoskeleton Suits and it’s Implementation

“…The system includes force sensor, the ground reaction force sensor, and the motor encoder [11]. In order to improve the dynamic response frequency of the lower limb, the system also proposed a novel signal line prediction algorithm based on time series analysis [12]. The other related researches and universities include the Massachusetts Institute of Technology, U.S. SARCOS, Zhejiang University, China Science and Technology University [9].…”