# Bio-Inspired Real-Time Prediction of Human Locomotion for Exoskeletal Robot Control
## 1 Introduction  
Human motion detection is of fundamental importance for control of human-robot coupled systems such as exoskeletal robots, and for many other applications. In recent years, inertial measurement unit (IMU) technologies have quickly evolved, and complex mechanisms for tracking the operator's movement [1, 2] have gradually been replaced by motion capture systems comprising IMUs [3, 4, 5, 6, 7]. IMU-based motion capture systems have unique advantages, such as multidimensional information richness, compactness, and light weight. However, compared with biosensors for which signals appears earlier than in human body motion [8, 9, 10], human motion capture systems bear the apparent disadvantage of asynchronization with execution delay, which is a critical challenge for applications in exoskeletal robot control.  
Human locomotion is a nonlinear quasi-periodic process with chaos [11] resulting from latent human musculoskeletal dynamics. During human walking, the two lower limbs operate synergistically [12, 13, 14, 15]. Both the quasi-periodicity and the synergy lead to the development ofseveral efficient prediction methods. Firstly, by drawing evidence from the lower limb synergy, echo control has long been used in powered prosthesis for generating joint trajectories of one limb by a phase shift of the opposite limb [16]. To apply echo control in prediction, the time shift needs to be correctly identified. Secondly, the complementary limb motion estimation (CLME) method [8] improves the echo control method by assuming a coupling relationship between the two lower limbs. However, its performance is not always consistent due to a significant influence from the underlying regression method [17]. Thirdly, the aforementioned quasi-periodic nonlinear behaviour of walking has led to [PERSON] reconstruction method, which is closely related to [PERSON] reconstruction theorem. It is essentially a nonlinear time series dynamic factor analysis method for predicting joint trajectories [18; 19]. However, it requires historical system data to achieve a better performance which brings in inevitable dead time. There are also some other prediction methods, such as the autoregressive integrated model method, autoregressive integrated moving average method, autoregressive moving average model method [20], and modified recursive least square methods [21], which are also time series analysis methods but with undesired computational expense, dead time, or complex parameter tuning processes. Besides, the consistency and smoothness of human joint motion should not be ignored as human joints possess apparent biological damping [22; 23; 24] which damps out the high frequency vibration. Such observations indicate that the traditional [PERSON]'s method, in the short term for smoothing or correction techniques, might be beneficial. For [PERSON]'s method, estimation of the velocity and acceleration with noise rejecting ability is needed. High-gain observers could be exploited, which are usually used in robot or automation applications [25; 26; 27].  
In this work, we consider, without loss of generality, a series of prediction methods and its improving techniques utilizing the biological features of human locomotion. Based on progressive analysis, we introduce a novel method which fuses the advantages of different approaches to achieve an even better performance verified by a daily motion dataset of a normal subject, including irregular motion. The method is also applicable to real-time embedded systems.  
This paper is organized as follows. Section 2 introduces the IMU-based motion capture system. Section 3 describes different prediction methods, assessment measures, and the used dataset. Section 4 evaluates the influence of several common parameters and the prediction performance with different prediction methods by simulation, elaborates their characteristics, and then proposes our best method. Also, the real-time applicability of the proposed algorithm is demonstrated. Section 5 concludes the paper.