## 1 Introduction  
Modern robots, such as rehabilitation robots, try to actively interact with human partners towards achieving a common task. This gives rise to the emerging field of research named human robot interaction (HRI), aiming at maximizing the performance, efficiency, and applicability of coupled human-robot systems [1]. It can be defined as \"a field of study dedicated to understanding, designing, and evaluating robotic systems for use by or with humans\" [2]. The system performance, however, is largely limited by the output of human intent learning and prediction [3]. In many human-robot systems, continuous human motion intents instead of a limited number of classified motion patterns have to be identified and further reconstructed as control commands to the robot so that the robot could match human motion intents and perform efficient assistances [4]. Thus, how to determine the human motion intent in terms of continuous joint angle has become the key issue. Two main related problems remain: one is the choice of the learning model for continuous joint angle prediction, the other is the selection of input signals that could reflect both the human motion dynamics and the human-robot interaction state well. As for the modelling methods for the forward dynamics of human neuromusculoskeletal system, one choice is the application of phenomenological or semi-phenomenological models. Clear biomechanical interpretations can be found in these kinds of models. Although most of the existing models, such as the model proposed by [PERSON] [5] and [PERSON] [6,7], are based on the phenomenological models derived from [PERSON]'s work [8] or the more complex biophysical model of [PERSON] [9], the transformation from muscle activation to muscle force and human kinematics has not been fully understood yet. In addition, the determination of muscle geometry, such as the muscle-tendon moment arm, is especially difficult for a living person and thus makes the estimation prone to error [10,11]. Problems also exist associated with the application of phenomenological models when the workload of the model parameter identification for each subject is extremely heavy [12,13]. The other approach that bypasses the above problems and limitations is to use machine learning methods. Moreover, with the fast development of artificial intelligence, artificial neural networks (ANNs) are now widely applied in the control of human-exoskeleton systems [1]. In addition, the Gaussian process (GP) is also receiving more attention in the field [14, 15, 16]. The GP model has superior flexibility when the parameters that directly determine the model performance can be adjusted through learning [17,18]. As for the mapping structure, one widely applied approach is to map muscle activation or neural signals to the joint angle [19]. When the history of the movements reflects the inherent dynamics of human motion [20], joint angle could also be predicted using autoregressive models. To combine the advantages of both and achieve continuous, intuitive and naturalistic understanding of human motion intent, nonlinear autoregressive with exogenous inputs (NARX) would be a better choice and has begun to be applied in the field of joint angle prediction [4,21,22]. Its application combines the non-linear spatio-temporal correlation structure of natural human movements with muscle-driven control signals to exploit the best of both worlds [23]. However, such approaches treat the entire central nervous system as a black box with no biomechanical interpretations. Thus, the application is still a matter of great debate. The ideal solution to such a contradiction should be one model that not only integrates the state-of-art machine learning techniques that provide good adaptivity for practical applications, but also has corresponding biomechanical interpretations. When the state model and measurement model of state space model (SSM) could be used to interpret the related biomechanical mechanisms, the SSM, which also integrates GP and ANN, may be the promising choice.