## 1 Introduction
For the input signal selection, electromyography (EMG) signals which reflect the muscle activation have been widely utilized in the fields of joint angle learning and prediction [19,24]. The EMG signal provides easy access to physiological processes that cause the muscle to generate force and produce movement. Different from intramuscular EMG which uses a fine needle electrode inserted into the muscle, surface EMG (sEMG) is more commonly used due to its outstanding characters including noninvasiveness and safety [25,26]. As the quality of sEMG signals is very sensitive to muscle fatigue, sweat, and changing electrode characteristics, a proper processing method is needed and the application of on-line learning approaches, such as the GP and ANN as just introduced, would be preferred. In addition, sEMG signals are also used as the measurement output in SSMs for human joint angle prediction, such as the redundant subsectors of the sEMG-vector [4] and other extracted EMG features, including waveform length (WL) and zero crossing (ZC) [27]. Meanwhile, interactive force, which is a direct end-effector reflection in human-exoskeleton systems, can be regarded as an important indicator of transparency between a human and robot [28,29]. Although the fusion of EMG and force would improve the performance of joint angle prediction, interactive force has rarely been used in combination with EMG signals in joint angle prediction [30]. More importantly, how the interactive force works in the human-exoskeleton system model, i.e., its biomechanical generation mechanism, is often ignored. Thus, the influence of the interactive force on the human-exoskeleton system is always overlooked. The motivation of this paper is to develop a GP-integrated SSM for continuous joint prediction and to provide biomechanical interpretations for its state model and measurement model in human-exoskeleton integrated applications. To the best of our knowledge, the interactive force has rarely been used in combination with EMG signals in the continuous joint prediction of human-exoskeleton systems. In the proposed model, interactive force is applied as the measurement model output and the EMG signal is used as the state model input to indicate muscle activation. [PERSON]'s muscle model and semi-phenomenological (SP) model [31] are adopted to acquire the forward dynamics of joint motion for the state model and the human-machine interaction mechanism interpretation for the measurement model, respectively. In practical applications, the GP-based NARX model and back propagation neural network (BPNN) are applied to provide better flexibility and adaptivity. Compared with the existing SSMs for continuous joint angle prediction [4; 27], our main contributions lie in three aspects. First, interactive force is fused with the EMG signal in the SSM instead of using extracted EMG signal features alone. When the impacts of interactive force on the human-exoskeleton system are considered, the performance of joint angle prediction would improve. Second, in addition to the use of machine learning techniques, [PERSON]'s muscle model and SP model are used to derive the biomechanical mechanisms for the state model and measurement model of the proposed SSM. Third, as a non-parametric model, GP is integrated with traditional BPNN to combine the relative strengths of both the parametric model and non-parametric model. Corresponding experimental results demonstrate the validity and superiority of the method.