## 4 Experimental Study and Discussion  
### Experimental Setup and Data Processing  
Experiments were conducted on the self-developed lower limb exoskeleton [46], as illustrated in Figure 5a. The applied robot was the combination of an exoskeleton robot and a bed which can turn from horizontal to 50\({}^{\circ}\). As shown in Figure 5b, the shank part of the exoskeleton is composed of a shank base and a sliding sleeve. This mechanical structure is adopted so that the contact point between the user and the exoskeleton can be adjusted according to personal requirements before experiments. Once decided, the position will be fixed during the running process when the change of contact point in the process may give rise to reduced prediction performance. A force sensor is planted at the contact point and a gas bag is used to ensure the degree of comfort of the subject. Six healthy men aged from 23 to 30 participated in the experiments. This work was approved by the Ethics Committee of Shanghai Jiao Tong University. All the subjects gave written informed consent  
Figure 4: Joint angle prediction from the muscle activation and interactive force where \(\overline{x}_{k+1}\) represents the estimated value of \(x_{k+1}\) calculated by the UKF  
Figure 5: Experimental setup. (**a**) The self-developed lower-limb exoskeleton worn by the experimental subject and the bed framework, (**b**) the shank part of the exoskeleton. Figure 3: The state space model framework, where GP-NARX and BPNN are utilized in the state model and measurement model; \(\hat{x}_{k+1}\) and \(\hat{y}_{k+1}\) are the predicted values of \(x_{k+1}\) and \(y_{k+1}\)prior to the participation in the study. Each subject was required to make voluntary and random knee flexions and extensions to drive the exoskeleton when the subject's thigh was kept in parallel with the ground and the position of the knee joint was fixed. About seven sessions, in each of which the subject randomly flexed and extended his leg for about 40 s, were conducted for each participant. Every two sessions were separated by a rest of 3 min to avoid muscle fatigue. More sessions would be added when poor data collection quality or obvious errors could be observed and the corresponding results were excluded from the final analysis. The data of the first four sessions of random movements were used for training the motion prediction models, and the remaining three sessions were used as the test data for performance evaluation. During the whole process, the exoskeleton worked under the interactive force sensing and control mode. In this mode, the exoskeleton was driven by the motors to follow human motion in real-time. Through detecting the interactive force between the human and exoskeleton, the trajectory prediction and compensation were implemented. The detailed force control strategy can be found in [47]. Disposable bi-polar Ag-AgCl surface EMG electrodes were used to collect the EMG signals from the RF. In our experiments, only the EMG signals from RF instead of those from both RF and BF were used to feature the muscle activation. The corresponding muscles are illustrated in Figure 6. There were two main reasons that the EMG signals from BF were not included in our model. The first was that the activation of BF stayed low and did not change much during the experiment process when the subject's thigh was kept parallel to the ground [46]. This meant that the BF contributed little to the joint variation. The second reason is that reducing the number of inputs would largely increase the computational efficiency, especially when GP was included in our model [16; 17]. The force signal was processed by a 3 rd-order digital Butterworth filter of 0.01-1 Hz. The EMG signals were processed under the sampling frequency of 2000 Hz. A 4 th-order digital Butterworth filter of 10-500 Hz and a notch-rectifier of 50 Hz (to remove the power noise) were applied to the EMG signals.