## 1 Introduction
[14; 15] found that therapists tended to cue the subject's anterior superior iliac spine (ASIS) when observing the subject's heel strike (HS) on the opposite side during clinical NDT training. Therefore, automatic NDT trainers were designed to repeat this intervention pattern at the key gait events, and many methods have been proposed to identify these important gait events. For instance, optical motion capture systems, such as the VZ4000 [17] and VICON [18], are frequently applied to measure motions. Much research has also applied artificial intelligence to detect gait events based on gait information obtained by inertial measurement units (IMUs) [19; 20]. For example, [PERSON] and [PERSON] [21] applied machine learning to detect real-time gait events using accelerometer signals, whereas [PERSON] et al. [20] estimated stride length and velocity using gyroscope data. [PERSON] [22] proposed multi-feature fusion to recognize freezing gaits, and [PERSON] et al. [23] applied wearable IMUs to recognize elderly users. [PERSON] et al. [16] developed a gait detection algorithm to identify important gait events based on IMU data. The algorithm could automatically update parameters in real time for different users with various walking patterns. However, updating the parameters took several steps (in a few seconds) and might have degraded the rehabilitation effects. Therefore, a recurrent neural network (RNN) model was applied to the gait detection system [24], and this allowed detection of HS events in real time based on the IMU data from two legs. The average accuracy was more than 98%, and the average latency was less than 0.036 s for different subjects, including elderly subjects, stroke patients, and patients with Parkinson's disease (PD). Therefore, in this paper, we applied the RNN model to detect HS events. The currently available NDT trainer can repeat the therapist's intervention pattern by cuing the subject's ASIS when observing the HS on the opposite side. However, we noted that the present intervention method improves the subject's longitudinal symmetry but does not improve pelvic rotation. The locomotion of the pelvis is closely related to walking stability [25; 26]. For example, [PERSON] et al. [27] showed the contribution of pelvic rotation to lengthening the stride, especially when walking at fast speeds. Energy conservation is crucial during gait, and the vertical displacement of the body is minimized by a number of factors known as the determinants of gaits. These determinants operate independently and simultaneously to produce a smooth sinusoidal vertical and horizontal path, which has one vertical peak and trough for each step. One of the determinants of gait is the rotation of the pelvis, which reduces center of mass (COM) movement, thereby conserving energy [28; 29]. Therefore, we analyzed the subjects' pelvic motions and the therapists' interventions during clinical NDT rehabilitation and proposed a modified method to improve the pelvic rotation. We implemented the modified intervention method and recruited 10 healthy subjects who wore a rehabilitation gaiter to mimic stoke gaits to verify the effectiveness of the trainer in improving gait performance and pelvic rotation. We also invited 10 stroke patients to participate in the experiments, and their results confirmed that the proposed intervention method simultaneously improved both pelvic rotation and gait symmetry. This paper is organized as follows. Section 2 describes the movable trainer, which can automatically conduct NDT rehabilitation intervention. The trainer consists of a gait detection system and a motor control system. The gait detection system employs an RNN model to identify HS events in real time, so that the motor system can be activated for NDT intervention at the key time. Section 3 introduces a modified intervention method that can simultaneously improve the subjects' gait symmetry and pelvic rotation. We recruited 10 healthy subjects and had them wear a rehabilitation gaiter to imitate stroke gaits to test the trainer.