# A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information
## 1 Introduction  
### Background  
Power assistive devices have been developed in recent years [1; 2; 3] for various purposes: in the welfare field for nursing [4; 5], rehabilitation [6; 7; 8; 9; 10], and gait assistance [11; 12; 13; 14; 15; 16; 17; 18; 19]; in the industrial field for assembling and load-carrying [20; 21; 22; 23; 24]; and in the military field [25]. For example, [PERSON] [4] developed a robot suit HAL (Hybrid Assistive Limb) driven by electric motors, and it detects the walking intention by bioelectrical sensors attached to the users. [PERSON] et al. [6] proposed an upper limb rehabilitation robot embedded with force/torque sensors. [PERSON] et al. [11; 12; 13] developed a soft exosuit which is light weight system driven by Bowden cables, and [PERSON] et al. [26] proposed the online control parameter tuning method of the soft exosuit. [PERSON] et al. [23] developed a muscle suit driven by a pneumatic artificial rubber muscle (PARM), and measured muscle fatigue by oxygen concentration in the blood in order to evaluate the assist effect [24]. BLEEX (Berkeley Lower Extremity Exoskeleton) [25] was developed for enhancing carrying capacity and is driven by hydraulic actuators. These devices have different mechanisms, and appropriate sensors and actuators are utilized in each situation. One of the technical challenges for realizing a practical assistive device is to synchronize the assistive force to the wearer's motion. To detect the wearer's intention of motion, most conventional devices requireusers to wear electric sensors, such as bioelectrical sensors [4; 5], joint-angle sensors [10; 14; 20; 22], inertial measurement unit (IMU) [11; 12; 13], force/ torque sensors [6; 15; 17; 22], and undersole pressure sensors [11; 12; 13; 20; 25]. The reliability of the intention detection can be increased by fusing multi-modal information from different type sensors. However, wearing many sensors increases the time required to start up the device because of its calibration process, and usability of the device will become worse. Therefore, from an easy wearing point of view, it is desirable for the assistive devices to reduce wearable sensors. On the other hand, several assistive devices using the PARMs have been developed [27]. The PARM has high power-to-weight ratio and is lightweight and soft, making it suitable for exoskeleton type power assistive device. [PERSON] et al. [28] developed a pneumatic gait assistive suit helps the ankle motion. This suit uses electric foot switches for the motion detection. [PERSON] et al. [29] developed a soft-inflatable exosuit for knee rehabilitation. This device uses IMUs and shoe insole sensors. [PERSON] et al. [30] developed a wearable device for ankle-foot rehabilitation. [PERSON] et al. [31] developed a pneumatic assistive device with a wearable air supply system. The targets of these researches [30; 31] are the novel device design, and motion detection method is not their subjects. [PERSON] et al. [18] proposed a pneumatically-driven walking assistive device and its control method using the backdrivability of the PARMs. In this control method, the gait phase of the wearer is detected by using air pressure sensors isolated from the device itself, without attaching electric sensors to the wearer's body. The several gait phase detection methods have been developed. For example, [PERSON] et al. [32] developed an algorithm to continuously recognize a variety of locomotion modes performed by patients with transfemoral amputations. Electromyogram (EMG) signals and ground reaction forces/moments were measured and used for the algorithm. [PERSON] et al. [33] developed an insole sensor system that can determine various dynamic models of a lower extremity exoskeleton. [PERSON] et al. [34] proposed a feature selection method to evaluate all the possible combinations of the gait parameters, in order to find the best subset able to classify among diseased and healthy subjects. In this method, medical data is retrieved from a stereophotogrammetric system. [PERSON] et al.