# Current Status and Consideration of Support/Care Robots for Stand-Up Motion
## 3 Results
### Assist Form and Control
#### 3.2.3 Designing of Control Strategy  
A total of 39 out of 45 robots used ACTs in this study. Among them, there were 31 cases in which the control strategy was specified, and the remaining 8 cases were not specified. Of the 31 control strategy design, 5 cases used force control, 17 cases used position control, 1 case used hybrid control, and 2 cases used impedance control. In addition, there were 4 switchable cases of position control and force control (P/F), 2 cases of ON/OFF control, 10 cases of no description, and 4 cases of them having no control. The following are classified into force control, position control, and compliant motion control hybrid control and impedance control. The results are summarized in Figure 6, and the previous studies using the respective control strategy are mentioned. Figure 5: Summary of power unit classification in non-wearable robots. Force Control  
One example of the force-control robots used in three of the wearable types is the report of [PERSON] et al. [25] which used \"TTI-Exo.\" TTI-Exo is an electric whole-body exoskeleton robot that estimates a wearer's attitude transition from an input signal detected by an angle sensor installed at the joint of the robot. The output of the ACT designed for the hip joint and knee joint of the robot is controlled so that the assisting torque suitable for the transition can be obtained. The Myosuit reported by [PERSON] et al. [18] is a textile robot that uses both tendon actuators and rubber bands to assist the muscle torque of the user's hip and knee joints. The robot calculates the knee joint angle from the trunk and lower leg angles monitored by the IMU and sends a feedback signal to the PID controller along with the tendon cable length. The exerted torque of the tendon actuator is controlled in real time. An exemplary position-controlled robot used in eight of the wearable types is the \"Vanderbilt powered lower limb prostheses\" from [PERSON] et al. [26]. The posture of the orthotic is estimated from the angle measurement based on the Hall effect in each knee joint and hip joint, a triaxial accelerometer, and a gyroscope in each thigh. Using the estimated values of this posture, four motors give the torque of sagittal plane to the knee joint and hip joint to control the position (in this case, joint angle). The non-wearable type uses position control in 9 cases, one of them is an \"E-JUST Assistive Device\" reported by [PERSON] et al. [41]. The robot estimates the user's posture through inputs from the user's lower limb and an inertial sensor worn on the fuselage; it controls the trajectory of the end-effector to imitate the real motion of the caregiver. The operation of the end effector is performed by a motor located at the joint part of the robot. Figure 6: Summary of control strategy classification. Hybrid Control/Impedance Control  
In the literature investigated this time, hybrid control is used in the \"HAL\" of [PERSON] et al. [11], which is a wearable type. This robot measures the relative angle by attaching a potentiometer to each joint of the lower leg, arranges a three-axis accelerometer in the control box to be attached to the waist, measures the absolute angle of the fuselage, and calculates the center of gravity by attaching a pressure sensor to the foot sole of the shoe. They recognize the wearer's operation intention by using the center of gravity position and floor reaction force sensors, and give assistance/assistance torque to each joint using the power unit in each joint. In this control, each torque and joint angle of the ankle joint, the knee joint, and the hip joint are controlled simultaneously. Examples of impedance control include FUM-Knee EXO of [PERSON] et al. [14]. This equipment measures the deflection of the spring with a porcelain linear encoder; the power of LSEA is used for the estimation, the rotational angle of the ankle joint division is measured by the encoder, and it is used for the track estimation.