Development and preliminary testing of a novel wheelchair integrated exercise/ rehabilitation system

A research on the postural stability of a person wearing the lower limb exoskeletal robot by the HAT model

This paper proposes a method of detecting the postural stability of a person wearing the lower limb exoskeletal robot with the HAT(Head-Arm-Trunk) model. Previous studies have shown that the human posture is stable when the CoM(Center of Mass) of the human body is placed on the BoS(Base of Support). In the case of the lower limb exoskeletal robot, the motion data, which are used for the CoM estimation, are acquired by sensors in the robot. The upper body, however, does not have sensors in each segment so that it may cause the error of the CoM estimation. In this paper, the HAT(Head-Arm-Trunk) model which combines head, arms, and torso into a single segment is considered because the motion of head and arms are unknown due to the lack of sensors. To verify the feasibility of HAT model, the reflecting markers are attached to each segment of the whole human body and the exact motion data are acquired by the VICON to compare the COM of the full body model and HAT model. The difference between the CoM with full body and that with HAT model is within 20mm for the various motions of head and arms. Based on the HAT model, the XCoM(Extrapolated Center of Mass) which includes the velocity of the CoM is used for prediction of the postural stability. The experiment of making unstable posture shows that the XCoM of the whole body based on the HAT model is feasible to detect the instance of postural instability earlier than the CoM by 20-250 msec. This result may be used for the lower limb exoskeletal robot to prepare for any action to prevent the falling down.

A method to accurately estimate the muscular torques of human wearing exoskeletons by torque sensors

In exoskeletal robots, the quantification of the user’s muscular effort is important to recognize the user’s motion intentions and evaluate motor abilities. In this paper, we attempt to estimate users’ muscular efforts accurately using joint torque sensor which contains the measurements of dynamic effect of human body such as the inertial, Coriolis, and gravitational torques as well as torque by active muscular effort. It is important to extract the dynamic effects of the user’s limb accurately from the measured torque. The user’s limb dynamics are formulated and a convenient method of identifying user-specific parameters is suggested for estimating the user’s muscular torque in robotic exoskeletons. Experiments were carried out on a wheelchair-integrated lower limb exoskeleton, EXOwheel, which was equipped with torque sensors in the hip and knee joints. The proposed methods were evaluated by 10 healthy participants during body weight-supported gait training. The experimental results show that the torque sensors are to estimate the muscular torque accurately in cases of relaxed and activated muscle conditions.