In highly mobile workplaces, wearable walking assistant robots can reduce muscle fatigue in the lower extremities of workers and increase energy efficiency. In this study, walking efficiency according to the development of an ultralight wearable hip-assist robot for industrial workers was verified. Five healthy adult males participated in this study. Their muscle fatigue and energy consumption were compared with and without the robot while walking on a flat treadmill and stairs. When walking on the treadmill while wearing the robot, muscle fatigue in the rectus femoris and gastrocnemius decreased by 90.2% and 37.7%, respectively. Oxygen uptake and energy expenditure per minute also decreased by 8.9% and 13.1%, respectively. When climbing stairs while wearing the robot, fatigue of the tibialis anterior, semitendinosus, and gastrocnemius muscles decreased by 18.2%, 33.3%, and 63.6%, respectively. Oxygen uptake and energy expenditure per minute also decreased by 3.6% and 3.7%, respectively. Although wearing a hip-assist robot could reduce muscle fatigue and use metabolic energy more efficiently, it is necessary to further increase the energy efficiency while climbing stairs. This study is intended to provide basic data to improve the performance of robots.

The quality and quantity of heat treatment in mold processing can vary depending on the skill level of the equipment operator. Therefore, study on ways to overcome these disadvantages are essential. This study aimed to increase the antiwear properties of molds through high-frequency induction heat treatment and laser heat treatment processes. The heat treatment was applied to the surfaces of molds used in car body production using an articulated robot, to achieve long-term use and quality maintenance. Additionally, an articulated robot system based on redundant degrees of freedom suitable for mold heat treatment processes was designed, and its operational efficiency was verified through virtual environment simulations. Furthermore, heat treatment was validated through on-site testing of the robot system. Its effects were analyzed according to mold materials and shape conditions, ultimately deriving the optimal robot heat treatment conditions. Finally, off-line programming (OLP) in virtual processes was proposed to minimize robot setup time and maximize production efficiency. The conditions for articulated robot automated heat treatment obtained in this study can be preapplied in simulation environments when generating heat treatment robot programs based on OLP. They can be utilized for optimizing the quality of mold heat treatment in car body production.

Total hip replacement is a representative treatment for avascular necrosis of the femoral head. However, the stress shielding caused by the replacement induces dissociation of the artificial hip joint and various complications. Many studies have tried to explore the stress shielding but, most studies have been conducted at macro level and not at micro level. Thus, this study aimed to quantitatively analyze the structural behavior of the proximal femur according to total hip replacement at the micro level to explore the stress shielding. For this purpose, this study selected the artificial hip joint of the single wedge type and implanted the joint into a proximal femur that has a high resolution of 50 μm. Then the structural behavior of the implanted femur was analyzed by comparing that of the intact femur under three daily activity loads. As a result, the high possibility was confirmed that the stress shielding will occur in both cortical and cancellous bones under the one-legged stance movements. Additionally, it was discovered that the cancellous bone had a considerably lesser chance of adducting at an angle similar to the neck shaft angle of an artificial hip joint.

Recently, the estimation of joint kinetics such as joint force and moment using wearable inertial sensors has received great attention in biomechanics. Generally, the joint force and moment are calculated though inverse dynamics using segment kinematic data, ground reaction force, and moment. However, this approach has problems such as estimation error of kinematic data and soft tissue artifacts, which can lead to inaccuracy of joint forces and moments in inverse dynamics. This study aimed to apply a recurrent neural network (RNN) instead of inverse dynamics to joint force and moment estimation. The proposed RNN could receive signals from inertial sensors and force plate as input vector and output lower extremity joints forces and moments. As the proposed method does not depend on inverse dynamics, it is independent of the inaccuracy problem of the conventional method. Experimental results showed that the estimation performance of hip joint moment of the proposed RNN was improved by 66.4% compared to that of the inverse dynamics-based method.

As the population ages, the concept of active seniors has been emerging recently. Among various body parts that are cared for by an active elderly, the shoulder has a unique exercise structure. Therefore, the incidence of shoulder injuries might be high. In the case of a shoulder disease, the method of measuring the movement angle of the shoulder is mainly used. To measure the movement angle of a shoulder accurately, a goniometer is used. In addition, we suggested self-diagnosis, believing that if shoulder disease could be detected early through self-diagnosis, rapid treatment will be possible. This paper measured and compared shoulder angles with the goniometer, OpenCV, and motion capture systems to determine measurement errors between them. Through experimental results of this paper, the possibility of self-diagnosis with precise measurement of the movement angle of a shoulder oneself with a goniometer was confirmed even if the expert could not measure the shoulder angle.

Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

In this study, an insole-type ground reaction force (GRF) measurement system using a load cell was manufactured and configured as a system that can measure joint angle and GRF, when walking in conjunction with a commercialized inertial sensor. The data acquisition device was used to acquire synchronized data, between the inertial measurement unit (IMU) sensor and the load cell insole. A three-dimensional motion analysis system comprising six infrared cameras and two ground reaction forces, was used to check the accuracy of the gait measurement system, comprising an inertial sensor and a load cell insole. The motion and force data were acquired while performing five times six-meter walking test by five young adult male subjects (Age: 26.0±1.8, Height: 171.4±6.8 cm, Weight: 62.2±10.8 kg). It was measured and as a result of comparing the calculated sagittal joint angle with the vertical GRF, the sagittal lower extremity joint angle correlation coefficient (Pearson’s r) was 0.40 to 0.94, and the vertical GRF to be 0.98 to 0.99. It is necessary to upgrade the joint angle calculation algorithm through future research. Additionally, the possibility of clinical application for actual stroke patients will be reviewed.

Relative position estimation between body segments is one essential process for inertial sensor-based human motion analysis. Conventionally, the relative position was calculated through a constant segment to joint (S2J) vector and the orientation of the segment, assuming that the segment was rigid. However, the S2J vector is deformed by soft tissue artifact (STA) of the segment. In a previous study, in order to handle the above problem, Lee and Lee proposed the relative position estimation method using time-varying S2J vectors based on inertial sensor signals. Here, time-varying S2J vectors were determined through the joint flexion angle using regression. However, it was not appropriate to consider only the flexion angle as a deformation-related variable. In addition, regression has limitations in considering complex joint motion. This paper proposed artificial neural network models to compensate for the STA by considering all three-axis motion of the joint. A verification test was conducted for lower body segments. Experimental results showed that the proposed method was superior to the previous method. For pelvis-to-foot relative position estimation, averaged root mean squared error of the previous method was 17.38 mm, while that of the proposed method was 12.71 mm.

One of the problems in inverse dynamics calculation for the inertial measurement unit (IMU)-based joint force and torque estimation is the amplified signal noises of segment kinematic data mainly due to the differentiation procedure and segmental soft tissue artifacts. In order to deal with this problem, appropriate filtering methods are often recommended for signal enhancement. Conventionally, a low-pass filter (LPF) is widely used for the kinematic data. However, the zero-phase LPF requires post-processing, while the real-time LPF causes an unignorable time lag. For this reason, it is inappropriate to use the LPF for real-time joint torque estimation. This paper proposes a Kalman filter (KF) for inverse dynamics of IMUbased joint torque estimation in real time without any time lag, while utilizing the smoothing capability of the KF. Experimental results showed that the proposed KF outperformed a real-time LPF in the estimation accuracy of hip joint force and torque during jogging on the spot by 100 and 29%, respectively. Although the proposed KF requires the process of adjusting covariance according to the dynamic conditions, it can be expected to improve the estimation performance in the field where joint force and torque need to be estimated in real time.

Knee contact forces and knee stiffness are biomechanical factors worth considering for walking in knee osteoarthritis patients. However, it is challenging to acquire these factors in real time; thus, making it difficult to use them in robotic rehabilitation and assistive systems. This study investigated whether trained deep neural networks (DNNs) can capture the biomechanical factors only using kinematics during gait, which is possible to measure via sensors in real time. A public dataset of walking on the ground was analyzed through biomechanical analysis to train and test DNNs. Using the training dataset, several DNN topologies were explored via Bayesian optimization to tune the hyperparameters. After optimization, DNNs were trained to estimate the biomechanical factors in a supervised manner. The trained DNNs were then evaluated using two new datasets, which were not used in the training process. The trained DNNs estimated the biomechanical factors with a high level of accuracy in both types of test datasets. Results confirmed that DNNs can estimate the biomechanical factors based on only kinematics during gait.

This paper describes the design of a 4-axis SCARA-Type robot in the form of a scalar robot for the loading and unloading of workpieces in machine tools. The 4-axis dedicated robot is a 4-degrees-of-freedom robot consisting of a joint 1, 2, 3 motor and a 180° rotating gripper made up of a horizontal gripper and a vertical gripper. It was designed in a scalar shape that is suitable for machine tools, and the size of each link and elbow was determined through structural analysis. Through additional structural analysis, the deflection of the end center of the workpiece fixed to the horizontal gripper and the vertical gripper was designed to be within 0.1 mm, and based on the design result, a 4-axis SCARA-Type robot was manufactured, and the basic motion characteristics of the manufactured robot were tested. As a result of the characteristic test, the manufactured 4-axis SCARA-Type robot operated smoothly, so it is judged to be adequate for usage in loading and unloading the workpieces in machine tools.

The purpose of this study was to compare ankle joint loads (Linear and Angular Impulses) while descending the stairs and ramp. Ten young male subjects participated in this study. Stairs and ramp of identical slope (30 degrees) were custom-made to include force plates in the middle of pathways. Subjects descended the stairs and ramp at a comfortable speed and posture. The stance period was divided into three phases, weight acceptance (WA), single limb stance, and pre-swing. Three-directional impulses and their sum were derived from the reaction forces and moments at the ankle joint. Differences in impulse sums (Both Linear and Angular) between stairs and ramp were significant only in the early (WA) phase, whereas those of stairs were greater than the ramp. All subjects adopted forefoot strike strategy for the stairs and 80% of the subjects adopted rearfoot strike strategy for the ramp. An increase in the GRF and moment arm of the GRF at the ankle joint in case of forefoot strike may have contributed to the increase in the linear and angular impulse in the early phase of stair descent compared to ramp descent. The results are in agreement with the preference of ramp in the elderly.

This paper deals with the development of a passive modular hip exoskeleton system aimed at preventing musculoskeletal low back pain, which commonly occurs in heavy weight transport workers, by improving back muscle strength. The passive exoskeleton system has the advantage of being lightweight, making it suitable for modular exoskeleton systems. The cam and spring actuator designed in this study was applied to the passive modular exoskeleton system to build human hip and lumbar muscle strength. In order to evaluate the effectiveness of the passive modular exoskeleton system, a test was performed in which a subject lifted a 15 kg weight three times in a stoop posture, using heart rate measurement and Borg scale recording. According to the results, all subjects showed 26.83% lower maximum heart rate and 34.73% lower average heart rate than those who did not wear the system, and Borg scale evaluation result was lower. All subjects wore this system and did not experience back pain during the experiment. Through this study, we validated the effectiveness of the passive modular exoskeleton system and proved that this system can build the strength of industrial workers and be a solution to prevent musculoskeletal lumbar disease.

In this paper, an integrated ankle torque sensor and mechanism (Foot Link) of a Tendon driven-type wearing walking aid robot were designed. The foot link consists of an ankle torque sensor and a mechanism connected to the footrest. The size of the sensing part of the ankle torque sensor was designed through structural analysis and assembled by attaching a strain gauge. As a result, the reproducibility error and the nonlinearity error were within 0.04%, respectively. And the calibration result of the ankle torque sensor, reproducibility error, and non-linearity error were identified to be within 1%, respectively. Therefore, it is proposed that the ankle torque sensor presented in this paper can be used to measure the torque acting on the tendon-driven walking aid robot.

For years, crane, a chain block, an elevator and a forklift truck have been developed and used to carry heavy loads, but manpower needed where heavy equipment use is not practical. Aging workers suffer from musculoskeletal disorders, and are helped by developing various muscle assisting wearable robots. Industrial wearable robots must meet the payload capacity required for the pilot"s overall operation to ensure safety and operational performance. However, the payload capacity of wearable robot using rotary actuator or linear actuator at the knee joint decreases dramatically in the knee-flexion posture, with reduced moment arms. To solve this problem, the author recommends using Single Acting Hydraulic Telescopic Cylinder Electro Hydrostatic Actuator (SAT-EHA) to increase the torque of the knee in the knee flexion position. The characteristic of telescopic cylinder is high speed in 1st stage and high force in 2nd stage. The Human Universal Mobility Assist-Hybrid (HUMA-H) was developed by designing and fabricating the waist joint to balance the front and rear directions using an electric motor driver. As the payload capacity increases, the robot pilots can squat and stand up with heavy loads. The performance was verified through the operation test and respiratory gas analysis test of the manufactured HUMA-H.