Carrying heavy objects in agricultural and industrial sites is the most basic labor, which requires a lot of energy. Many equipment such as crane, chain block, elevator, and forklift truck has been developed to reduce human power. Nevertheless, many tasks require human labor. In addition, rapid aging is increasing musculoskeletal diseases in industrial workers. Consequently, various muscle auxiliary wear robots and devices are being developed. In this study; a passive upper limbs exoskeleton (H-Frame) was developed to help carry over 20 kg of weight in industrial and agricultural sites. For the functional test of the developed H-Frame, tests were carried out for 20, 30, and 40 kg of each box. To measure the objective and numerical data of the H-Frame, various sensor values such as EMG (Electromyography), harness compression force sensor, and load cell value of side support and rope were measured. EMG and metabolic experiments were also performed on 8 subjects before and after wearing the device. The average value of the upper extremity muscle showed a 44% reduction effect after wearing. The device helped the wearer when carrying heavy objects. It could help prevent musculoskeletal diseases in industrial and agricultural fields.

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.