## 4 Results  
The sensor data images when the system is running are shown in Figures 10-12. Comparing the movement angle curves of the joints when carrying heavy objects, we can find that the movement angle curves of the joints (the Figure 10 and the Figure 11a) have not changed significantly with or without the assistance of the exoskeleton during the process of lifting heavy objects, which means that no mechanical and control interference affect the carrier's manual material handling. In the gait phase of Figure 11c, the system distinguishes between semi-squat and upright. The definition of gait phase is consistent with the definition of finite state machine. Since the transition of the human body in semi-squat and upright is not realized immediately, we define the transition process state of the two states as 0. The controller can further distinguish between bending and standing up according to the slope of the angle sensor data and assist the human body to get up. As can be seen in Figure 12, turns out that under different joint angular velocities, the output torque of the exoskeleton hip joint changes with the angular velocity of the human body when the subject is standing up. The controller system calculates the torque of the exoskeleton hip joint based on the angular velocity. It can be found that when the movement is fast, the output torque of the joint is small; compared with when the movement speed is slow, the average output torque of the joint is smaller and the peak torque lasts shorter. Figure 11: The result of the control system recognizing the posture of the human body. (**a**) Manual material handling assisted by exoskeleton angle sensor data. (**b**) Interact force during manual material handling. (**c**) Gait phase in manual material handling. Figure 10: Manual material handling without exoskeleton sensor data. In the experiment of detecting the metabolism cost of energy in the state of continuous transportation, the energy consumption of the subject's continuous transportation with or without an exoskeleton was recorded and subtracted from the basic metabolism cost of energy. Figure 13 shows the change of energy consumption with time under the state of continuous transportation; the results showed that wearing the exoskeleton upright did not significantly increase the overall energy consumption, while during the reciprocating process, the total energy consumption of the carrier decreased by 18% (n = 3, two-sided paired \(t\) tests, p = 0.091 \(<\) 0.01). After rectifying the original EMG signal and low-pass (10-400 Hz) filtering, the root mean square (RMS) value was calculated for the EMG signal every 5 ms, and the EMG signal amplitude of the muscle group is shown in Figure 14a-d, respectively; the mean EMG amplitude in four muscle groups is shown in Figure 14e. Comparing the EMG signals of the erector spinae with or without exoskeleton assistance during transport, we found that the activity of EMG signals without exoskeletons was significantly higher than with exoskeleton assistance. Specifically, there was a 48% (without 97.9 \(\pm\) 11.9 and with 50.1 \(\pm\) 6.4 \(\mu\)v, n = 7, two-sided paired \(t\) tests, p = 0 \(<\) 0.05) reduction of muscle activity for the LTTES (Left Thorax Thoracic Erector Spinae), a 47% (without 121.2 \(\pm\) 21.8 and with 64.1 \(\pm\) 7.04 \(\mu\)v, n = 7, two-sided paired \(t\) tests, p = 0 \(<\) 0.05) reduction for the LTLES (Left Thorax Lumbar Erector Spinae), a 37% (without 86.7 \(\pm\) 6.0 and with 53.5 \(\pm\) 6.01 \(\mu\)v, n = 7, two-sided paired \(t\) tests, p = 0 \(<\) 0.05) reduction for the RTTES (Right Thorax Thoracic Erector Spinae), and a 30% (without 64.3 \(\pm\) 14.1 and  
Figure 12: Actuator output torque at different speeds when standing up. Figure 13: Metabolic cost of energy during the continuous manual material handling with and without exoskeleton. (**a**) Metabolic Rate in 5 min. (**b**) Metabolic cost of energy in 5 min.