Yida Liu

Exoskeleton robots are frequently applied to augment or assist the user’s natural motion. Generally, each assisted joint corresponds to at least one specific motor to ensure the independence of movement between joints. This means that as there are more joints to be assisted, more motors are required, resulting in increasing robot weight, decreasing motor utilization, and weakening exoskeleton robot assistance efficiency. To solve this problem, the design and control of a lightweight soft exoskeleton that assists hip-plantar flexion of both legs in different phases during a gait cycle with only one motor is presented in this paper. Inspired by time-division multiplexing and the symmetry of walking motion, an actuation scheme that uses different time-periods of the same motor to transfer different forces to different joints is formulated. An automatic winding device is designed to dynamically change the loading path of the assistive force at different phases of the gait cycle. The system is designed to assist hip flexion and plantar flexion of both legs with only one motor, since there is no overlap between the hip flexion movement and the toe-offs movement of the separate legs during walking. The weight of the whole system is only 2.24 kg. PD iterative control is accomplished by an algorithm that utilizes IMUs attached on the thigh recognizing the maximum hip extension angle to characterize toe-offs indirectly, and two load cells to monitor the cable tension. In the study of six subjects, muscle fatigue of the rectus femoris, vastus lateralis, gastrocnemius and soleus decreased by an average of 14.69%, 6.66%, 17.71%, and 8.15%, respectively, compared to scenarios without an exoskeleton.

Wearable robotic devices have been proved to considerably reduce the energy expenditure of human walking. It is not only suitable for healthy people, but also for some patients who require rehabilitation exercises. However, in many cases, the weight of soft exosuits are relatively large, which makes it difficult for the assistant effect of the system to offset the metabolic consumption caused by the extra weight, and the heavy weight will make people uncomfortable. Therefore, reducing the weight of the whole system as much as possible and keeping the soft exosuit output power unchanged, may improve the comfort of users and further reduce the metabolic consumption. In this paper, we show that a novel lightweight soft exosuit which is currently the lightest among all known powered exoskeletons, which assists hip flexion. Indicated from the result of experiment, the novel lightweight soft exosuit reduces the metabolic consumption rate of wearers when walking on the treadmill at 5 km per hour by 11.52% compared with locomotion without the exosuit. Additionally, it can reduce more metabolic consumption than the hip extension assisted (HEA) and hip flexion assisted (HFA) soft exosuit which our team designed previously, which has a large weight. The muscle fatigue experiments show that using the lightweight soft exosuit can also reduce muscle fatigue by about 10.7%, 40.5% and 5.9% for rectus femoris, vastus lateralis and gastrocnemius respectively compared with locomotion without the exosuit. It is demonstrated that decreasing the weight of soft exosuit while maintaining the output almost unchanged can further reduce metabolic consumption and muscle fatigue, and appropriately improve the users’ comfort.

To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.