Understanding the effects of different hip assistance modes is a fundamental step in the process of designing hip assistance devices and controllers that can provide better performance in terms of metabolic cost. We have developed and tested a soft exoskeleton for hip assistance, which includes three assistance modes: hip extension assistance (HEA), hip flexion assistance (HFA), and hip extension and flexion assistance (HEFA). A proportional derivative (PD) iterative learning controller based on the feedforward model was proposed to control the assistive force accurately. The three hip assistance modes were evaluated on seven male subjects walking on a treadmill at a speed of 5 km/h in two scenarios--first with a 15-kg backpack and then without any backpack. The net metabolic costs could be reduced during the loaded condition, compared with those under no exoskeleton condition, by 9.95%, 6.25%, and 15.28% for HEA, HFA, and HEFA, respectively. The reductions were found significant in HEA (p=0.048) and HEFA (p=0.005) modes, while the HFA mode (p=0.202) was not found statistically significant. It indicates that the HEA and HEFA modes with the soft exoskeleton provide more benefit to the net metabolic cost compared with the HFA mode. The net metabolic costs reduced during the unloaded condition were 9.21%, 2.58%, and 13.05% for HEA, HFA, and HEFA, respectively. The improvements in the walking efficiency during both the conditions with the developed soft exoskeleton are demonstrated.

Lower-limb exoskeleton robots can effectively help patients with lower-limb disabilities caused by stroke or spinal cord injury to walk again. However, when faced with complex ground surfaces, patients find it difficult to quickly and accurately transmit the motion intention to the robot, resulting in falls or errors. In this article, we develop the vision-assisted autonomous lower-limb exoskeleton robot (VALOR). Based on the principles of human visual feedback and motion decision-making, a vision-assisted autonomous gait pattern planning method is proposed to improve the adaptability of the robot to the environment. The robot obtains environmental information via an RGB-D camera and extracts the ground object features that might affect gait. Then, the robot makes an autonomous decision according to the environmental features, robot state, and safety constraints. Lastly, a suitable step length and height are given to the parameterized gait pattern planning model of robot to assist with walking. The feasibility of the proposed method is verified on VALOR in a controlled indoor environment with limited obstacles, and our results demonstrate that the method can significantly improve robot adaptability to complex walking environments.

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.

The loaded walking ability of people can be enhanced by lower limb exoskeleton. How to avoid lower limb joint injuries while improving walking efficiency is a major challenge for loaded walking assistance. Inspired by the muscle-skeleton working mechanism of human, we proposed a lower limb exoskeleton with rigid and soft structure for active hip extension assistance and passive load transfer assistance. Its motion flexibility, load transfer ability and metabolic cost were investigated. Five motions including jumping jack, sit down, crouch, across obstacle, and forward lunge were realized by the wearer. Under the conditions of quiet standing and dynamic walking, the percentages indicating the degree of load transfer to the ground reached up to 88% and 67%, respectively. Compared to the condition of no exoskeleton, the net metabolic reductions of 20kg-loaded walking in three experimental setups, namely, active-passive assistance on, active exosuit assistance only, and active assistance off-passive assistance on, achieved 12.8%, 8.5% and 3.7%, individually. The results demonstrate that our rigid-soft lower limb exoskeleton presents outperformance in loaded walking assistance.

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

In dynamic manufacturing and warehousing environments, the work scene made it impossible for workers to sit, so workers suffer from muscle fatigue of the lower limb caused by standing or squatting for a long period of time. In this paper, a semi-active exoskeleton used to reduce the muscle fatigue of the lower limb was designed and evaluated. (i) Background: The advantages and disadvantages of assistive exoskeletons developed for industrial purposes were introduced. (ii) Simulation: The process of squatting was simulated in the AnyBody.7.1 software, the result showed that muscle activity of the gluteus maximus, rectus femoris, vastus medialis, vastus lateralis, vastus intermedius, and erector spinae increased with increasing of knee flexion angle. (iii) Design: The exoskeleton was designed with three working modes: rigid-support mode, elastic-support mode and follow mode. Rigid-support mode was suitable for scenes where the squatting posture is stable, while elastic-support mode was beneficial for working environments where the height of squatting varied frequently.The working environments were identified intelligently based on the EMGs of the gluteus maximus, and quadriceps, and the motor was controlled to switch the working mode between rigid-support mode and elastic-support mode. In follow mode, the exoskeleton moves freely with users without interfering with activities such as walking, ascending and descending stairs. (iv) Experiments: Three sets of experiments were conducted to evaluate the effect of exoskeleton. Experiment one was conducted to measure the surface electromyography signal (EMGs) in both condition of with and without exoskeleton, the root mean square of EMGs amplitude of soleus, vastus lateralis, vastus medialis, gastrocnemius, vastus intermedius, rectus femoris, gluteus maximus, and erector spinae were reduced by 98.5, 97.89, 80.09, 77.27, 96.73, 94.17, 70.71, and 36.32%, respectively, with the assistance of the exoskeleton. The purpose of experiment two was aimed to measure the plantar pressure with and without exoskeleton. With exoskeleton, the percentage of weight through subject's feet was reduced by 63.94, 64.52, and 65.61% respectively at 60°, 90°, and 120° of knee flexion angle, compared to the condition of without exoskeleton. Experiment three was purposed to measure the metabolic cost at a speed of 4 and 5 km/h with and without exoskeleton. Experiment results showed that the average additional metabolic cost introduced by exoskeleton was 2.525 and 2.85%. It indicated that the exoskeleton would not interfere with the movement of the wearer Seriously in follow mode. (v) Conclusion: The exoskeleton not only effectively reduced muscle fatigue, but also avoided interfering with the free movement of the wearer.