Investigating User Volitional Influence on Step Length in Powered Exoskeleton Designed for Users with SCI

Volitional movement from users of assistive lower limb exoskeletons may be exploited to increase the controlled variability in the movements of a human-exoskeleton system. This may in turn allow these devices to handle the variability encountered in the terrain of everyday life. This study aimed to investigate the degree to which users can volitionally influence step length, when using an assistive exoskeleton designed for users with spinal cord injury (SCI) running a fixed robotic exoskeleton trajectory. An experiment was conducted to investigate the accessible range of step lengths when five able-bodied participants and one participant with SCI piloted a user-balanced exoskeleton. Participants were asked to take steps as large as possible ("large") and as small as possible ("small"), with the able-bodied individuals asked to minimise use of their leg muscles, with step length of each step measured. Surface electromyography (sEMG) data were collected on major leg muscles of the able-bodied subjects to monitor their muscle activities with a novel processing method introduced to facilitate discussion in the context of users with SCI. The results demonstrate that a user can intentionally manipulate the resulting step length, with every participant having significantly different large and small step sizes (p < 0.05). However, large variations were observed between individuals in terms of absolute step lengths and difference between large and small steps. Moreover, the range of step length (normalised by the leg length) ranged from 0.237 to 0.375 for the able-bodied subjects and 0.245 for the individual with SCI. Although positive correlation was present between the sEMG data and resulting step lengths, the result was not statistically significant (p > 0.05).

Review of control strategies for lower-limb exoskeletons to assist gait

Background

Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user.

Methods

In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller.

Results

291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper.

Conclusions

By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00906-3.

Design on electrohydraulic servo driving system with walking assisting control for lower limb exoskeleton robot

According to the characteristics of human gait and the requirements of power assistance, locomotive mechanisms and electrohydraulic servo driving are designed on a lower limb exoskeleton robot, in which the miniaturization and lightweight of driving system are realized. The kinematics of the robot is analyzed and verified via the typical movements of the exoskeleton. In this article, the simulation on the power of joints during level walking was analyzed in ADAMS 2016, which is a multibody simulation and motion analysis software. Motion ranges and driving strokes are then optimized. A proportional integral derivative (PID) control method with error estimation and pressure compensation is proposed to satisfy the requirements of joints power assistance and comply with the motion of human lower limb. The proposed method is implemented into the exoskeleton for assisted walking and is verified by experimental results. Finally, experiments show that the tracking accuracy and power-assisted performance of exoskeleton robot joints are improved.

A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

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.