Human Weight Compensation With a Backdrivable Upper-Limb Exoskeleton: Identification and Control

Active exoskeletons are promising devices for improving rehabilitation procedures in patients and preventing musculoskeletal disorders in workers. In particular, exoskeletons implementing human limb’s weight support are interesting to restore some mobility in patients with muscle weakness and help in occupational load carrying tasks. The present study aims at improving weight support of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments, for design and validation purposes, are conducted on a total of 65 participants who performed posture maintenance and elbow flexion/extension movements. The introduction of joint misalignments in the weight support model significantly reduced the model errors, in terms of weight estimation, and enhanced the estimation reliability. The introduced control architecture reduced model tracking errors regardless of the condition. Weight support significantly decreased the activity of antigravity muscles, as expected, but increased the activity of elbow extensors because gravity is usually exploited by humans to accelerate a limb downwards. These findings suggest that an adaptive weight support controller could be envisioned to further minimize human effort in certain applications.

Jiles-Atherton-Based Hysteresis Identification of Joint Resistant Torque in Active Spacesuit Using SA-PSO Algorithm

To eliminate the influence of spacesuits’ joint resistant torque on the operation of astronauts, an active spacesuit scheme based on the joint-assisted exoskeleton technology is proposed. Firstly, we develop a prototype of the upper limb exoskeleton robot and theoretically analyse the prototype to match astronauts’ motion behavior. Then, the Jiles-Atherton model is adopted to describe the hysteretic characteristic of joint resistant torque. Considering the parameter identification effects in the Jiles-Atherton model and the local optimum problem of the basic PSO (particle swarm optimization) algorithm, a SA- (simulated annealing-) PSO algorithm is proposed to identify the Jiles-Atherton model parameters. Compared with the modified PSO algorithm, the convergence rate of the designed SA-PSO algorithm is advanced by 6.25% and 20.29%, and the fitting accuracy is improved by 14.45% and 46.5% for upper limb joint model. Simulation results show that the identified J-A model can show good agreements with the measured experimental data and well predict the unknown joint resistance torque.

sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the [email protected] could reach 82.3%, and [email protected]–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.

Eyes-Free Tongue Gesture and Tongue Joystick Control of a Five DOF Upper-Limb Exoskeleton for Severely Disabled Individuals

Spinal cord injury can leave the affected individual severely disabled with a low level of independence and quality of life. Assistive upper-limb exoskeletons are one of the solutions that can enable an individual with tetraplegia (paralysis in both arms and legs) to perform simple activities of daily living by mobilizing the arm. Providing an efficient user interface that can provide full continuous control of such a device—safely and intuitively—with multiple degrees of freedom (DOFs) still remains a challenge. In this study, a control interface for an assistive upper-limb exoskeleton with five DOFs based on an intraoral tongue-computer interface (ITCI) for individuals with tetraplegia was proposed. Furthermore, we evaluated eyes-free use of the ITCI for the first time and compared two tongue-operated control methods, one based on tongue gestures and the other based on dynamic virtual buttons and a joystick-like control. Ten able-bodied participants tongue controlled the exoskeleton for a drinking task with and without visual feedback on a screen in three experimental sessions. As a baseline, the participants performed the drinking task with a standard gamepad. The results showed that it was possible to control the exoskeleton with the tongue even without visual feedback and to perform the drinking task at 65.1% of the speed of the gamepad. In a clinical case study, an individual with tetraplegia further succeeded to fully control the exoskeleton and perform the drinking task only 5.6% slower than the able-bodied group. This study demonstrated the first single-modal control interface that can enable individuals with complete tetraplegia to fully and continuously control a five-DOF upper limb exoskeleton and perform a drinking task after only 2 h of training. The interface was used both with and without visual feedback.

Industrial Upper-Limb Exoskeleton Characterization: Paving the Way to New Standards for Benchmarking

Exoskeletons have been introduced in industrial environments to prevent overload or repetitive stress injuries in workers. However, due to the lack of public detailed information about most of the commercial exoskeletons, it is necessary to further assess their load capacity and evolution over time, as their performance may change with use. We present the design and construction of a controlled device to measure the torque of industrial exoskeletons, along with the results of static and dynamic testing of an exoskeleton model. A step motor in the test bench moves the exoskeleton arm in a pre-defined path at a prescribed speed. The force measured with a beam load cell located at the interface between the exoskeleton arm and the test bench is used to derive the torque. The proposed test bench can be easily modified to allow different exoskeleton models to be tested under the same conditions.