Incremental learning of EMG-based Control commands using Gaussian Processes

Myoelectric control is the process of controlling a prosthesis or an assistive robot by using electrical signals of the muscles. Pattern recognition in myoelectric control is a challenging field, since the underlying distribution of the signal is likely to change during the application. Covariate shifts, including changes of the arm position or different levels of muscular activation, often lead to significant instability of the control signal. This work tries to overcome These challenges by enhancing a myoelectric human machine interface through the use of the sparse Gaussian Process (sGP) approximation Variational Free Energy and by the introduction of a novel adaptive model based on an unsupervised incremental learning approach. The novel adaptive model integrates an interclass and intraclass distance to improve prediction stability under challenging conditions. Furthermore, it demonstrates the successful incorporation of incremental updates which is shown to lead to a significantly increased performance and higher stability of the predictions in an online user study

Electromyography for teleoperated tasks in weightlessness

The cooperation between robots and astronauts will become a core element of future space missions. This is accompanied by the demand for suitable input devices. An interface based on electromyography (EMG) represents a small, light and wearable device to generate a continuous 3D control signal from voluntarily muscle activation of the operator's arm. We analyzed the influence of microgravity on task performance during a 2D task on a screen. Six subjects performed aiming and tracking tasks in parabolic flights. Three different levels of fixation -- fixed feet using foot straps, semi-free by using a foot rail, and free-floating feet -- were tested to investigate how much user fixation is required to operate via the interface. The user study showed that weightlessness affects the usage of the interface only to a small extent. Success rates between 89% and 96% were reached within all conditions during microgravity. A significant effect between 0G and 1G could not be identified for the test series of fixed and semi-free feet, while free-floating feet showed significantly worse results in fine and gross motion times in 0G compared to ground tests (with success rates of 92% for 0G and 99% for 1G). Further adaptation to the altered proprioception may be needed here. Hence, foot rails as already mounted in the ISS would be sufficient to use the interface in weightlessness. Low impact of microgravity, high success rates, and an easy handling of the system, indicates a high potential of an EMG-based interface for teleoperation in space

A New Labeling Approach for Proportional Electromyographic Control

Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to create labels for decoder calibration. In certain scenarios, where ground truth, such as additional sensor data, can not be measured, e.g., with people suffering from physical disabilities, these methods come with the challenge of generating appropriate labels. We introduce a new approach that uses the EMG-feature stream recorded during a simple training procedure to generate continuous labels. The method avoids synchronization mismatches in the labels and has no need for additional sensor data. Furthermore, we investigated the influence of the transient phase of the muscle contraction when using the new labeling approach. For this purpose, we performed a user study involving 10 subjects performing online 2D goal-reaching and tracking tasks on a screen. In total, five different labeling methods were tested, including three variations of the new approach as well as methods based on binary labels, which served as a baseline. Results of the evaluation showed that the introduced labeling approach in combination with the transient phase leads to a proportional command that is more accurate than using only binary labels. In summary, this work presents a new labeling approach for proportional EMG control without the need of a complex training procedure or additional sensors

Shared Control Templates for Assistive Robotics

Light-weight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we propose a constraint-based shared control scheme to define skills which provide support during task execution. This is achieved by representing a skill as a sequence of states, with specific user command mappings and different sets of constraints being applied in each state. New skills are defined by combining different types of constraints and conditions for state transitions, in a human-readable format. We demonstrate its versatility in a pilot experiment with three activities of daily living. Results show that even complex, high-dimensional tasks can be performed with a low-dimensional interface using our shared control approach

EDAN - An EMG-controlled Daily Assistant To Help People With Physical Disabilities

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured,processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment

Functional Tasks performed by People with Severe Muscular Atrophy using an sEMG controlled Robotic Manipulator

For paralyzed people activities of daily living like eating or drinking are impossible without external assistance. Robotic assistance systems can give these people a part of their independence back. Especially if the operation with a joystick is not possible anymore due to a missing hand function, people need innovative interfaces to control assistive robots in 3D. Besides brain computer interfaces an approach based on surface electromyography (sEMG) can present an opportunity for people with a strong muscular atrophy. In this work we show that two people with proceeded spinal muscular atrophy can perform functional tasks using an sEMG controlled robotic manipulator. The interface provides a continuous control of three degrees of freedom of the endeffector of the robot. The performance was assessed with two clinical measures of upper limb functionality: the Box and Blocks Test and the Action Research Arm Test. Additionally, the participant could show that they can drink by themselves with the provided system

An sEMG-based Interface to give People with Severe Muscular Atrophy control over Assistive Devices

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. While assistive technology becomes more and more available to the people affected, there is still a big need for user interfaces suitable for people without functional hand movement. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to functionally move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice, to create an EMG-based user interface, and e.g. allow for control of assistive devices. In this paper, we enhance this user interface with additional EMG-features and an improved training paradigm in order to increase information extraction from recordings of strongly atrophic muscles. The interface was tested and validated by subjects with severe spinal muscular atrophy. Results show that the used methods improve the decoding and thereby allow for a considerable increase in performance when controlling a robotic manipulator in a 3D reaching task

Blindfolded Robotic Teleoperation using Spatial Force Feedback to the Toe

This paper examines the capability to incorporate spatial force feedback to the human toe when teleoperating a robotic arm in a force task. Due to the growing complexity of teleoperated systems new means of feedback get increasingly important. To investigate the viability of spatial toe-feedback, experiments with 12 subjects were conducted. The participants had to teleoperate a DLR Light-Weight Robot (LWR) via optical tracking of one finger in order to push a toy train. The orientation of the rail was unknown to the subject and had to be explored using the haptic Feedback - a three-dimensional spatial force to the toe, reflecting the contact forces at the robotic endeffector - in absence of visual feedback. The rail was mounted in one of four possible orientations (differences of 45°). The main task of the experiment was to identify the present orientation. In our study subjects could successfully identify the orientation of the rail in more than two thirds of all trials (68%). In almost half of the trials (44%) the subjects were able to move the train along the rails long enough to reach the bumpers at the end and identify them as such. Assuming no feedback would be provided at all, the first metric has a chance level of 25%, and reaching the bumper can be considered impossible. Thus, we can conclude that humans can incorporate spatial force feedback to the toe into their sensorimotor loop

Analysis of Humans Capability to Discriminate Multidimensional Forces at the Big Toe

Tactile sensation represents an essential skill for tasks of daily living. However, people that are depending on auxiliary devices, like hand prosthesis or assisitve-robotic arms, have to life mostly without this skill. In most of the commercially available active hand prosthesis only an open-loop control is possible. Usually no haptic information is provided to the user. Given that the glabrous skin of the feet provides a similar sensory structure as the glabrous skin of the hands, a new approach for haptic feedback could be applying haptic information to the toes. During object manipulations with the hands the human perceives and processes multidimensional forces in the human hand. This master thesis analyzes whether humans have the capability to discriminate multidimensional forces also at the big toe. Using a modified state-of-the-art haptic feedback device open-loop tests with 24 healthy subjects were performed. Tangential displacements from 0° to 25° were provided to the subject’s right big toe while contact forces of 2N, 5N or 8N were applied. In total the four different directions of up (distal), down (proximal), left (medial), and right (lateral) were tested. The results showed that within defined proportions of the tangential force a directional discrimination was possible at 5 N and above. The tangential displacement as well as the total acting force showed an influence to the results. The four different directions showed no affect across all subjects