Pattern recognition based on HD-sEMG spatial features extraction for an efficient proportional control of a robotic arm.

To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device’s velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 × 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals’ amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm

Improving the forward kinematics of cable-driven parallel robots through cable angle sensors

This paper presents a sensor fusion method that aims at improving the accuracy of cable-driven planar parallel mechanisms (CDPMs) and simplifying the kinematic resolution. While the end-effector pose of the CDPM is usually obtained with the cable lengths, the proposed method combines the cable length measurement with the cable angle by using a data fusion algorithm. This allows for a resolution based on the loop closure equations and a weighted least squares method. The paper first presents the resolution of the forward kinematics for planar parallel mechanisms using cable angle only. Then, the proposed sensor fusion scheme is detailed. Finally, an experiment comparing the different procedures for obtaining the pose of the CDPM is carried out, in order to demonstrate the efficiency of the proposed fusion method

The Impact of Experimental Pain on Shoulder Movement During an Arm Elevated Reaching Task in a Virtual Reality Environment

Background: People with chronic shoulder pain have been shown to present with motor adaptations during arm movements. These adaptations may create abnormal physical stress on shoulder tendons and muscles. However, how and why these adaptations develop from the acute stage of pain is still not well-understood. Objective: To investigate motor adaptations following acute experimental shoulder pain during upper limb reaching. Methods: Forty participants were assigned to the Control or Pain group. They completed a task consisting of reaching targets in a virtual reality environment at three time points: (1) baseline (both groups pain-free), (2) experimental phase (Pain group experiencing acute shoulder pain induced by injecting hypertonic saline into subacromial space), and (3) Post experimental phase (both groups pain-free). Electromyographic (EMG) activity, kinematics, and performance data were collected. Results: The Pain group showed altered movement planning and execution as shown by a significant increased delay to reach muscles EMG peak and a loss of accuracy, compared to controls that have decreased their mean delay to reach muscles peak and improved their movement speed through the phases. The Pain group also showed protective kinematic adaptations using less shoulder elevation and elbow flexion, which persisted when they no longer felt the experimental pain. Conclusion: Acute experimental pain altered movement planning and execution, which affected task performance. Kinematic data also suggest that such adaptations may persist over time, which could explain those observed in chronic pain populations

Muscle Activity Distribution Features Extracted from HD sEMG to Perform Forearm Pattern Recognition

An efficient pattern recognition system based exclusively on forearm surface Electromyographic (sEMG) signals is proposed to provide a more intuitive control of a robotic arm used by some of the disabled. The main contribution of this paper is the use of an original set of features characterizing the muscle activity distribution obtained with high-density sEMG (HD sEMG) sensors. Contrary to simple sEMG, HD sEMG can produce muscle activity images with spatial distributions that differ according to forearm movement. In order to translate this distribution, the proposed set of features includes the center of gravity, the mean amplitude and the percentage of influence computed in each HD sEMG image divided in sub-images. Based on these features, the recognition system locates nine forearm movements with high classification accuracies (99.23%). The results in terms of the number of learning data, the image resolutions (spatial filtering) and the number of sub-images demonstrate the potential of the proposed recognition system and its good performance-complexity trade-off

The REHAB-LAB model for individualized assistive device co-creation and production

Assistive devices are designed to enhance individuals with disabilities' functional abilities. The rise of 3D printing technology enabled the production of individualized assistive devices (IADs). A REHAB-LAB is intended for IAD provision involving technical referents and occupational therapists. This study aimed to develop the REHAB-LAB logic model; to explore its fidelity and desirability; and to explore the characteristics of arising initiatives of IAD production. The REHAB-LAB logic model development involved stakeholders throughout the research process. A pragmatic multimethod approach followed two phases 1) logic model development and 2) exploration of its fidelity and desirability. The REHAB-LAB logic model presented the resources (equipment, space, human) required to implement IAD provision in a rehabilitation center, and the expected deliverables (activities and outputs). The REHAB-LAB logic model highlights the interdisciplinarity of IAD provision including occupational therapists, doctors, engineers, managers, and technical referents and places the users at the center of the IAD production. Results confirmed the fidelity and desirability of the REHAB-LAB logic model. The REHAB-LAB logic model can be used as a reference for future healthcare organizations wishing to implement an IAD provision. This research highlighted the interest of IAD provision based on the REHAB-LAB model involving users and transdisciplinary practice