The Effects of EMG-Based Classification and Robot Control Method on User's Neuromuscular Effort during Real-Time Assistive Hand Exoskeleton Operation.

EMG-based intention recognition and assistive device control are often developed separately, which can lead to the unintended consequence of requiring excessive muscular effort and fatigue during operation. In this paper, we address two important aspects of the performance of an integrated EMG-based assistive system. Firstly, we investigate the effects of muscular effort on EMG-based classification and robot control. Secondly, we propose a robot control solution that reduces muscular effort required in assisted dynamic daily tasks compared to the state-of-the-art control methods.

A Modular Design for Distributed Measurement of Human-Robot Interaction Forces in Wearable Devices.

Measurement of interaction forces distributed across the attachment interface in wearable devices is critical for understanding ergonomic physical human-robot interaction (pHRI). The main challenges in sensorization of pHRI interfaces are (i) capturing the fine nature of force transmission from compliant human tissue onto rigid surfaces in the wearable device and (ii) utilizing a low-cost and easily implementable design that can be adapted for a variety of human interfaces. This paper addresses both challenges and presents a modular sensing panel that uses force-sensing resistors (FSRs) combined with robust electrical and mechanical integration principles that result in a reliable solution for distributed load measurement. The design is demonstrated through an upper-arm cuff, which uses 24 sensing panels, in conjunction with the Harmony exoskeleton. Validation of the design with controlled loading of the sensorized cuff proves the viability of FSRs in an interface sensing solution. Preliminary experiments with a human subject highlight the value of distributed interface force measurement in recognizing the factors that influence ergonomic pHRI and elucidating their effects. The modular design and low cost of the sensing panel lend themselves to extension of this approach for studying ergonomics in a variety of wearable applications with the goal of achieving safe, comfortable, and effective human-robot interaction.

Improvement of hand functions of spinal cord injury patients with electromyography-driven hand exoskeleton: A feasibility study.

We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test, and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects.

Methodologies for determining minimal grasping requirements and sensor locations for sEMG-based assistive hand orthosis for SCI patients.

In this paper, we address two of the most important challenges in development and control of assistive hand orthosis. First, supported by experimental results, we present a method to determine an optimal set of grasping poses, essential for grasping daily objects. Second, we present a method for determining the minimal number of surface EMG sensors and their locations to carry out EMG-based intention recognition and to control the assistive device by differentiating between the hand poses.