Cognitive and neuromuscular robotic rehabilitation framework

© Springer International Publishing AG 2017.This paper presents a cognitive and neuromuscular robotic rehabilitation framework to support enhanced control of arm movement for humans with muscular control impairment, typically with some level of memory deficiency due to, for example, suffering from a stroke. It describes the design, development and integration of the framework architecture as well as a Baxter robot based demonstration platform. Three key elements of the proposed framework (rehabilitation module, workspace and rehabilitation scenarios) have been described in detail. In the rehabilitation sessions, the users and the robot are asked to work together to place cubes so as to form a predefined shape. The robot and the user hold the same object in order to move it to a particular destination according to a rehabilitation scenario. If the robot detects a force from the user directed in the wrong direction during the navigation then it resists and corrects the movement in order to assist the user towards the right direction. The assistive support scenarios were designed to evaluate the achieved enhancement of precision, efficiency and dexterity of arm movements. The proposed rehabilitation framework provides a modular, automated and open-source platform for researchers and practitioners in neuromuscular rehabilitation applications

Classification of human hand movements using surface EMG for myoelectric control

© Springer International Publishing AG 2017.Surface electromyogram (sEMG) is a bioelectric signal that can be captured non-invasively by placing electrodes on the human skin. The sEMG is capable of representing the action intent of nearby muscles. The research of myoelectric control using sEMG has been primarily driven by the potential to create humanmachine interfaces which respond to users intentions intuitively. However, it is one of the major gaps between research and commercial applications that there are rarely robust simultaneous control schemes. This paper proposes one classification method and a potential real-time control scheme. Four machine learning classifiers have been tested and compared to find the best configuration for different potential applications, and non-negative matrix factorisation has been used as a pre-processing tool for performance improvement. This control scheme achieves its highest accuracy when it is adapted to a single user at a time. It can identify intact subjects hand movements with above 98% precision and 91% upwards for amputees but takes double the amount of time for decision-making

Running time of experiments on DBLP-four-areas dataset.

<p>Running time of experiments on DBLP-four-areas dataset.</p

AC of experiments on DBLP-four-areas dataset.

<p>AC of experiments on DBLP-four-areas dataset.</p

The iteration number and running time with different <i>D</i> and <i>S</i>.

<p>The iteration number and running time with different <i>D</i> and <i>S</i>.</p

Examples of network schemas for two different heterogeneous information networks.

<p>(A): DBLP network with a star network schema. (B): Douban Movie network with a general network schema.</p

NMI of experiments on DBLP-four-areas dataset.

<p>NMI of experiments on DBLP-four-areas dataset.</p

The AC and NMI with different <i>T</i> and <i>O</i>.

<p>The AC and NMI with different <i>T</i> and <i>O</i>.</p

The iteration number and running time with different <i>T</i> and <i>O</i>.

<p>The iteration number and running time with different <i>T</i> and <i>O</i>.</p

The AC and NMI values with different <i>D</i> and <i>S</i>.

<p>The AC and NMI values with different <i>D</i> and <i>S</i>.</p