Objective identification of upper limb tremor in multiple sclerosis using a wrist-worn motion sensor: establishing validity and reliability

Introduction Over 25% of people with multiple sclerosis experience tremor, which may impact on activities of daily living and quality of life. Yet there is no method to objectively measure tremor and effectiveness of interventions on tremor. This study aimed to test validity and reliability of a new objective measurement for upper limb tremor in people with multiple sclerosis. Method Twelve participants with multiple sclerosis who self-reported tremor were observed performing standardised tasks. Validity and reliability of a new method to detect tremor from wrist movement was established against occupational therapist observation of tremor (FAHN). Concurrent validity of severity (displacement) of tremor was assessed. Responsiveness to change in tremor characteristics was explored in a sub-set of participants using weighted wrist-cuffs. Results The new method correctly predicted 98.2% of tremor cases identified by the occupational therapist, with high sensitivity (0.988) and specificity (0.976). Calculated displacement of tremor correlated with FAHN tremor severity scores moderately (rs = .452, p = .004). The new measure was responsive to changes in tremor characteristics due to change in weight of wrist-cuffs. Conclusion The new method of characterising tremor in those with multiple sclerosis demonstrated excellent validity and reliability in relation to tremor identified by an occupational therapist, and could provide valuable objective insight into the efficacy of interventions. </jats:sec

Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury

Background: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention.; Method: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. Results: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard).; Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05).; Conclusion: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences.Peer ReviewedPostprint (published version

A perspective on the control of FES-supported standing

This special section is about the control of electrical stimulators to restore standing functions to paraplegics. It addresses several important topics regarding the interactions of the intact central nervous systems (CNS) with the artificial control system. The topics are as follows: how paraplegics use their arms to help themselves stand up with functional electrical stimulation (FES); the user-driven artificial control of FESsupported standing up; a controller which is promising for the control of sitting down; the application of reinforcement machine learning for the controllers of standing up; arms-free\ud standing with voluntary upper body balancing and artificially controlled ankle stiffness; and cognitive feedback in balancing. This Commentary introduces the papers in this section and relates them to earlier research

Autonomy Infused Teleoperation with Application to BCI Manipulation

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments

Feature Analysis for Classification of Physical Actions using surface EMG Data

Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithm

Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury

Objective. The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI). Approach. Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine. Main results. Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results. Significance. Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intentionPeer ReviewedPostprint (author's final draft