Upper limb movement quality measures: comparing IMUs and optical motion capture in stroke patients performing a drinking task.

Introduction: Clinical assessment of upper limb sensorimotor function post-stroke is often constrained by low sensitivity and limited information on movement quality. To address this gap, recent studies proposed a standardized instrumented drinking task, as a representative daily activity combining different components of functional arm use. Although kinematic movement quality measures for this task are well-established, and optical motion capture (OMC) has proven effective in their measurement, its clinical application remains limited. Inertial Measurement Units (IMUs) emerge as a promising low-cost and user-friendly alternative, yet their validity and clinical relevance compared to the gold standard OMC need investigation. Method: In this study, we conducted a measurement system comparison between IMUs and OMC, analyzing 15 established movement quality measures in 15 mild and moderate stroke patients performing the drinking task, using five IMUs placed on each wrist, upper arm, and trunk. Results: Our findings revealed strong agreement between the systems, with 12 out of 15 measures demonstrating clinical applicability, evidenced by Limits of Agreement (LoA) below the Minimum Clinically Important Differences (MCID) for each measure. Discussion: These results are promising, suggesting the clinical applicability of IMUs in quantifying movement quality for mildly and moderately impaired stroke patients performing the drinking task.

Kinematic effects of inertia and friction added by a robotic knee exoskeleton after prolonged walking.

The capabilities of robotic gait assistive devices are ever increasing; however, their adoption outside of the lab is still limited. A critical barrier for the functionality of these devices are the still unknown mechanical properties of the human leg during dynamic conditions such as walking. We built a robotic knee exoskeleton to address this problem. Here, we present the effects of our device on the walking pattern of four subjects. We assessed the effects after a short period of acclimation as well as after a 1.5h walking protocol. We found that the knee exoskeleton decreased (towards extension) the peak hip extension and peak knee flexion of the leg with the exoskeleton, while minimally affecting the non-exoskeleton leg. Comparatively smaller changes occurred after prolonged walking. These results suggest that walking patterns attained after a few minutes of acclimation with a knee exoskeleton are stable for at least a couple of hours.

Sub-processes of motor learning revealed by a robotic manipulandum for rodents.

Rodent models are widely used to investigate neural changes in response to motor learning. Usually, the behavioral readout of motor learning tasks used for this purpose is restricted to a binary measure of performance (i.e. "successful" movement vs. "failure"). Thus, the assignability of research in rodents to concepts gained in human research - implying diverse internal models that constitute motor learning - is still limited. To solve this problem, we recently introduced a three-degree-of-freedom robotic platform designed for rats (the ETH-Pattus) that combines an accurate behavioral readout (in the form of kinematics) with the possibility to invasively assess learning related changes within the brain (e.g. by performing immunohistochemistry or electrophysiology in acute slice preparations). Here, we validate this platform as a tool to study motor learning by establishing two forelimb-reaching paradigms that differ in degree of skill. Both conditions can be precisely differentiated in terms of their temporal pattern and performance levels. Based on behavioral data, we hypothesize the presence of several sub-processes contributing to motor learning. These share close similarities with concepts gained in humans or primates.

Activity classification based on inertial and barometric pressure sensors at different anatomical locations.

Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.

Silicon photomultipliers for improved detection of low light levels in miniature near-infrared spectroscopy instruments.

Silicon photomultipliers are novel solid state photodetectors that recently became commercially available. The goal of this paper was to investigate their suitability for low light level detection in miniaturized functional near-infrared spectroscopy instruments. Two measurement modules with a footprint of 26×26 mm(2) were built, and the signal-to-noise ratio was assessed for variable source-detector separations between 25 and 65 mm on phantoms with similar optical properties to those of a human head. These measurements revealed that the signal-to-noise ratio of the raw signal was superior to an empirically derived design requirement for source-detector separations up to 50 mm. An arterial arm occlusion was also performed on one of the authors in vivo, to induce reproducible hemodynamic changes which confirmed the validity of the measured signals. The proposed use of silicon photomultipliers in functional near-infrared spectroscopy bears large potential for future development of precise, yet compact and modular instruments, and affords improvements of the source-detector separation by 67% compared to the commonly used 30 mm.

Real-time fMRI neurofeedback: progress and challenges.

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Neuroscience robotics to investigate multisensory integration and bodily awareness.

Humans experience the self as localized within their body. This aspect of bodily self-consciousness can be experimentally manipulated by exposing individuals to conflicting multisensory input, or can be abnormal following focal brain injury. Recent technological developments helped to unravel some of the mechanisms underlying multisensory integration and self-location, but the neural underpinnings are still under investigation, and the manual application of stimuli resulted in large variability difficult to control. This paper presents the development and evaluation of an MR-compatible stroking device capable of presenting moving tactile stimuli to both legs and the back of participants lying on a scanner bed while acquiring functional neuroimaging data. The platform consists of four independent stroking devices with a travel of 16-20 cm and a maximum stroking velocity of 15 cm/s, actuated over non-magnetic ultrasonic motors. Complemented with virtual reality, this setup provides a unique research platform allowing to investigate multisensory integration and its effects on self-location under well-controlled experimental conditions. The MR-compatibility of the system was evaluated in both a 3 and a 7 Tesla scanner and showed negligible interference with brain imaging. In a preliminary study using a prototype device with only one tactile stimulator, fMRI data acquired on 12 healthy participants showed visuo-tactile synchrony-related and body-specific modulations of the brain activity in bilateral temporoparietal cortex.

Model-based attenuation of movement artifacts in fMRI.

Behavioral analysis of multi-joint arm reaching has allowed important advances in understanding the control of voluntary movements. Complementing this analysis with functional magnetic resonance imaging (fMRI) would give insight into the neural mechanisms behind this control. However, fMRI is very sensitive to artifacts created by head motion and magnetic field deformation caused by the moving limbs. It is thus necessary to attenuate these motion artifacts in order to obtain correct activation patterns. Most algorithms in literature were designed for slow changes of head position over several brain scans and are not very effective on data when the movement is of duration below the resolution of a brain scan. This paper introduces a simple model-based method to remove motion artifacts during short duration movements. The proposed algorithm can account for head movement and field deformations due to movements within and outside of the scanner's field of view. It uses information from the experimental design and subject kinematics to focus the artifact attenuation in time and space and minimize the loss of uncorrupted data. Applications of the algorithm on arm reaching experimental data obtained with blocked and event-related designs demonstrate attenuation of motion artifacts with minimal effect on the brain activations.

Accurate real-time feedback of surface EMG during fMRI.

Real-time acquisition of EMG during functional MRI (fMRI) provides a novel method of controlling motor experiments in the scanner using feedback of EMG. Because of the redundancy in the human muscle system, this is not possible from recordings of joint torque and kinematics alone, because these provide no information about individual muscle activation. This is particularly critical during brain imaging because brain activations are not only related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is corrupted by large artifacts induced by the varying magnetic fields and radio frequency (RF) pulses in the scanner. Methods proposed in literature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We describe an acquisition system and algorithm that enables real-time acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated. Although this decreases the power content of the EMG, this method provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, like actual EMG, very well correlated with joint torque and can thus be used for real-time visual feedback during functional studies.

Multi-joint arm movements to investigate motor control with FMRI.

Performing multi-joint arm movements in controllable dynamic environments during functional magnetic resonance imaging (fMRI) could provide important insights into the brain mechanisms involved in human motor control and related dysfunctions. In order to obtain useful data, these movements must be possible and comfortable for the subject within the narrow bore of the scanner and should not create any movement artifacts in the image. We found that commonly studied arm movements involving the shoulder create movement artifacts, and investigated alternative multijoint arm movements within a mock-up of an MR scanner. We selected movements involving the elbow and wrist joints, with an extension attached to the hand, and propose a dedicated kinematic structure using the MR compatible actuators we have previously developed.

A Haptic Interface Based on Potential Mechanical Energy to Investigate Human Motor Control using fMRI.

This paper describes a mechanical interface to use in conjunction with fMRI, in order to infer the brain mechanisms of human motor learning. Innovative mechanical concepts based on gravity and elastic forces were used to generate typical stable and unstable dynamic interactions at the hand during multijoint arm movements. Two designs were retained and implemented from MR compatible materials. The first uses a spring constrained between two specially designed surfaces and the other a capstan to transform the force induced by a groove carved on a shaft. These two degree-of-freedom mechanical interfaces have been constructed and tested. The use of a capstan mechanism was found to be limited by excessive friction, however, the method using a machined surface provides a simple and effective interface to investigate human motor control.