A non-invasive, wearable, magnetic brain stimulation device could improve motor function in stroke patients, according to preliminary late breaking science presented today at the American Stroke Association’s International Stroke Conference 2020.
The conference, Feb. 19-21 in Los Angeles, is a world premier meeting for researchers and clinicians dedicated to the science of stroke and brain health.
In an initial, randomized, double-blind, sham-controlled clinical trial of 30 chronic ischemic stroke survivors, a new wearable, multifocal, transcranial, rotating, permanent magnet stimulator, or TRPMS, produced significant increases in physiological brain activity in areas near the injured brain, as measured by functional MRI.
“The robustness of the increase in physiological brain activity was surprising.
With only 30 subjects, a statistically significant change was seen in brain activity,” said lead study author David Chiu, M.D., director of the Eddy Scurlock Stroke Center at Houston Methodist Hospital in Texas.
“If confirmed in a larger multicenter trial, the results would have enormous implications. This technology would be the first proven treatment for recovery of motor function after chronic ischemic stroke.”
Magnetic stimulation of the brain was previously investigated to promote recovery of motor function after stroke.
The stimulation may change neural activity and induce reorganization of circuits in the brain. Researchers introduced a new wearable stimulator.
Stroke survivors who had weakness on one side of their body at least three months post-stroke were enrolled in a preliminary study to evaluate safety and efficacy of the device.
Half of the patients were treated with brain stimulation administered in twenty 40-minute sessions over four weeks.
The rest had sham, or mock, treatment. Researchers analyzed physiologic brain activity before, immediately after and one month after treatment.
Multifocal transcranial rotating permanent magnet stimulator (TRPMS). The image is credited to Blessy John.
They found that treatment was well tolerated, and there were no device-related complications. Active treatment produced significantly greater increases in brain activity: nearly 9 times higher than the sham treatment.
Although the study could not prove that the transcranial stimulator improved motor function, numerical improvements were demonstrated in five of six clinical scales of motor function, as measured by a functional MRI test.
The scales measured gait velocity, grip strength, pinch strength, and other motor functions of the arm. The treatment effects persisted over a three-month follow-up.
The researchers believe the study results are a signal of possible improved clinical motor function after magnetic brain stimulation for patients after stroke, which will need to be confirmed in a larger, multicenter trial.
Funding: The trial was funded by a grant from the Houston Methodist Research Institute Translational Research Initiative and to Seraya Medical, LLC. A multicenter trial sponsored by the technology patent holder to Seraya Medical LLC is currently planned.
Stroke is one of the leading causes of disability worldwide , with a global prevalence estimated at 42.4 million in 2015 . Stroke results in permanent motor disabilities in 80% of cases . During the acute and subacute stages (< 6 months after stroke ), patients receive rehabilitation therapies at specialized healthcare centers, consisting of an iterative process involving impairment assessments, goal definition, intervention, and progress evaluation .
After being discharged from the rehabilitation center (i.e. after entering the chronic stage, e.g., 6 months after stroke), 65% of patients are unable to integrate affected limbs into everyday-life activities , showing a need for further treatment.
Phrased differently, the rehabilitative process after stroke depends on the effective assessment of motor deficit and congruent allocation to treatment (diagnostics), accurate appraisal of treatment effects (recovery/adaptation evaluation), and prolonged treatment for continuous recovery during the chronic stage (extended training).
Each of these three aspects present practical challenges. Assigned treatments depend on the assessed early-stage disability . A variety of assessment scales exist to evaluate motor impairment after stroke, designed to capture aspects such as joint range of motion (ROM), synergistic execution of movements, reaching and grasping capabilities, object manipulation, etc. .
These assessments are normally applied by specialized medical personnel, which entails certain variability between assessments . Besides consistency in repeated measurements, some scales like the Fugl-Meyer assessment (FMA) , are unable to capture the entire spectrum of motor function in patients due to limited sensitivity or ceiling effects .
In addition to thorough standardized assessment scales, progress in patients is observable during the execution of activities of daily living (e.g., during occupational therapy sessions). Nevertheless, task completion not always reflects recovery, as patients often adopt different synergistic patterns to compensate for lost function , and such behavior is not always evident.
Main provision of rehabilitation therapies occurs at hospitals and rehabilitation centers. Evidence of enhanced recovery related to more extensive training has been found , but limited resources at these facilities often obstruct extended care during the chronic stage. This calls for new therapeutic options allowing patients to train intensively and extensively after leaving the treatment center, while ensuring the treatment’s quality, effectiveness and safety.
Wearable sensors used during regular assessments can reduce evaluation times and provide objective, quantifiable data on the patients’ capabilities, complementing the expert yet subjective judgement of healthcare specialists. These recordings are more objective and replicable than regular observations.
They have the potential of reducing diagnostic errors affecting the choice for therapies and their eventual readjustment. Additional information (e.g., muscle activity) extracted during the execution of multiple tasks can be used to better characterize motor function in patients, allowing for finer stratification into more specific groups, which can then lead to better targeted care (i.e. personalized therapies).
These devices also make it possible to acquire data unobtrusively and continuously, which enables the study of motor function while patients perform daily-life activities. Further, the prospect of remotely acquiring data shows promise in the implementation of independent rehabilitative training outside clinics, allowing patients to work more extensively towards recovery.
The objective of this review is to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity, aiming to present a roadmap for translating these technologies from “bench to bedside”.
We selected articles based on their reports about tests conducted with actual stroke patients, with the exception of conductive elastomer sensors, on which extensive research exists without tests in patients. In the section “Wearable devices used in stroke patients”, we summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. In the “Discussion” section, we present suggestions concerning data acquisition and processing, as well as opportunities arising in this field, to guide future studies performed by clinicians and engineers alike.
Wearable devices used in stroke patients
Recent availability of ever more compact, robust and power-efficient wearable devices has presented research and development groups in academia and industry with the means of studying and monitoring activities performed by users on a daily basis.
Over the past years, multiple research groups have worked towards a reliable, objective and unobtrusive way of studying human movement. From the array of sensors and devices created, a few have gained popularity in time due to their practicality. The next subsections will focus on the wearable devices most frequently used in the study of human motion, with special emphasis on monitoring of upper limbs in stroke patients.
Inertial measurement units (IMUs)
Inertial measurement units (IMUs) are devices combining the acceleration readings from accelerometers and the angular turning rate detection of gyroscopes . Recent versions of such devices are equipped with a magnetometer as well, adding an estimation of the orientation of the device with respect to the Earth’s magnetic field .
A general description of how inertial data are used to extract useful information from these devices is offered by Yang and Hsu . High-end IMUs used for human motion tracking, such as the “MTw Awinda” sensor (Xsens®, Enscheda, Overijssel, The Netherlands) , acquire data at sampling rates as high as 1 kHz (sensitivities of ±2000 deg/s, ±160 m/s2, ±1.9 G). More affordable sensors (e.g. “MMR” (mbientlab Inc.®, San Francisco, California, USA) ) stream data at 100 Hz (max sensitivities of ±2000 deg/s, ±16 g, 13 G).
The necessary sampling rate depends on the application, and must be defined such that aliasing is avoided (i.e. Nyquist rate, 2 times the frequency of the studied phenomenon). Figure 1 shows an example of motion tracking using these devices.
Stroke is a frequent disorder that often results in long-lasting loss of motor functions. After stroke, the rehabilitative process relies on three main elements: 1. Diagnosis, in which clinicians use standardized scales to estimate maximum recovery for every patient  and assign them to rehabilitation therapies accordingly . 2.
Evaluation of recovery or adaptation, during which clinicians assess the extent up to which patients can perform activities of daily living. 3. Extended training, necessary for patients with persistent motor impairment after entering the chronic stage.
Conventional motor assessment is vulnerable to biases derived from measurement errors  and ceiling effects , whereas compensatory strategies frequently adopted by patients while performing different tasks  can complicate the appraisal of recovery.
Therapy and training provision at healthcare centers is limited to available resources and restricted by its corresponding costs, which obstructs prolonged rehabilitative training for patients who do not recover fully within the first months after stroke.
A promising option to assess stroke patients objectively resides in the use of wearable technology. As high-end sensors become more accessible, more reliable and less obtrusive, the chance of acquiring relevant data during patients’ training or daily routines gets easier. A variety of wearable sensors (e.g. [29, 49, 59, 60, 62, 98]) have been used to assess several aspects of motor performance in stroke patients, going from motor impairment to more subtle forms of behavior, such as limb neglect.
In the present paper, we seek to compare different setups with the intention of finding the most promising candidates for different applications. There are four main wearable sensors used in the study of stroke: IMUs, EMG, potentiometers/encoders and flexible sensors.
IMUs allow measuring changes in acceleration, inclination and orientation unobtrusively. Wireless, energy-efficient  transmission of data characterizing these sensors enables whole-body recordings through sensor networks , supporting this sensors’ candidacy for movement tracking [28, 35, 49].
Several groups have used IMUs with diagnostic purposes [19–27] and to assess the execution of daily-life activities [19, 28–33]. High portability and accessible costs further support these sensors as an option for prolonged training during the chronic stage (e.g. at home) . There are general complications inherent to the use of these devices, such as estimation errors derived from accumulated error in the calculation of orientation from angular acceleration (i.e. orientation drift ) and quantization noise . In addition, high movement variability in stroke patients, resulting from adopted compensatory muscle synergies and slower, segmented movements , complicate data characterization and comparison.
EMG wearable sensors have also been used for diagnosis [27, 62] and first attempts at extended training outside clinical environments . Monitoring the execution of activities of daily living can benefit from EMG recordings, as these sensors allow capturing differences in muscle pattern activations resulting from compensatory movements .
These sensors can complement the information obtained with IMUs. Aspects neglected by some assessment scales (e.g. FMA), such as applied force , can be derived from muscle activation as recorded with EMG. EMG sensors are susceptible to different sources of noise, which must be removed before signals can be used . Furthermore, variable placement of electrodes can also mislead estimations and affect the performance of the models used to classify measured activity.
Potentiometers and encoders are robust to noise and require little processing of signals, as the output from these sensors can be mapped directly to angular displacement (or linear, in the case of linear encoders).
The range of applications in stroke for these sensors is limited to measuring ROM of limbs, and requires mounting them on a parallel structure, such as an orthosis, limiting the degrees of freedom of measured movements. Still, their potential in extensive home-based training is clear .
The need for an orthosis disappears with the use of linear encoders  due to integration of the sensors into gloves. Nevertheless, the use of both orthoses and gloves can be difficult for patients suffering from hand spasticity, which would complicate their use at home. This problem persists whenever using flexible sensors embedded in gloves. Flexible sensors embedded in clothing could be a viable option for tracking everyday life activities, but practical issues related to washing the garments and to the large amount of wiring required still impede their regular use.
As IMU and EMG data cannot be mapped directly into the movements and actions that generated them, acquired signals must be processed differently. Depending on the objective (e.g. assign grades to movements, compare patients to healthy controls, etc.) data can either be classified using different forms of statistical processing, such as common methods applied in machine learning , or compared using algorithms like DTW [48, 49]. Built models often fail to generalize to data from highly impaired patients due to lower signal-to-noise ratio (SNR) . Further, results are hard to compare due to a lack of a unified data acquisition protocol .
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