Name: a human-machine-interface integrating low-cost sensors with a neuromuscular electrical stimulation system for post-stroke balance rehabilitation
Uploaded: 2016-04-12T13:00:00
Description: Watch this Scientific Journal Video about A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation at JoVE.com

Research conducted within the context of the Joint targeted Program in Information and Communication Science and Technology - ICST, supported by CNRS, Inria, and DST, under CEFIPRA&#39;s umbrella. The authors would like to acknowledge the support of students, specifically Rahima Sidiboulenouar, Rishabh Sehgal, and Gorish Aggarwal, towards development of the experimental setup.

Note: The HMI software pipeline was developed based on freely available open-source software and off-the-shelf low-cost video game sensors (details available at: https://team.inria.fr/nphys4nrehab/software/ and https://github.com/NeuroPhys4NeuroRehab/JoVE). The HMI software pipeline is provided for data collection during a modified functional reach task (mFRT)21 in a VR based gaming platform for visuomotor balance therapy (VBT)8.
Figure 2a shows the diagn...

<ol>
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A simple-to-use, clinically valid low-cost tool for movement and balance therapy will be a paradigm shift for neurorehabilitation in a low-resource setting. It is likely to have a very high societal impact since neurological disorders like stroke will dramatically increase in future due to aging world population2. There is, therefore, a pressing need to leverage cyber physical systems where the ability to customize, monitor, and support neuro-rehabilitation at remote sites has recently become possible with the...

An episode of neurological dysfunction caused by focal cerebral, spinal, or retinal infarction is called stroke1. Stroke is a global health problem and fourth leading cause of disability worldwide1. In countries like India and China, the two most populous nations of the world, neurologic disability due to stroke is being labeled as hidden epidemic2. One of the most common medical complications after a stroke are falls with a reported incidence of up to 73% in the first year post-stroke3. The post-stroke fall is multifactorial and includes both spinal and supraspinal factors like balance and visuospatial neglect4. A review by Geurts and colleagues5 identified 1) multi-directionally impaired maximal weight shifting during bipedal standing, 2) slow speed, 3) directional imprecision, and 4) small amplitudes of single and cyclic sub-maximal frontal plane weight shifts as the balance factors for fall risk. The consequent impact on activities of daily living can be significant since prior works have shown that balance is associated with ambulatory ability and independence in gross motor function5,6. Moreover, Geurts and colleagues5 suggested that supraspinal multisensory integration (and muscle coordination7) in addition to muscle strength is critical for balance recovery which is lacking in current protocols. Towards multisensory integration, our hypothesis8 on volitionally driven non-invasive electrotherapy (NMES/SES) is that this adaptive behavior can be shaped and facilitated by modulating active perception of sensory inputs during NMES/SES-assisted movement of the affected limb such that the brain can incorporate this feedback into subsequent movement output by recruiting alternate motor pathways9, if needed.
To achieve volitionally driven NMES/SES-assisted balance training in a resource-poor setting, a low-cost human-machine-interface (HMI) was developed by leveraging available open-source software and recent advances in off-the-shelf video game sensor technology for visual-auditory biofeedback. NMES involves coordinated electrical stimulation of nerves and muscles that has been shown to improve muscle strength and reduce spasticity10. Also, SES involves stimulation of sensory nerves with electrical current to evoke sensations where preliminary published work11 showed that subsensory stimulation applied over the tibialis anterior muscles alone is effective in attenuating postural sway. Here, the HMI will make possible sensory-motor integration during interactive post-stroke balance therapy where volitionally-driven NMES/SES for the ankle muscles will act as a muscle amplifier (with NMES) as well as enhance afferent feedback (with SES) to assist healthy ankle strategies12,13,14 to maintain upright stance during postural sways. This is based on the hypothesis presented in Dutta et al.8 that an increased corticospinal excitability of relevant ankle muscles effected through non-invasive electrotherapy may lend to an improved supraspinal modulation of ankle stiffness. Indeed, prior work has shown that NMES/SES elicits lasting changes in corticospinal excitability, possibly as a result of co-activating motor and sensory fibers15,16. Moreover, Khaslavskaia and Sinkjaer17 showed in humans that concurrent motor cortical drive present at the time of NMES/SES enhanced motor cortical excitability. Therefore, volitionally-driven NMES/SES may induce short-term neuroplasticity in spinal reflexes (e.g., reciprocal Ia inhibition17) where corticospinal neurons that project via descending pathways to a given motoneuron pool can inhibit the antagonistic motoneuron pool via Ia-inhibitory interneurons in humans18, as shown in Figure 1, towards an operant conditioning paradigm (see Dutta et al.8).
<img alt="Figure 1" src="/files/ftp_upload/52394/52394fig1.jpg" /> 
Figure 1: The concept (details at Dutta et al.21) underlying interactive human machine interface (HMI) to drive the center of pressure (CoP) cursor to the cued target to improve ankle muscle coordination under volitionally driven neuromuscular electrical stimulation (NMES)-assisted visuomotor balance therapy. EEG: electroencephalography, MN: &#945;-motoneuron, IN: Ia-inhibitory interneuron, EMG: electromyogram, DRG: dorsal root ganglion. Reproduced from8 and37&#8203;.&#160;<a href="https://www.jove.com/files/ftp_upload/52394/52394fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The antero-posterior (A-P) displacements in center of mass (CoM) are performed by ankle plantarflexors (such as medial gastrocnemius and soleus muscles) and dorsiflexors (such as the anterior tibial muscle) while medio-lateral (M-L) displacements are performed by ankle invertors (such as the anterior tibial muscle) and evertors (such as peroneus longus and brevis muscles). Consequently, stroke-related ankle impairments including weakness of the ankle dorsiflexor muscles and increased spasticity of the ankle plantarflexor muscles lead to impaired postural control. Here, agility training programs6 can be leveraged in a virtual reality (VR) based gaming platform that challenge dynamic balance where tasks are progressively increased in difficulty which may be more effective than static stretching/weight-shifting exercise program in preventing falls6. For example, subjects can perform volitionally driven NMES/SES assisted A-P and M-L displacements during a dynamic visuomotor balance task where the difficulty can be progressively increased to ameliorate post-stroke ankle-specific control problems in weight shifting during bipedal standing. Towards volitionally driven NMES/SES assisted balance therapy in a resource-poor setting, we present a low-cost HMI for Mobile Brain/Body Imaging (MoBI)19, towards visual-auditory biofeedback which can also be used for data collection from low-cost sensors for offline data exploration in MoBILAB (see Ojeda et al.20).

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>NMES stimulator</td>
			<td>Vivaltis, France</td>
			<td>PhenixUSBNeo</td>
			<td>NMES stimulator cum EMG sensor (Figure 2b)</td>
		</tr>
		<tr>
			<td>Balance Board</td>
			<td>Nintendo, USA</td>
			<td>Wii Balance Board</td>
			<td>Balance Board (Figure 2b)</td>
		</tr>
		<tr>
			<td>Motion Capture</td>
			<td>Microsoft, USA</td>
			<td>XBOX-360 Kinect</td>
			<td>Motion Capture (Figure 2b)</td>
		</tr>
		<tr>
			<td>Eye Tracker&#160;</td>
			<td>Eye Tribe</td>
			<td>The Eye Tribe</td>
			<td>SmartEye Tracker (Figure 2a)</td>
		</tr>
		<tr>
			<td>EEG Data Acquisition System</td>
			<td>Emotiv, Australia</td>
			<td>Emotiv Neuroheadset</td>
			<td>Wireless EEG headset (Figure 2b)</td>
		</tr>
		<tr>
			<td>EEG passive electrode</td>
			<td>Olimex</td>
			<td>EEG-PE</td>
			<td>EEG passive electrode for EOG and references (6 in number)(Figure 2b)</td>
		</tr>
		<tr>
			<td>EEG active electrode</td>
			<td>Olimex</td>
			<td>EEG-AE</td>
			<td>EEG active electrode (10 in number)(Figure 2b)</td>
		</tr>
		<tr>
			<td>Computer with PC monitor</td>
			<td>Dell</td>
			<td></td>
			<td>Data processing and visual feedback (Figure 2)</td>
		</tr>
		<tr>
			<td>Softwares, EMG electrodes, NMES electrodes, and cables</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

a human-machine-interface integrating low-cost sensors with a neuromuscular electrical stimulation system for post-stroke balance rehabilitation

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to reorganize its structure, function and connections as a response to intrinsic or extrinsic stimuli is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Beneficial neuroplastic changes may be facilitated with non-invasive electrotherapy, such as neuromuscular electrical stimulation (NMES) and sensory electrical stimulation (SES). NMES involves coordinated electrical stimulation of motor nerves and muscles to activate them with continuous short pulses of electrical current while SES involves stimulation of sensory nerves with electrical current resulting in sensations that vary from barely perceivable to highly unpleasant. Here, active cortical participation in rehabilitation procedures may be facilitated by driving the non-invasive electrotherapy with biosignals (electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG)) that represent simultaneous active perception and volitional effort. To achieve this in a resource-poor setting, e.g., in low- and middle-income countries, we present a low-cost human-machine-interface (HMI) by leveraging recent advances in off-the-shelf video game sensor technology. In this paper, we discuss the open-source software interface that integrates low-cost off-the-shelf sensors for visual-auditory biofeedback with non-invasive electrotherapy to assist postural control during balance rehabilitation. We demonstrate the proof-of-concept on healthy volunteers.

Figure 4 shows the eye gaze features that were extracted offline for the quantification of an able-bodied performance during a smooth pursuit task. The following features were extracted as shown in Table 1:
Feature 1 = percentage deviation between target stimulus position and the centroid of participant&#39;s fixation points when the stimulus is changing position in the horizontal direction....

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A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing ...

The overall goal of this procedure is to apply video game sensor technology towards a low cost human machine interface for balance rehabilitation. This is accomplished by first setting up mobile brain body imaging, or MoBI equipment, and software. The second step is to place MoBI sensors on the body to capture physiological data.Next, an eye tracker is used to evaluate post stroke visual pursuit eye movements. Eye tracking will show that gaze related indices can help in delineating post-stroke oculomotor deficits, which may be relevant for planning visuomotor balance therapy. Then, sensors for neuromuscular electrical stimulation, or NMES, assisted in visuomotor balance therapy, under MoBI, are calibrated.The final step is to collect multi-sensor data from low cost sensors during visuomotor balance therapy. We are extending this technology for our home base rehabilitation for stroke patients who suffer from balance loss. Our system includes gaze based, prognostic dual, and one gaze spectrum as an important ingredient in visuomotor balance therapy.So this paradigm shifting technology captures how the visual system, the motor system, and the balance systems walks together after stroke and tries to rehabilitate them through training. So in principle, this technic can be used in other conditions, like Autism Spectrum Disorder, Parkinsonism, Traumatic Brain Injury, and also psychomotor disturbance due to depression. All can be rehabilitated in this training system.Begin by obtaining a projection screen to display the visual bio feedback. Adjust the height so that the center of the screen will be at the subject's eye level. Then place the motion caption sensor in front of the projection screen and aim it at the volume of motion capture.Confirm that the volume of motion capture is 1.5 meters to 2.5 meters in front of the motion capture sensor. Next, place the balance board on the floor, about 2.0 meters away from the motion capture sensor. Leave enough room around the balance board to ensure full body movement.To begin sensor placement for electromyography, or EMG, in the NMES system, ask the subject to sit on a chair. Place the EMG and NMES electrodes bilaterally on the Medial Gastrocnemius and Tibialis Anterior muscles of the subject. Then, connect the electrodes to the wireless NMES simulator system.Next, place the electroencephalogram, or EEG cap, on the subject's head by following the international 10-20 system. Use conductive paste to adhere the electrodes, seen here to the scalp, before connecting them to the wireless EEG headset. Place two passive EEG electrodes above and below one eye to capture the vertical electrooculogram, or EOG.Add another two to the outer campus of each eye to capture the horizontal EOG. Finally, place two passive EEG electrodes on the subject's earlobes to serve as EEG reference electrodes. Begin by placing the eye tracker just below the visual feedback computer monitor and connect it to the visual feedback computer using a USB 3 port.Then, ask the subject to sit at the desk at a distance of 50 centimeters from the eye tracker, adjust the visual feedback computer monitor so that the subject's eyes are at the center of the monitor. In the visual feedback computer, open the eye tribe server and eye tribe when-you-eye to ensure whether the subject's eyes are detected by the Gooey and to perform the eye tracker calibration routine subsequently. Next, ask the subject to look straight at the visual feedback computer monitor for visual cues.Run the eye tracker calibration routine by clicking on the calibrate tab in the Gooey. Run the visual underscore stimulus dot exc in the smart eye folder to execute the virtual reality based interface. Subsequently, run the smart eye dot exc program present in the smart eye folder to acquire the subject's eye gaze data that is synchronized with the virtual reality based task.Ask the subject to follow the fixed and moving visual stimuli at various positions on the visual feedback computer screen in order to evaluate post stroke pursuit eye movements. First, connect the eye tracker and balance board sensors to the visual feedback computer. Turn on the balance board, or BB sensor.Press the button on the BB sensor to make the remote discoverable in the menu. Click on the show or hide icon in the system's task bar, and click bluetooth device icon. Then, click on the added device option and pair the BB as a bluetooth device without using the code to the visual feedback computer.Once the balance board is connected to the visual feedback computer, open the VBT folder, and run the We BB interface dot m file to establish Matt lab BB interface. Have the subject stand on the balance board. Click OK to begin the calibration, then enter the subject's weight and click OK again.Next, connect and turn on the motion capture sensor to the data acquisition computer and ensure that it has fully booted, which is indicated by the green light. Open the LSL folder and start Mocap software to begin streaming of the motion capture sensor data. Turn on the EEG and EOG data acquisition systems connected to the data acquisition computer.To do this, first open the LSL folder and open open vibe acquisition server with LSL dot cmd. Finally, select emotive EPOC from the menu for EEG and EOG and configure the module by clicking on driver properties. Then, click on connect and click on play to start the acquisition server.Begin by asking the subject to stand on the balance board and attach a safety harness. Set a minimum baseline NMES level for upright standing by setting the stimulation frequency at 20 hurtz. Then, increase the pulse width and current level in the NMES software installed in the data acquisition computer until upright standing has been achieved.For NMES assisted VBT, ask the subject to sit on a chair, facing the motion capture sensor with their feet on the balance board. Run the calib sensors program in the data collect folder in order to collect multi sensor calibration data. Finally, ask the subject to perform self initiated maximal reach movements in different directions that affect center of mass location on the visual feedback.Begin by running the collect baseline program in the data collect folder to collect baseline resting state, eyes open, multi sensor data. To do this, ask the subject to stand still for two minutes and look straight at the center of mass location on the visual feedback. Then, run the collected VBT program in the data collect folder to collect sensor data during VBT.Connect the visual feedback computers video output to the projection screen and run the smart IVRT tasks dot exe file in the VBT folder in the visual feedback computer to launch the smart IVRT tasks Gooey. Then, ask the subject to pay attention to the audio and visual prompt on the computer screen and steer the cursor as fast as possible towards randomly presented peripheral targets. Following the Move Phase, ask the subject to hold the cursor at the target location for one second.Following the peripheral hold phase, ask the subject to return back to the central hold position while the system acquires the subject's two d coordinates of center of pressure and the two d coordinates of the gaze data. The eye gaze features were collected with the eye tracker to quantify a subjects performance during a smooth pursuit task for later comparison with post stroke VBT data. Then, for VBT, the protocol used to modify functional reach task to quantify the subject's ability to volitionally shift their center of pressure position as quickly as possible without losing balance, while cued with visual feedback.Here, EOG data shows that during VBT, the FD ratio, or ratio of fixation duration on the target, and the fixation duration on the cursor, increased while the normalized mean squared error decreased. While attempting this procedure it is very important to consider the safety and the comfort of the stroke patient. We expect that a virtual reality based balance rehabilitation system will be potent to address balance disorders of stroke patients.So following this procedure, other additional clinical measures, like the clinical measure of balance and mobility can be far from to comprehensively know how quantitive measures compared to other standard clinical parameters so that we can get a comprehensive picture of functional deficit of a stroke patient and we can comprehensively address this issue in the rehabilitation.

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