Name: लेखक स्पॉटलाइट: एफएनआईआरएस का उपयोग करके रोबोट-असिस्टेड लोअर लिंब पुनर्वास में मस्तिष्क गतिविधि का आकलन करना
Uploaded: 2024-06-07T12:40:00
Description: Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise ...

इस अध्ययन की राय, परिकल्पना, निष्कर्ष और सिफारिशें लेखकों की हैं और जरूरी नहीं कि फंडिंग एजेंसी की राय का प्रतिनिधित्व करें। जेआरएस साओ पाउलो रिसर्च फाउंडेशन (एफएपीईएसपी, अनुदान संख्या 2021/05332-8, 2018/04654-9, 2018/21934-5 और 2023/02538-0) और तकनीकी सहायता के लिए जैक्सन सियोनेक का आभारी है। AMM और Vivax Ltda FAPESP (साओ पाउलो रिसर्च फाउंडेशन) और FINEP (ब्राजीलियाई इनोवेशन एजेंसी) के आभारी हैं। इस परियोजना को FAPESP (अनुदान संख्या 2018/09559-4) और FINEP (अनुदान संख्या 2019/09933-6) से अनुदान के साथ वित्त पोषित किया गया था।

इस अध्ययन को UNICEP (Centro Universitario Paulista) के स्थानीय एथिक्स रिव्यू बोर्ड द्वारा अनुमोदित किया गया था। सभी प्रतिभागियों ने मनुष्यों से जुड़े वैज्ञानिक अनुसंधान के संबंध में सभी संस्थागत दिशानिर्देशों और संघीय मा?...

एक निचले अंग रोबोट थेरेपी सत्र के दौरान fNIRS द्वारा मस्तिष्क सक्रियण का मूल्यांकन

इस प्रूफ-ऑफ-कॉन्सेप्ट अध्ययन में, स्वस्थ विषयों से एफएनआईआरएस डेटा का उपयोग करके मस्तिष्क सक्रियण मानचित्रण पर अनुमान लगाने की व्यवहार्यता की जांच की गई, जबकि उन्होंने निचले अंग पुनर्वास के लिए रोबो?...

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>32 inch Smart TV</td>
			<td>Samsung</td>
			<td>N/A</td>
			<td>TV connected to robot via HDMI cable</td>
		</tr>
		<tr>
			<td>8-detector silicon photodiode (SiPD) optodes for optical detection with dual tip</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>8-source optodes bundle for optical illumination with dual tip</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>Aurora acquisition software</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>Laptop Precision XPS 13</td>
			<td>Dell Technologies (Round Rock, TX, USA)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>nirsLAB fNIRS Analysis software</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>NIRSports2 fNIRS acquisition system</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td>It has two different continuous wave optics (760 and 850 nm), 8 dual-ended LED sources and 8 dual-ended active detectors.</td>
		</tr>
		<tr>
			<td>R</td>
			<td>R-project.org (open source software)</td>
			<td>https://www.r-project.org/</td>
			<td></td>
		</tr>
		<tr>
			<td>Standard cut cap, black color for up to 128 holders.</td>
			<td>Easycap GmbH (W&#246;rthsee, Germany)</td>
			<td>https://www.easycap.de/</td>
			<td></td>
		</tr>
		<tr>
			<td>Vivax Assistive Rehabilitation Machine (ARM)&#160;</td>
			<td>Vivax Ltda (S&#227;o Paulo, Brazil)</td>
			<td>https://vivaxbr.com/home/</td>
			<td>It is a portable robot designed to deliver lower limb rehabilitation. It has a 3D reachable workspace and is compact and light, weighing about 35 lb., which makes it easy to transport and to install.&#160;</td>
		</tr>
	</tbody>
</table>

an experiment using functional near-infrared spectroscopy and robot-assisted multi-joint pointing movements of the lower limb

स्ट्रोक हर साल दुनिया भर में लगभग 17 मिलियन व्यक्तियों को प्रभावित करता है और दीर्घकालिक विकलांगता का एक प्रमुख कारण है। रोबोटिक थेरेपी ने स्ट्रोक रोगियों को खोए हुए मोटर कार्यों को पुनः प्राप्त करने में मदद करने का वादा दिखाया है। मोटर रिकवरी कैसे होती है, इसकी समझ बढ़ाने के लिए एक संभावित एवेन्यू स्वस्थ व्यक्तियों में चिकित्सा द्वारा लक्षित आंदोलनों के दौरान मस्तिष्क सक्रियण का अध्ययन करना है। कार्यात्मक निकट-इन्फ्रारेड स्पेक्ट्रोस्कोपी (एफएनआईआरएस) मोटर फ़ंक्शन के तंत्रिका आधार की जांच के लिए एक आशाजनक न्यूरोइमेजिंग तकनीक के रूप में उभरा है। इस अध्ययन का उद्देश्य स्वस्थ विषयों में जटिल निचले अंग आंदोलनों के एफएनआईआरएस तंत्रिका सहसंबंधों की जांच करना है। प्रतिभागियों को मोटर पुनर्वास के लिए रोबोटिक डिवाइस का उपयोग करके 6 मिनट के लिए आराम और आंदोलन के चक्र करने के लिए कहा गया था। कार्य को कंप्यूटर स्क्रीन पर प्रदर्शित लक्ष्यों को इंगित करने के लिए समन्वित घुटने और टखने के संयुक्त आंदोलनों की आवश्यकता होती है। रोबोट द्वारा प्रदान की गई आंदोलन सहायता के विभिन्न स्तरों के साथ दो प्रयोगात्मक स्थितियों का पता लगाया गया था। परिणामों से पता चला कि एफएनआईआरएस प्रोटोकॉल ने कार्य के दौरान मोटर नियंत्रण से जुड़े मस्तिष्क क्षेत्रों का प्रभावी ढंग से पता लगाया। विशेष रूप से, सभी विषयों ने सहायक स्थिति की तुलना में नो-असिस्टेंस स्थिति के दौरान कॉन्ट्रालेटरल प्रीमोटर क्षेत्र में अधिक सक्रियण का प्रदर्शन किया। अंत में, एफएनआईआरएस निचले अंग के बहु-संयुक्त इंगित आंदोलनों से जुड़े ऑक्सीहीमोग्लोबिन एकाग्रता में परिवर्तन का पता लगाने के लिए एक मूल्यवान दृष्टिकोण प्रतीत होता है। यह शोध स्ट्रोक मोटर रिकवरी तंत्र की समझ में योगदान दे सकता है और स्ट्रोक रोगियों के लिए बेहतर पुनर्वास उपचार का मार्ग प्रशस्त कर सकता है। हालांकि, मोटर फ़ंक्शन और नैदानिक सेटिंग्स में इसके अनुप्रयोगों का अध्ययन करने में एफएनआईआरएस की क्षमता को पूरी तरह से स्पष्ट करने के लिए और शोध की आवश्यकता है।

Epidemiological data indicates that worldwide there are ~17 million new cases of stroke each year, with an increase in incidence in low- and middle-income countries1. The number of new cases is estimated to increase to 77 million by 20302. Motor impairment due to stroke often affects patient&#39;s mobility and participation in daily life activities, contributing to a low quality of life. Traditional motor rehabilitation includes manual therapy, but over the past few decades, robotic systems for rehabilitation have been developed. These systems can deliver therapy at high intensity, dose, quantifiability, reliability, repeatability, and flexibility3 and have shown potential as effective rehabilitation treatments for both acute and chronic stroke patients4,5,6. In addition to delivering therapy, robotic systems for rehabilitation can be used as evaluation tools as they can be equipped with sensors that can measure patient movement kinematic/kinetic data7,8. For upper extremity motor rehabilitation, such data has not only been proven to be useful for assessing the level of patient&#39;s motor recovery elicited by robotic therapy and served as a supplementary tool to traditional clinical assessments9,10, but it has also contributed to advancing the understanding of the process of motor recovery from stroke11,12 as well as neural control of movement and motor learning in healthy subjects3,13,14. As a result, these findings have provided a foundation for enhancing rehabilitation treatments15.
Over the last two decades, many robotic devices for lower limb neurorehabilitation have been proposed, spanning from exoskeletons that support the patient body weight during walking (e.g., over a treadmill, such as Lokomat16) to stationary robotic systems that allow the patient to exercise the ankle, knee or foot without walking (such as the Rutgers Ankle17, the High-Performance Ankle Rehabilitation Robot18, and the Gwangju Institute of Science and Technology (GIST) ankle/foot rehabilitation robot19) or active foot orthoses that are actuated exoskeletons worn by the patient to walk overground or over a treadmill (such as the Powered Gait Orthosis20&#160;and the MIT Anklebot21).&#160;See22,23,4 for a review on robots for lower limb rehabilitation.
Results of clinical studies of robotic devices for lower limb rehabilitation on stroke patients have been encouraging and have shown that these systems may improve joints&#39; Range of Motion (ROM), muscle strength, or gait, depending on the specific device and clinical protocol (see&#160;24,25&#160;for a review on the efficacy of lower limb robots for rehabilitation). While it has been postulated that robot-assisted therapy promotes neuroplastic changes, which ultimately result in improved motor abilities26, how the process of motor recovery from stroke exactly occurs and which robotic training protocols optimize the process of recovery of lower limb motor abilities, remain mostly unclear. In fact, there is a significant, growing disparity between the increasing development of rehabilitation robots (either by academic researchers or commercial entities) and the limited understanding of the neurophysiological mechanisms that underlie motor recovery4. Measurements of movement kinematics or joint torques taken with embedded sensors have contributed to quantitatively describing motor behavioral changes that occur as patients recover lower limb motor abilities27,28,29, partially filling this gap. However, the neural correlates underlying such changes have been less investigated. This is due to several reasons.
Brain functional imaging is time-consuming and sometimes difficult to complete in the context of clinical trials, which often require keeping patient burden minimal to maximize the likelihood of patient adherence to the study. This holds particularly true for individuals who have suffered a stroke, given the fact that post-stroke fatigue and muscle weakness are frequently observed30. Also, imaging modalities that are based on magnetic fields, such as functional Magnetic Resonance Imaging (fMRI), require both patient and robotic hardware to be magnet-safe.
Among non-invasive imaging modalities, functional Near-Infrared Spectroscopy (fNIRS) is an imaging technique particularly suitable for assessing areas of brain activation in subjects undergoing robotic therapy. Similarly to fMRI, fNIRS measures blood oxygenation/deoxygenation in the brain. However, unlike fMRI, fNIRS is fully compatible with robotic hardware, and it is often portable, even being usable at the bedside. Also, fNIRS has low cost and less sensitivity to motion artifacts31,32,33.
Despite its clear advantages and widespread use in many clinical settings since its first introduction in the late 70s34, only a few studies have used fNIRS to quantify brain activation associated with lower limb movements and stroke motor recovery. FNIRS studies aimed at elucidating mechanisms of neural control of movement and/or mechanisms or evaluation of motor recovery from stroke have mostly investigated single-joint movements (e.g., dorsiflexion, plantar flexion or knee extension movements35,36,37), walking38,39,40,41,42,43, or cycling44. See45&#160;for a review. Similarly, fNIRS studies on robot-assisted therapy for the lower limb have mostly focused on robot-assisted gait rehabilitation; see46&#160;for a review. A few studies have focused on using fNIRS as part of a Brain-Computer Interface (BCI) system to derive control signals for robotic devices47,48; while this research area also relies on the processing of fNIRS signals, its goal is different and mainly&#160;focused on decoding patient intentions (e.g., patients with severe motor disabilities).
The pilot study presented herein is part of an initial effort to investigate the effects of a robotic system for lower limb rehabilitation. The robot can deliver target-oriented lower limb rehabilitation that involves training in everyday multi-joint movements as well as deliver therapy to single joints (e.g., knee or ankle) of the lower limb (i.e., implement a bottom-up rehabilitation program).
The study aimed to investigate the feasibility of an experimental protocol that required the acquisition of fNIRS data during the performance of lower limb, multi-joint pointing movements. The duration of the data acquisition period in this study, which was limited to 6 min, is shorter than typical fNIRS protocols. This was a deliberate choice made with the aim of enhancing the practicality and clinical applicability of this research, particularly in patients with limited mobility or strength. Identifying fNIRS correlates of such complex multi-joint movements and gaining insights into how brain activation was modulated by robot assistance were also points of interest. For this purpose, two sessions of experiments were conducted with the same participants: one with no robot assistance and one with robot assistance. Finally, it is important to remark that this study focused on healthy subjects in order to establish a foundation for future research in terms of recording protocol feasibility and evaluation of brain activation during movements targeted by robotic therapy.
Apparatus 
A portable robot designed to deliver lower limb rehabilitation (see Figure 1) was used to conduct our experiments. The robot has a 3D reachable workspace and is compact and light, weighing about 35 lb., which makes it easy to transport and install.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66004/66004fig01.jpg" /> 
Figure 1: Experimental setup. (A) The robotic system (installed on the floor) designed for the lower limb. A volunteer is shown utilizing the interface with their right foot. (B) Support structure for the subject&#39;s foot that enables attachment to the robotic system. (C) A screenshot of the Picnic game. The objective of the game is to move the foot (green and white shoe) to the target (yellow circle). <a href="https://www.jove.com/files/ftp_upload/66004/66004fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The robotic system is designed to assist a patient in performing lower limb movements similar to those performed in everyday tasks, such as pointing or kicking. It uses interactive virtual reality games, which are displayed on a computer monitor or a television screen placed in front of the robotic device (see Figure 1). The robot-end effector is attached to the patient&#39;s lower limb (e.g., ankle), and its position is mapped to the position of a cursor on the screen. A typical game shows the patient movement targets (e.g., the object to point to or where to kick the ball).
To complete the movement task, the robot may assist the patient with a level of assistance that can range from full assistance to none. The level of robotic assistance is chosen at the beginning of each rehabilitation session based on the patient&#39;s level of motor impairment. The movements performed by the subject are used by the game to score the patient&#39;s performance and provide them feedback on their performance (e.g., ROM, number of movements, and level of robotic assistance). The games are designed to be interactive and entertaining to sustain patient interest and attention. In this study, participants played the &#34;Picnic game&#34;, in which the player had to stop the insects from reaching the towel and stealing the food (see Figure 1, bottom panel, for a screenshot).
Data acquisition was performed with a portable fNIRS acquisition system with two different continuous-wave optodes (760 nm and 850 nm), 8 dual-tip LED sources, and 8 dual-tip active detectors. The signals were acquired using a sampling rate of 10.17 Hz. A laptop was used for the calibration optimization and signal recording using a Wi-Fi network created by the fNIRS system.
A cap was used to hold the optodes in the predetermined locations. The sources and detectors were placed according to the 10-10 international EEG system in a grid spatial distribution. Each fNIRS channel was defined by a source-detector pair with inter-optode distances of approximately 30 mm. The optodes were placed over the supplementary motor, premotor, and motor areas at the locations shown in Figure 2. The total number of channels was 28, where 8 were short-distance channels that were coupled to each source using a fiber optics adapter to a single detector. Given the multiplexing setup of the hardware, it is possible to acquire short-distance information from all sources using only one detector.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66004/66004fig02.jpg" /> 
Figure 2: Montage layout using the 10-10 EEG system. The letters and numbers indicate the source/detector locations. The red and blue dots represent the source and detector optodes, respectively. The green lines represent the fNIRS channels which consist of source and detector pairs. <a href="https://www.jove.com/files/ftp_upload/66004/66004fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Experimental design 
The experiment was conducted under two distinct experimental conditions, differing in the level of assistance provided by the robot for the subject&#39;s movements. In the first condition, the robot was programmed not to provide any assistance to the subject&#39;s movements, while in the second condition, the robot controlled the subject&#39;s foot and leg movements (robot-assisted movement).
Each experiment followed a block design paradigm involving alternating cycles of a motor task (playing the game - 30 s) and resting (30 s), as illustrated in Figure 3. The starting and conclusion of each phase (play/game or rest) were visually signaled to the subject through the computer screen. During the rest phase, a message indicating a pause was displayed. Each cycle (play/game + rest) had a duration of 60 s and was repeated six times, resulting in a total runtime of 360 s (6 min).
The participants played the &#34;Picnic game&#34;, wherein the objective was to prevent insects from reaching the towel and stealing food. This game involved a sequence of lower limb movements, starting from a designated home target (initial position) and extending towards one of three outer targets before returning to the home target. On the screen, the outer targets were visually represented as animated moving insects, which the participants had to reach and step onto. There were three outer-reaching targets, each randomly presented an equal number of times, alongside a common home target for every movement. The distance that the foot needed to travel from the home target to the position of the outer targets formed an arc, approximately measuring 26 cm. The motor task required the execution of multi-joint movements, demanding coordination between knee flexion/extension, plantar flexion, and dorsiflexion movements.
The fNIRS data recordings were synchronized with the visual stimuli presented by the game to the subject through a transistor-transistor-logic (TTL) pulse generated by the robot. Pulses were generated at the outset of each phase (play/game and rest). Thus, all timing control was performed by the game, which provided visual cues (targets) to the participant to start each movement, sent TTL signals to the fNIRS system to mark the brain activity recordings, and, if required by the experiment, sent signals to the robot control system to initiate movement assistance.

लेखक स्पॉटलाइट: एफएनआईआरएस का उपयोग करके रोबोट-असिस्टेड लोअर लिंब पुनर्वास में मस्तिष्क गतिविधि का आकलन करना

कार्यात्मक निकट-अवरक्त स्पेक्ट्रोस्कोपी और निचले अंग के रोबोट-सहायता प्राप्त बहु-संयुक्त इंगित आंदोलनों का उपयोग करके एक प्रयोग

In this study, we aim to investigate the feasibility of an experimental protocol involving movements of the lower limb attached to a robotic device and simultaneous acquisition of functional near-infrared spectroscopy, fNIRS, data. Few studies have employed fNIRS to study brain activation in stroke, motor recovery, and lower limb movements, focusing mainly on single joint actions, walking, or cycling. Our study pioneers in exploring multi-joints lower limb training for target rehabilitation.Functional near-infrared spectroscopy, fNIRS, effectively measures temporal changes in brain blood oxygenation, serving as an indirect brain activity indicator, to functional MRI signals. Unlike fMRI, fNIRS is compatible with robotic hardware, portable, and more robust to motion artifacts. There are two main challenge.The first is to synchronize the robotic device movements with fNIRS data. The second is to obtain enough data to achieve the statistical power, to detect hemodynamic changes in only six minutes. Our study confirmed the feasibility of the protocol and yielded anticipated results.In the assisted condition, only two subject exhibited significant brain activation. The unassisted exercise, requiring more effort, showed a greater HbO concentration change than assisted ones, with notable difference in the contralateral PMC.

सभी छह विषयों ने दोनों प्रयोगों को पूरा किया। बिना सहायता की स्थिति में, प्रत्येक विषय द्वारा औसतन 76.67 परीक्षण (कक्षा 10.73) पूरे किए गए थे (ध्यान दें, प्रत्येक विषय के लिए, परीक्षणों की संख्या सफल पहुंच की सं?...

यह अनुमान लगाया गया है कि दुनिया भर में 6 में से 1 व्यक्ति को अपने जीवनकाल में स्ट्रोक होगा, जिससे दीर्घकालिक विकलांगता होगी, जिसका पुनर्वास तंत्र अभी भी खराब समझा जाता है। यह अध्ययन निचले अंग रोबोट थेरेपी सत्र के दौरान कार्यात्मक निकट-अवरक्त स्पेक्ट्रोस्कोपी (एफएनआईआरएस) द्वारा मस्तिष्क सक्रियण का मूल्यांकन करने के लिए एक प्रोटोकॉल का प्रस्ताव करता है।

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise ...

इस अध्ययन में, हम एक प्रयोगात्मक प्रोटोकॉल की व्यवहार्यता की जांच करना चाहते हैं जिसमें रोबोटिक डिवाइस से जुड़े निचले अंग के आंदोलनों और कार्यात्मक निकट-अवरक्त स्पेक्ट्रोस्कोपी, एफएनआईआरएस, डेटा का एक साथ अधिग्रहण शामिल है। कुछ अध्ययनों ने स्ट्रोक, मोटर रिकवरी और निचले अंग आंदोलनों में मस्तिष्क सक्रियण का अध्ययन करने के लिए एफएनआईआरएस को नियोजित किया है, जो मुख्य रूप से एकल संयुक्त क्रियाओं, चलने या साइकिल चलाने पर ध्यान केंद्रित करते हैं। लक्ष्य पुनर्वास के लिए बहु-जोड़ों के निचले अंग प्रशिक्षण की खोज में हमारा अध्ययन अग्रणी है।कार्यात्मक निकट-अवरक्त स्पेक्ट्रोस्कोपी, एफएनआईआरएस, प्रभावी रूप से मस्तिष्क रक्त ऑक्सीकरण में अस्थायी परिवर्तनों को मापता है, जो कार्यात्मक एमआरआई संकेतों के लिए अप्रत्यक्ष मस्तिष्क गतिविधि संकेतक के रूप में कार्य करता है। एफएमआरआई के विपरीत, एफएनआईआरएस रोबोट हार्डवेयर, पोर्टेबल और गति कलाकृतियों के लिए अधिक मजबूत के साथ संगत है। दो मुख्य चुनौतियां हैं।पहला fNIRS डेटा के साथ रोबोट डिवाइस आंदोलनों को सिंक्रनाइज़ करना है। दूसरा सांख्यिकीय शक्ति प्राप्त करने के लिए पर्याप्त डेटा प्राप्त करना है, केवल छह मिनट में हेमोडायनामिक परिवर्तनों का पता लगाने के लिए। हमारे अध्ययन ने प्रोटोकॉल की व्यवहार्यता की पुष्टि की और प्रत्याशित परिणाम प्राप्त किए।सहायक स्थिति में, केवल दो विषयों ने महत्वपूर्ण मस्तिष्क सक्रियण का प्रदर्शन किया। बिना सहायता के अभ्यास, जिसमें अधिक प्रयास की आवश्यकता होती है, ने सहायक लोगों की तुलना में अधिक एचबीओ एकाग्रता परिवर्तन दिखाया, जिसमें विरोधाभासी पीएमसी में उल्लेखनीय अंतर था।

an-experiment-using-functional-near-infrared-spectroscopy-robot

Research

JoVE Journal

Neuroscience

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise in helping stroke patients regain lost motor functions. One potential avenue for increasing the understanding of how motor recovery occurs is to study brain activation during the movements that are targeted by therapy in healthy individuals. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging technique for examining neural underpinnings of motor function. This study aimed to investigate fNIRS neural correlates of complex lower limb movements in healthy subjects. Participants were asked to perform cycles of rest and movement for 6 min using a robotic device for motor rehabilitation. The task required coordinated knee and ankle joint movements to point to targets displayed on a computer screen. Two experimental conditions with different levels of movement assistance provided by the robot were explored. The results showed that the fNIRS protocol effectively detected brain regions associated with motor control during the task. Notably, all subjects exhibited greater activation in the contralateral premotor area during the no-assistance condition compared to the assisted condition. In conclusion, fNIRS appears to be a valuable approach for detecting changes in oxyhemoglobin concentration associated with multi-joint pointing movements of the lower limb. This research might contribute to the understanding of stroke motor recovery mechanisms and might pave the way for improved rehabilitation treatments for stroke patients. However, further research is needed to fully elucidate the potential of fNIRS in studying motor function and its applications in clinical settings.

It is estimated that 1 in 6 individuals worldwide will have a stroke in their lifetime, causing long-term disability, whose rehabilitation mechanisms are still poorly understood. This study proposes a protocol to evaluate brain activation by functional near-infrared spectroscopy (fNIRS) during a lower limb robotic therapy session.