A specific transcranial near-infrared stimulation parameter combination targeting the motor cortex improves cortical excitability and hemodynamics

 Ask your competent? doctor; DOES THIS IMPROVE STROKE RECOVERY?  Not nebulous excitability which means nothing to survivors! Oh NO, your doctor doesn't know about this and doesn't care to find out! Get that doctor fired for incompetence, not following research!

A specific transcranial near-infrared stimulation parameter combination targeting the motor cortex improves cortical excitability and hemodynamics

• Xiaowei Ma,
• Li Liu,
• Ruijing Liang,
• Mengwei Yuan,
• Fangyuan Yan,
• Shuaixiang Wang,
• Jiayi Liang,
• Shuo Qu,
• Huiqing Qiu &
• Yuping Wang 

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

Abstract

Background

Transcranial near-infrared stimulation (tNIRS) is an emerging, light-based, non-invasive neuromodulation technique with great potential to improve functions like cognition and motor performance. Cortical excitability and hemodynamic changes represent key neurophysiological mechanisms of tNIRS effects. The selection of tNIRS parameters is closely associated with their functional effects, and the neurophysiological alterations induced in the human cerebral cortex by different parameter combinations warrant further exploration.

Methods

Twenty-two healthy participants received four types of active tNIRS and a Sham condition of tNIRS over the left motor cortex in a randomized order, which included five stimulus conditions: Sham, 810-nm continuous wave (CW), 810-nm 40-Hz pulsed wave (PW), 1064-nm CW, and 1064-nm 40-Hz PW. Each session was divided into three phases, namely pre-stimulation, stimulation, and post-stimulation. Changes in cortical excitability were assessed by recording motor evoked potentials (MEPs) before and for up to 30 min after stimulation. Concurrently, behavioral performance and cortical hemodynamic changes induced by tNIRS were evaluated using functional near-infrared spectroscopy (fNIRS) during a finger-opposition task.

Results

Compared to the Sham condition, all active tNIRS protocols induced an increase in MEPs recorded from the right abductor pollicis brevis muscle. At 30 min post-stimulation, the 1064-nm 40-Hz PW condition elicited significantly larger MEP amplitudes than both the 810-nm CW and 1064-nm CW conditions. For behavior, the number of completed cycles during the right-hand finger-opposition task was significantly higher in the 1064-nm 40-Hz PW condition compared to the Sham condition. Regarding cortical hemodynamics, the 1064-nm 40-Hz PW condition showed decreased activation in broad regions of the frontal and motor cortices during the left-hand finger-opposition task compared to baseline. Conversely, the 810-nm 40-Hz PW condition exhibited decreased hemodynamic activation only in the motor cortex during the right-hand finger-opposition task.

Conclusion

The 40 Hz pulsed tNIRS protocol with a wavelength of 1064 nm, irradiance of 120 mW/cm², duration of 20 min, and stimulation area of 0.24 cm² induced more pronounced changes in cortical excitability and hemodynamics. This enhanced effect may be attributed to the more significant neurophysiological cumulative response elicited by this specific parameter combination. This specific parameter set represents a promising candidate for future clinical applications of tNIRS.

Motor imagery enhances swallowing motor cortex excitability and activates sensorimotor regions: a TMS and fNIRS study

 Will your competent? doctor bring this intervention into the hospital? NO? Why not? Although first they have to initiate research on stroke subjects.

Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem!

Motor imagery enhances swallowing motor cortex excitability and activates sensorimotor regions: a TMS and fNIRS study

• Tuo Lin,
• Gengbin Chen,
• Yaojian Lao,
• Wanqi Li,
• Guangqing Xu &
• Yue Lan 

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

Abstract

Background

Action observation (AO) and motor imagery (MI) represent promising, non-invasive strategies for promoting neuroplasticity in motor rehabilitation by engaging the shared neural substrates of actual movement. However, their translation to swallowing rehabilitation, particularly for neurogenic dysphagia, lacks a robust neurophysiological foundation. A critical barrier is the absence of direct, multimodal evidence comparing how swallowing-specific static AO (SAO), dynamic AO (DAO), and MI differentially engage the cortical swallowing network. Specifically, their immediate effects on corticobulbar excitability and hemodynamic activation within key sensorimotor regions remain unquantified and poorly contrasted, limiting the rationale for their targeted clinical application.

Objective

This study employed a dual-modal neuroimaging approach to precisely quantify and compare the immediate neurophysiological effects of SAO, DAO, and MI on the human swallowing sensorimotor system. We aim to evaluate their modulatory effects on bilateral suprahyoid motor cortical excitability and intracortical inhibitory/facilitatory circuitry using transcranial magnetic stimulation (TMS), and map their hemodynamic activation patterns within core sensorimotor cortices compared to motor execution (ME) using functional near-infrared spectroscopy (fNIRS).

Methods

Thirty-two healthy adults underwent integrated assessments using transcranial magnetic stimulation (TMS) and functional near-infrared spectroscopy (fNIRS). TMS measured motor-evoked potentials (MEPs), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF) in bilateral suprahyoid motor cortices during rest, SAO, DAO, and MI. fNIRS mapped hemodynamic changes in dorsal/ventral precentral (dPreCG/vPreCG) and postcentral gyri (dPoCG/vPoCG), superior/middle frontal gyri (SFG/MFG) during swallowing-specific SAO, DAO, MI and ME.

Results

MI reduced bilateral SICI and increased left ICF, concurrently activating bilateral dPoCG, left SFG/MFG, and right dPreCG/vPreCG/vPoCG—regions overlapping with ME-activated networks (bilateral vPreCG/vPoCG/MFG), with left MFG, right vPreCG/vPoCG as coactivating areas. In contrast, DAO reduced left SICI but elicited no hemodynamic activation, while SAO showed no significant effects.

Conclusion

MI enhances the excitability of the swallowing motor cortex and activates the key sensorimotor cortical areas governed ME of swallowing. MI shows greater superiority over AO and may become a promising effective rehabilitation strategy for neurogenic dysphagia.

Trial registration Chinese Clinical Trial Registry, ChiCTR2000036715. Registered on 24 August 2020, https//www.chictr.org.cn/bin/home.

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

 So we finally got some research that attempts to OBJECTIVELY determine the damage from the stroke. Now if the proper followup occurs we'll get EXACT REHAB PROTOCOLS that fix such damage and get survivors 100% recovered. At least that is what proper stroke research does; it leads to survivor recovery; NOT biomarkers, predictions or descriptions of damage!

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

• C. O. Muller,
• G. Faity,
• M. Muthalib,
• S. Perrey,
• G. Dray,
• B. Xu,
• J. Froger,
• D. Mottet,
• I. Laffont,
• M. Delorme &
• K. Bakhti 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 188 (2024) Cite this article

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Abstract

Background

Following a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.

Methods

Twenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.

Results and conclusions

Kinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

Background

Due to its prevalence, functional non-recovery of the paretic upper limb (UL) is a critical concern in stroke rehabilitation [1]. UL functional recovery is mainly attributed to plastic reorganization within the human brain [2, 3], and post-stroke patients often demonstrate abnormal brain activation in comparison to healthy individuals. When using the paretic hand, patients with stroke show increased contralesional and ipsilesional sensorimotor network activation compared to healthy individuals [4], as well as increased activations of contralesional primary motor cortex and bilateral premotor and supplementary motor areas [5]. During the process of functional paretic arm recovery, there is a progressive evolution towards a more “normal” lateralization of the primary sensorimotor cortex [6,7,8,9,10], which underlines the potential of monitoring brain reorganization to predict patients’ responses to rehabilitation [11]. Brain reorganization is classically assessed by functional magnetic resonance imaging (fMRI), mostly in the supine position and during moderately functional tasks such as thumb-finger opposition or elbow flexion-extension [12]. To monitor brain activations under more ecological conditions, i.e., during upright, unrestrained, functional tasks, it is possible to use portable brain imagery techniques such as functional near infrared spectroscopy (fNIRS) and functional electroencephalography (fEEG).

The fNIRS method detects variations in blood-oxygen level-dependant response, as in fMRI [13], and can do so under more ecological conditions [14]. FNIRS measures both oxygenated (HbO₂) and deoxygenated (HbR) hemoglobin in the cerebral cortex blood vessels, and has been previously used to measure sensorimotor network activation during UL movements in healthy young adults [15, 16], older healthy adults [16, 17] and stroke patients [18, 19]. In fully UL functional tasks, such as reaching, studies have identified a bilateral sensorimotor cortex (SM1) activation pattern [16, 20]. Nevertheless, to the best of our knowledge, only one recent study investigated SM1 activation in a stroke population using fNIRS during a reaching task under ecological conditions [18]. They found enhanced ipsi/contralesional SM1 activation in the stroke patients despite poorer motor performance in reaching and grasping.

The fEEG method detects direct variations in electrical currents at the scalp due to local electric fields produced by neuronal activity [21]. Event-related power changes within specific frequency bands (alpha-mu – 8 to 13 Hz and beta – 14 to 29 Hz) reflect the balance between excitation and inhibition in the sensorimotor network [22], classically with an event-related desynchronization (ERD, i.e. power decrease) at movement execution and an event-related synchronization (ERS, i.e. power increase) at rest [23]. In patients with stroke, a number of studies have shown a relationship between the magnitude of the ERD in the lesioned hemisphere and the paretic UL function [24,25,26].

Coupling fNIRS and fEEG could provide a better spatio-temporal view of SM1 brain activation patterns in both hemispheres [27]. However, to better understand SM1 activity during fully functional UL tasks, it is important to complement functional brain imaging with kinematic assessments [16]. During forward-reaching tasks, stroke patients often exhibit non-mandatory trunk compensation, i.e. even if they can do with their paretic UL alone, they favour trunk flexion to the detriment of arm use [28, 29]. Unfortunately, this non-use of the paretic UL [30] can lead to maladaptive brain plasticity [31] and hinder functional recovery [32]. Overall, it is now clear that non-mandatory trunk compensation and associated non-use have an impact on the plastic reorganisation of the brain (for a review, see [33]). Thus, investigating how trunk compensation affects SM1 activations during different functional UL tasks (detailed description of UL tasks in Sect. “Experimental design”) may help to understand the mechanisms underlying functional recovery [18].

The primary aim of the present study was to investigate bilateral SM1 activation during functional UL tasks in people with and without stroke. We hypothesised increased SM1 activation in the stroke cohort, both in the ipsilesional and contralesional hemispheres and particularly during performance of the paretic UL. Additionally, we investigated the effect of stroke on the relationship between brain activation patterns and motor performance. Our hypothesis was that individuals in the stroke group would perform worse when using their paretic arm, and that SM1 activation in the injured hemisphere would be positively correlated with task performance.

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

 I see ABSOLUTELY NOTHING here that will get survivors recovered. Useless.

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

• Jinuk Kim,
• Eunmi Kim,
• Su-Hyun Lee,
• Gihyoun Lee &
• Yun-Hee Kim 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 115 (2024) Cite this article

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Abstract

Background

Stroke causes long-term disabilities, highlighting the need for innovative rehabilitation strategies for reducing residual impairments. This study explored the potential of functional near-infrared spectroscopy (fNIRS) for monitoring cortical activation during rehabilitation using digital therapeutics.

Methods

This cross-sectional study included 18 patients with chronic stroke, of whom 13 were men. The mean age of the patients was 67.0 ± 7.1 years. Motor function was evaluated through various tests, including the Fugl–Meyer assessment for upper extremity (FMA-UE), grip and pinch strength test, and box and block test. All the patients completed the digital rehabilitation program (MotoCog^®, Cybermedic Co., Ltd., Republic of Korea) while being monitored using fNIRS (NIRScout^®, NIRx Inc., Germany). Statistical parametric mapping (SPM) was employed to analyze the cortical activation patterns from the fNIRS data. Furthermore, the K-nearest neighbor (K-NN) algorithm was used to analyze task performance and fNIRS data to classify the severity of motor impairment.

Results

The participants showed diverse task performances in the digital rehabilitation program, demonstrating distinct patterns of cortical activation that correlated with different motor function levels. Significant activation was observed in the ipsilesional primary motor area (M1), primary somatosensory area (S1), and contralateral prefrontal cortex. The activation patterns varied according to the FMA-UE scores. Positive correlations were observed between the FMA-UE scores and SPM t-values in the ipsilesional M1, whereas negative correlations were observed in the ipsilesional S1, frontal lobe, and parietal lobe. The incorporation of cortical hemodynamic responses with task scores in a digital rehabilitation program substantially improves the accuracy of the K-NN algorithm in classifying upper limb functional levels in patients with stroke. The accuracy for tasks, such as the gas stove-operation task, increased from 44.4% using only task scores to 83.3% when these scores were combined with oxy-Hb t-values from the ipsilesional M1.

Conclusions

The results advocated the development of tailored digital rehabilitation strategies by combining the behavioral and cerebral hemodynamic data of patients with stroke. This approach aligns with the evolving paradigm of personalized rehabilitation in stroke recovery, highlighting the need for further extensive research to optimize rehabilitation outcomes.

Background

Stroke, which has become increasingly prevalent with the global aging population, poses a significant public health challenge, leading to long-term disabilities and substantial burden on healthcare systems [1]. This condition results from cerebral vascular events that cause brain damage, which manifests in various impairments ranging from motor and cognitive to emotional and linguistic difficulties, substantially affecting individuals’ quality of life [2]. Effective rehabilitation is essential for improving functional recovery, reducing disabilities, and enabling the reintegration of stroke survivors into daily life, thus alleviating the socioeconomic burdens on their families and healthcare systems [3].

To create more engaging and interactive rehabilitation experiences, recent advancements in digital technology have introduced innovative approaches to rehabilitation, employing wearable technology, gamification principles, and virtual reality (VR) systems [4, 5]. These digital rehabilitation programs leverage advanced sensors and data analytics for precise patient assessment and personalized treatment plans [6]. Furthermore, artificial intelligence algorithms process data obtained through digital platforms, optimizing therapeutic outcomes by tailoring rehabilitation programs to individual needs and enhancing treatment effectiveness and personalization [7, 8].

Understanding neuroplasticity, which refers to the brain’s ability to create new neural connections, is fundamental for the development of effective neurorehabilitation strategies [9, 10]. This concept underpins treatments aimed at leveraging neural plasticity for recovery, supported by functional brain imaging research that highlights the brain’s capacity for reorganization and adaptation following a stroke [10]. Despite the recognized benefits of digital technologies in rehabilitation, the detailed relationship between cortical activation and rehabilitation outcomes remains unclear. Notwithstanding its limitations, functional near-infrared spectroscopy (fNIRS) offers advantages such as low cost, portability, and resilience to motion artifacts, making it a promising tool for understanding and using brain activation and plasticity in digital rehabilitation [11, 12].

This preliminary study was conducted to investigate cortical activation during stroke rehabilitation, as indicated by cerebral hemodynamic signals measured through fNIRS during digital rehabilitation. The first objective was to determine the characteristics of brain activation captured by cerebral hemodynamic response signals during digital rehabilitation. The second objective was to determine whether machine learning algorithms can effectively classify brain signals and elucidate patients’ functional status. The third objective was to improve our understanding of the intricate relationship between neurophysiological changes and functional motor performance in digital rehabilitation.

More at link.

fNIRS-based Neurorobotic Interface for gait rehabilitation

No clue how this can help you. 
https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-018-0346-2

• Rayyan Azam Khan,
• Noman NaseerEmail authorView ORCID ID profile,
• Nauman Khalid Qureshi,
• Farzan Majeed Noori,
• Hammad Nazeer and
• Muhammad Umer Khan

Journal of NeuroEngineering and Rehabilitation201815:7

https://doi.org/10.1186/s12984-018-0346-2

©  The Author(s). 2018

Received: 20 October 2017

Accepted: 17 January 2018

Published: 5 February 2018

Abstract

Background

In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented.

Methods

fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested.

Results

The classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively.

Conclusion

The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

Keywords

Functional near-infrared spectroscopyBrain-computer interfacePrimary motor cortexHemodynamic response filterLinear discriminant analysisSupport vector machineComputed torque controller

Background

Neurological disability due specifically to stroke or spinal cord injury can profoundly affect the social life of paralyzed patients [1–3]. The resultant gait impairment is a large contributor to ambulatory dysfunction [4]. In order to regain complete functional independence, physical rehabilitation remains the mainstay option, owing to the significant expense of health care and the redundancy of therapy sessions. Such devices are developed as alternatives to traditional, expensive and time-consuming exercises in busy daily life. In the past, similar training sessions on treadmills performed using robotic mechanisms have shown better functional outcomes [1, 2, 5–7]. However, these devices have limitations particular to given research and clinical settings. Therefore, wearable upper- and lower-limb robotic devices have been developed [7, 8], which are used to assist users by actuating joints to partial or complete movement using brain intentions, according to individual-patient needs.

To date, various noninvasive modalities including functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used to acquire brain signals. fNIRS is a relatively new modality that detects brain intention with reference to changes in hemodynamic response. Its fewer artifacts, better spatial resolution and acceptable temporal resolution make it the choice for comprehensive and promising results in, for example, rehabilitation and mental task applications [9–20]. The main brain-computer interface (BCI) challenge in this regard is to extract useful information from raw brain signals for control-command generation [21–23]. Acquired signals are processed in the following four stages: preprocessing, feature extraction, classification, and command generation. In preprocessing, physiological and instrumental artifacts and noises are removed [24, 25]. After this filtration stage, feature extraction proceeds in order to gather useful information. Then, the extracted features are classified using different classifiers. Finally, the trained classifier is used to generate control commands based on a trained model [23]. Figure 1 shows a schematic of a BCI.

Fig. 1

Schematic of BCI

Previous studies on signal-acquisition techniques have shown promising outcomes, but rehabilitation applications require the best possible results [3, 4, 26]. In Eliana et al. [27], a treadmill was used to acquire EEG-based walking brain signals for sensorimotor applications with 87% accuracy. In Andreea et al. [28], EEG-based walking-intention signals were detected for stroke patients with an accuracy of 82%. Their data indicated that patients highly motivated for rehabilitation-related tasks tended to have higher success rates. In Naseer et al. [29], two-class motor imagery movements were analyzed using an LDA classifier. With their employed modality, fNIRS, the best features were found to be signal mean (SM) and signal slope (SS). By reducing the task period to between 2 and 7 s, the accuracies were improved to 77.56 and 87.28%, respectively. In Rea et al. [30], lower-limb movement for gait rehabilitation was detected based on fNIRS signals. They were able to acquire fNIRS signals in their chronic stroke patients during preparation for hip movement with 67.77 ± 11.35% accuracy. In Zhao et al. [31], a prosthetic controller was proposed for a bipedal robot. A walking gait pattern was found for the robot mechanism while an online optimized trans-femoral prosthesis control method (i.e. control Lyapunov function (CLF)-based quadratic programs (QPs) with variable impedance control) was tested on the knee and ankle joints of the prosthetic device. Azimi et al. [32] proposed stable robust adaptive impedance control for a prosthetic limb. A regressor-based nonlinear robust model was designed with reference to an adaptive impedance controller. In Richter et al. [33], dynamic modeling and simulation-based control of a prosthesis were performed, focusing on two-degree-of-freedom robot modeling, parametric estimation and feedback control for mimicking of hip motions. Perrey [34] explored neural gait control using fNIRS, specifically looking at the relevant cortical areas. In Venkatakrishnan [35], meanwhile, examined and discussed a rehabilitation-based brain machine interface (BMI) application for stoke patients.

The previous literature on the subject of rehabilitation shows that classification accuracy in the online setting is compromised by, among other problems, false triggering. Therefore, we also present a method to ensure that a correct command is always sent to a prosthetic leg (details are given in Section 3.1.1).

In this study, we acquired fNIRS walking signals of healthy subjects. Raw signals might contain noises and artifacts that can be removed using adaptive or band-pass filtering [25, 36]. In order to avoid such noises, the following six filters were compared for signal processing: Kalman, Wiener, finite impulse response (FIR), hemodynamic response (hrf), Band-pass, and Gaussian. Five classifiers, namely quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), support vector machine (SVM), k-Nearest Neighbour (KNN), and Naïve Bayes (NB), were analyzed for acquisition of maximum classification accuracies. For offline BCI, SVM showed greater statistical significance (p < 0.01) as compared with the other classifiers; however, in consideration of execution delay and minimum computation cost, for online BCI, we used LDA with combinations of six features: SS, SM, signal peak (SP), signal kurtosis (KR), signal skewness (SK), and signal variance (SV). Walking intention was then used to initiate and stop the gait cycle of the proposed prosthetic leg model. For minimization of discomfort, a nonlinear computed torque controller (CTC) with gravity compensation was applied to two active joints in the hip and knee and one passive joint in the ankle for position control and reduction of error in waking patterns [37–39]. Given its effective simulation of classical limb-type and mobile robotics, the Peter Corke® robotics tool box was used to minimize position error [40]. The proposed system is applicable not only to paralyzed patients but also, and with little modification, to amputees and elderly people.