Posts Tagged Sensors

[Abstract] Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication

Abstract

Spasticity and contractures are secondary to most neurological and orthopaedic pathologies. The most conservative method of management of spasticity and contractures is passive stretching exercises. Robotic rehabilitation aims to provide a solution to this problem. We describe in details the design of a powered wearable orthosis especially designed for managing spasticity and contractures. The device is fully portable, allowing the patient to undergo repeated-passive-dynamic exercises outside the hospital environment. The design of the device is modular to make it adaptable to different anatomies and pathologies. The device is also fitted with electrogoniometers and torque sensors to record kinematics and dynamics of the patient, improving the insight of the clinicians to the rehabilitation of the patient, as well as providing data for further clinical and scientific investigations. The mechanical integrity of the device elements is simulated and verified.

via Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication

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[NEWS] MbientLab Launches its MIOTherapy Physical Therapy Wearable Technology

Unique technology platform uses smart sensors, therapeutic exercises and games to improve rehabilitation and recovery for patients undergoing physical therapy

MIO is a complete, wearable sensor solution that automatically measures, analyzes, and stores a patient's physical therapy data. (Graphic: Business Wire)

MIO is a complete, wearable sensor solution that automatically measures, analyzes, and stores a patient’s physical therapy data. (Graphic: Business Wire)

January 28, 2019 09:00 AM Eastern Standard Time

 

SAN FRANCISCO–(BUSINESS WIRE)–MbientLab, a company building the next generation of sensors and tools for the healthcare industry, has announced the availability of its MIOTherapy (MIO) wearable technology for physical and occupational therapists. MIO is the first wearable technology platform that integrates the effectiveness of traditional physical therapy with smart sensors, therapeutic exercises, games, and 3D visualization technology to personalize and improve outpatient rehabilitation and accelerate recovery.

.@mbientLab announces the launch of its @MioTherapy wearable technology for physical and occupational therapists to improve rehabilitation and recovery for patients undergoing #physicaltherapy.

Research shows that most physical therapy patients do not fully adhere to their plans for care because of factors that include lack of social support, self-doubt and perceived barriers to exercise.1 This results in millions of Americans living with preventable mobility issues and pain that reduce their quality of life. This lack of compliance also increases the cost of healthcare for these patients due to a higher number of urgent care and emergency room visits related to their injuries, and in some cases, inpatient post-acute care stays.

Using a unique combination of technology software and sensors, MIO helps physical and occupational therapists improve the experience and outcomes of therapy for their patients. MIO provides consistently accurate measurements that can be used to monitor and personalize treatment, increase patient compliance, reduce recovery time, and reduce healthcare costs.

“I’ve found the MIO based technology to be an invaluable tool in improving post-operative care for my patients where position is critical. It’s clear to me that MIO will be a great platform for doctors and physical therapists to analyze, adjust and customize patient treatment plans using precise measurements captured in real time,” said Frank Brodie, M.D., clinical faculty, University of California San Francisco. “This technology provides data that enables me to have an accurate understanding of my patients’ ongoing progress and adjust accordingly. I look forward to integrating MIO even more into my practice.”

Patients using MIO attach its sensors to any body part using stickers or flexible straps, so that physical therapists can measure, collect, and record all motion from a specific body area, delivering key insights about a patient’s range of motion and measurable progress through their exercise program. The extremely accurate sensors measure, analyze, and store a patient’s physical therapy data in the cloud for easy access and analysis via the MIO App. MIO also offers real-time 3D visualization, providing an exact picture of what the patient is doing at any moment, and can be used in-office or via a telehealth platform with clinical oversight.

“We are excited to offer physical and occupational therapists a wearable technology platform that improves patient and provider engagement, and ultimately supports better results and a quicker recovery time for patients,” said Laura Kassovic, co-founder and CEO of MbientLab. “Serving as their virtual assistant, MIO will help physical therapists rethink how they provide physical therapy and work to heal their patients so they can get back to doing the things they enjoy.”

MIO has undergone extensive sensor testing with more than a dozen third-party users, including physical therapists, researchers, clinics, and university labs. Since 2013, there have been more than 250 papers published on the use of the MbientLab sensors used in MIO. Physicians at the University of California, San Francisco have demonstrated that the MIO sensors can increase patient compliance by 20 percent to 80 percent in post-operative retinal surgery patients.2 Researchers at Duke University also found an average cost-savings of $2,745 per patient undergoing virtual physical therapy with MIO compared to traditional physical therapy.3

MIO is now commercially available in the United States and internationally and can be purchased by physical and occupational therapists, caregivers and researchers at www.miotherapy.com. MIO is available through monthly subscription plans that include the app, sensors, and access to the cloud, as well as unlimited and free customer support via email, and on-site services.

About MIOTherapy

MIOTherapy is the first wearable technology that integrates the effectiveness of traditional physical therapy with therapeutic exercises, games, and smart sensors to improve outpatient rehabilitation and speed up recovery. Visit www.miotherapy.com or follow @miotherapy on Twitter, @miotherapy on Facebook and @miotherapy on Instagram for more information.

About MbientLab

MbientLab is building the next generation of sensors and tools for the healthcare industry including motion capture and analytics, biometrics, kinematics, industrial control, research and product development. Visit www.mbientlab.com for more information.

Picha KJ, Howell DM. A model to increase rehabilitation adherence to home exercise programmes in patients with varying levels of self-efficacy. Musculoskeletal Care, 2018; 16:233-237.

Brodie et al., Novel positioning with real-time feedback for improved postoperative positioning: pilot study in control subjects; May 2017

Duke Clinical Research Institute, VERITAS research study, 2016

Contacts

for MbientLab
Hannah Boxerman
707-326-0870
hannah@healthandcommerce.com

 

via MbientLab Launches its MIOTherapy Physical Therapy Wearable Technology | Business Wire

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[Abstract + References] Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication

Abstract

Hand function assessment is essential for upper limb rehabilitation of stroke survivors. Conventional acquisition devices have inherent and restrictive difficulties for their clinical usage. Data gloves are limited for applications outside the medical environment, and motion tracking systems setup are time and personnel demanding. We propose a novel instrument designed as a replica of a glass, equipped with an omnidirectional vision system to capture hand images and an inertial measurement unit for movements kinematic data acquisition. Four stroke survivors were invited as volunteers in pre and post-treatment experiments for its evaluating. The exercise of drinking water from a glass was elected for the trails. Before treatment, subjects used their contralesional and ipsilateral hands to perform them. Two main functional features were found in the data analysis. There were differences between limbs in the grasping hand postures, mainly in the index and thumb abduction angle, and in the task timing. After treatment, two volunteers repeated the protocol with their contralesional hands. Changes in the features were observed, index and thumb abduction angles were greater in both cases, and tasks timing were altered in distinct ways. These preliminary results suggest the instrument can be used both in evaluation of hand functional deficit and rehabilitation progress. Improvements and future work are also presented.
1. R. L. Sacco, S. E. Kasner, J. P. Broderick, L. R. Caplan, A. Culebras, M. S. Elkind, M. G. George, A. D. Hamdan, R. T. Higashida, B. L. Hoh et al., “An updated definition of stroke for the 21st century: a statement for healthcare professionals from the american heart association/american stroke association”, Stroke, vol. 44, no. 7, pp. 2064-2089, 2013.

2. C. A. Doman, K. J. Waddell, R. R. Bailey, J. L. Moore, C. E. Lang, “Changes in upper-extremity functional capacity and daily performance during outpatient occupational therapy for people with stroke”, American Journal of Occupational Therapy, vol. 70, no. 3, pp. 7003290040pl-7003290040p11, 2016.

3. B. Brouwer, M. V. Sale, M. A. Nordstrom, “Asymmetry of motor cortex excitability during a simple motor task: relationships with handedness and manual performance”, Experimental Brain Research, vol. 138, no. 4, pp. 467-476, 2001.

4. J. Langan, P. van Donkelaar, “The influence of hand dominance on the response to a constraint-induced therapy program following stroke”, Neurorehabilitation and neural repair, vol. 22, no. 3, pp. 298-304, 2008.

5. H. I. Krebs, M. L. Aisen, B. T. Volpe, N. Hogan, “Quantization of continuous arm movements in humans with brain injury”, Proceedings of the National Academy of Sciences, vol. 96, no. 8, pp. 4645-4649, 1999.

6. B. Fisher, C. Winstein, M. Velicki, “Deficits in compensatory trajectory adjustments after unilateral sensorimotor stroke”, Experimental brain research, vol. 132, no. 3, pp. 328-344, 2000.

7. H. Sugarman, A. Avni, R. Nathan, A. Weisel-Eichler, J. Tiran, “Movement in the ipsilesional hand is segmented following unilateral brain damage”, Brain and cognition, vol. 48, no. 2-3, pp. 579-587, 2002.

8. D. A. Nowak, “The impact of stroke on the performance of grasping: usefulness of kinetic and kinematic motion analysis”, Neuroscience & Biobehavioral Reviews, vol. 32, no. 8, pp. 1439-1450, 2008.

9. M. Coluccini, E. S. Maini, C. Martelloni, G. Sgandurra, G. Cioni, “Kinematic characterization of functional reach to grasp in normal and in motor disabled children”, Gait & posture, vol. 25, no. 4, pp. 493-501, 2007.

10. E. Jaspers, H. Feys, H. Bruyninckx, J. Harlaar, G. Molenaers, K. Desloovere, “Upper limb kinematics: development and reliability of a clinical protocol for children”, Gait & posture, vol. 33, no. 2, pp. 279-285, 2011.

11. D. A. Nowak, J. Hermsdörfer, H. Topka, “Deficits of predictive grip force control during object manipulation in acute stroke”, Journal of neurology, vol. 250, no. 7, pp. 850-860, 2003.

12. R. W. Bohannon, “Adequacy of hand-grip dynamometry for characterizing upper limb strength after stroke”, Isokinetics and exercise science, vol. 12, no. 4, pp. 263-265, 2004.

13. H. Zhou, H. Hu, “Human motion tracking for rehabilitationâĂŤa survey”, Biomedical Signal Processing and Control, vol. 3, no. 1, pp. 1-18, 2008.

14. A. C. P. Rocha, E. Tudella, L. M. Pedro, V. C. R. Appel, L. G. P. da Silva, G. A. d. P. Caurin, “A novel device for grasping assessment during functional tasks: preliminary results”, Frontiers in bioengineering and biotechnology, vol. 4, pp. 16, 2016.

15. E. Taub, G. Uswatte, “Constraint-induced movement therapy: bridging from the primate laboratory to the stroke rehabilitation laboratory”, Journal of Rehabilitation Medicine-Supplements, vol. 41, pp. 34-40, 2003.

16. R. d. N. B. Marques, A. C. Magesto, R. E. Garcia, C. B. d. Oliveira, G. d. S. Matuti, “Efeitos da terapia por contensão induzida nas lesões encefálicas adquiridas”, Fisioterapia Brasil, vol. 17, no. 1, pp. f-30, 2016.

17. E. E. Butler, A. L. Ladd, L. E. LaMont, J. Rose, “Temporal-spatial parameters of the upper limb during a reach & grasp cycle for children”, Gait & posture, vol. 32, no. 3, pp. 301-306, 2010.

18. E. E. Butler, A. L. Ladd, S. A. Louie, L. E. LaMont, W. Wong, J. Rose, “Three-dimensional kinematics of the upper limb during a reach and grasp cycle for children”, Gait & posture, vol. 32, no. 1, pp. 72-77, 2010.

19. L. Gauthier, Structural brain changes produced by different motor therapies after stroke, 2011.

20. L. M. Pedro, G. A. de Paula Caurin, “Kinect evaluation for human body movement analysis”, Biomedical Robotics and Biomechatronics (BioRob) 2012 4th IEEE RAS & EMBS International Conference on, pp. 1856-1861, 2012.

21. A. Hussain, S. Balasubramanian, N. Roach, J. Klein, N. Jarrassé, M. Mace, A. David, S. Guy, E. Burdet, “Sitar: a system for independent task-oriented assessment and rehabilitation”, Journal of Rehabilitation and Assistive Technologies Engineering, vol. 4, pp. 2055668317729637, 2017.

22. L. R. L. Cardoso, M. N. Martelleto, P. M. Aguiar, E. Burdet, G. A. P. Caurin, L. M. Pedro, “Upper limb rehabilitation through bicycle controlling”, 24th International Congress of Mechanical Engineering, 2017.

23. M. N. Martelleto, P. M. Aguiar, E. Burdet, G. A. P. Caurin, R. V. Aroca, L. M. Pedro, “Instrumented module for investigation of contact forces for use in rehabilitation and assessment of bimanual functionalities”, 24th International Congress of Mechanical Engineering, 2017.

 

via Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication

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[Abstract] sEMG Bias-driven Functional Electrical Stimulation System for Upper Limb Stroke Rehabilitation

Abstract:

It is evident that the dominant therapy of functional electrical stimulation (FES) for stroke rehabilitation suffers from heavy dependency on therapists experience and lack of feedback from patients status, which decrease the patients’ voluntary participation, reducing the rehabilitation efficacy. This paper proposes a closed loop FES system using surface electromyography (sEMG) bias feedback from bilateral arms for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition and FES modules, the former is used to measure and analyze the subject’s bilateral arm motion intention and neuromuscular states in terms of their sEMG, the latter of multi-channel FES output is controlled via the sEMG bias of the bilateral arms. The system has been evaluated with experiments proving that the system can achieve 39.9 dB signal-to-noise ratio (SNR) in the lab environment, outperforming existing similar systems. The results also show that voluntary and active participation can be effectively employed to achieve different FES intensity for FES-assisted hand motions, demonstrating the potential for active stroke rehabilitation.
Published in: IEEE Sensors Journal ( Early Access ) Date of Publication: 18 June 2018

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via sEMG Bias-driven Functional Electrical Stimulation System for Upper-Limb Stroke Rehabilitation – IEEE Journals & Magazine

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[Abstract] A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study

Abstract:

When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.

I. Introduction

Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis [1]. There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes [2]. Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments [3]. It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements [4]. To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings [5], which emphasizes the importance of the home-based therapy.

 

via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication

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[WEB SITE] Wearable tech aids stroke patients – BBC News

Scientists in the US are developing wearable sensors to speed up the recovery of stroke patients.

The sensors are able to send information to doctors continuously.

The team developing the system says it could allow therapists to more closely monitor the effectiveness of their care.

Details of the study were released at the recent annual meeting of the American Association for the Advancement of Science in Texas.

Lizzy McAninch had a stroke two years ago. She could not move or speak or swallow for several weeks.

Lizzy is testing out wearable sensors that might speed her recovery.

They look like small white sticking plasters, but they send information wirelessly to her medical team.

She is a doctor herself and can see how they could help her.

Sensor

SHIRLEY RYAN ABILITYLAB They look like small sticking plasters

“This technology to put sensors on the body to assess which muscle groups work or not can really pinpoint the areas affected by the stroke and can target therapies to specifically improve those issues,” she told BBC News.

The sensors continue to send back readings even after she has finished her exercises. This means that her therapist Kristen Hohl, from the Shirley Ryan AbilityLab in Chicago, can monitor her progress at home.

“As a therapist, I think about what my patients are doing at home. Are they able to carry through the recommendations I’m giving them as a therapist to do more? Do we see that they are walking more or do we see them engaging in conversations?

“Those are the types of things that I can get feedback from the sensors where currently I have to rely on what they tell me they have done.”

Tablet

 SHIRLEY RYAN ABILITYLAB  The team is gathering large amounts of data

The challenge for the scientists was to pack a lot of electronics on to a small flexible material and still make it comfortable for the patient to wear for a long time.

“It is almost mechanically imperceptible to the patient who is wearing the device,” according to John Rogers, of Northwestern University in Chicago, who developed the sensors.

“And you can embed all sorts of advanced sensor functionality, microprocessor computing capability, power supplies and WiFi into this very unusual platform, and that is the uniqueness of what we do.”

By the end of this year, the research team will have more information than ever before on stroke recovery. The scientists believe that their study could transform the way patients are treated in the future.

Lizzy and scientist

SHIRLEY RYAN ABILITYLAB

 

via Wearable tech aids stroke patients – BBC News

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[Abstract+References] A Computer-Assisted System with Kinect Sensors and Wristband Heart Rate Monitors for Group Classes of Exercise-Based Rehabilitation

Abstract

Exercise-based rehabilitation for chronic conditions such as cardiovascular disease, diabetes, and chronic obstructive pulmonary disease, constitutes a key element in reducing patient symptoms and improving health status and quality of life. However, group exercise in rehabilitation programmes faces several challenges imposed by the diversified needs of their participants. In this direction, we propose a novel computer-assisted system enhanced with sensors such as Kinect cameras and wristband heart rate monitors, aiming to support the trainer in adapting the exercise programme on-the-fly, according to identified requirements. The proposed system design facilitates maximal tailoring of the exercise programme towards the most beneficial and enjoyable execution of exercises for patient groups. This work contributes in the design of the next-generation of computerised systems in exercise-based rehabilitation.

References

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    Claes J, Buys R, Avila A et al (2017) Validity of heart rate measurements by the Garmin Forerunner 225 at different walking intensities. J Med Eng Technol 41:480–485.  https://doi.org/10.1080/03091902.2017.1333166CrossRefGoogle Scholar
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via A Computer-Assisted System with Kinect Sensors and Wristband Heart Rate Monitors for Group Classes of Exercise-Based Rehabilitation | SpringerLink

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[CORDIS] A sensor-fitted suit to analyse stroke patients’ movements.

The moment when stroke patients return home after treatment has always been a source of concern for both themselves and their physicians, as the latter are left blind without any feedback. But this is now a thing of the past: a novel suit fitted with 41 sensors is finally ready for commercialisation.

A sensor-fitted suit to analyse stroke patients’ movements

© Wright Studio, Shutterstock 

Could resorting to rehabilitation clinics be less of a necessity in the near future? Whilst these clinics effectively help patients to face post-stroke everyday life, stakeholders tend to agree that a better understanding of how these people function in the absence of medical support could lead to more effective rehabilitation at a lower cost.

This is what Bart Klaassen, PhD student at the University of Twente, and and a large team of researchers from across Europe have been working on under the INTERACTION project. Together they developed and validated an unobtrusive and modular system for monitoring daily life activities and for training motor function in stroke subjects, in the shape of a multi-sensor-equipped suit.

This project is presented by Klaassen and his team as a world first. ‘There has long been a great need for systems like this, but the technology simply was not ready,’ he says. ‘That is now changing rapidly, thanks to rapid developments in the fields of battery technology, wearables, smart e-textiles and big data analysis.’

The INTERACTION suit has been extensively tested on patients over a period of three months, during which they were asked to wear it under their regular clothes. The data was then transmitted, stored and processed thanks to a portable transmitter that can relay all of the information gathered through the internet to data processing servers at the University of Twente. The 41 sensors included in the suit monitor a large number of body segments, providing information on muscle strength, stretch and force.

‘We have been able to demonstrate that all the information is transmitted successfully, that this process is very efficient, and much more besides,’ Klaassen enthuses. ‘We have succeeded in modelling all of the relevant movements, and in cleaning up the data that is relevant for the therapist by filtering out the rest. Our project has delivered new techniques and methods that can be used to monitor patients at home for extended periods of time, and to identify any differences with structured clinical measurements. We are currently engaged in further research to obtain final verification that these methods are indeed an ideal way of supervising rehabilitation.’

The press release recently published by the University of Twente says no word about a potential date of commercialisation. However, the fact that both insurance companies and healthcare professionals were involved from the early stages of the project leaves little doubt that stroke patients will soon benefit from this technological breakthrough.

For more information, please see:
CORDIS project page

Source: Based on a press release from the University of Twente

 

via European Commission : CORDIS : News and Events : A sensor-fitted suit to analyse stroke patients’ movements

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[Abstract] The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation.

Abstract:

Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.

Source: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation – IEEE Xplore Document

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[Abstract] Towards an ankle neuroprosthesis for hybrid robotics: Concepts and current sources for functional electrical stimulation

Abstract:

Hybrid rehabilitation robotics combine neuro-prosthetic devices (close-loop functional electrical stimulation systems) and traditional robotic structures and actuators to explore better therapies and promote a more efficient motor function recovery or compensation. Although hybrid robotics and ankle neuroprostheses (NPs) have been widely developed over the last years, there are just few studies on the use of NPs to electrically control both ankle flexion and extension to promote ankle recovery and improved gait patterns in paretic limbs. The aim of this work is to develop an ankle NP specifically designed to work in the field of hybrid robotics. This article presents early steps towards this goal and makes a brief review about motor NPs and Functional Electrical Stimulation (FES) principles and most common devices used to aid the ankle functioning during the gait cycle. It also shows a current sources analysis done in this framework, in order to choose the best one for this intended application.

Source: Towards an ankle neuroprosthesis for hybrid robotics: Concepts and current sources for functional electrical stimulation – IEEE Xplore Document

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