Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Open and closed-loop control systems01:17

Open and closed-loop control systems

1.6K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.6K
The Citric Acid Cycle02:36

The Citric Acid Cycle

161.7K
The citric acid cycle, also known as the Krebs cycle or TCA cycle, consists of several energy-generating reactions that yield one ATP molecule, three NADH molecules, one FADH2 molecule, and two CO2 molecules.
161.7K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403
What are Biogeochemical Cycles?00:54

What are Biogeochemical Cycles?

39.2K
The most common elements in organic molecules, carbon, hydrogen, oxygen, nitrogen, sulfur, and phosphorus, are only available in the ecosystem in limited amounts. Therefore, these nutrients must be recycled through both biotic and abiotic components of the ecosystem, in processes generally called biogeochemical cycles.
39.2K
The Water Cycle01:00

The Water Cycle

28.2K
The Earth’s hydrosphere includes all of the areas where the storage and movement of water occurs. Since water is the basis of all living processes, the cycling of water is extremely important to ecosystem dynamics.
28.2K
The Phosphorus Cycle01:21

The Phosphorus Cycle

43.8K
Unlike carbon, water, and nitrogen, phosphorus is not present in the atmosphere as a gas. Instead, most phosphorus in the ecosystem exists as compounds, such as phosphate ions (PO43-), found in soil, water, sediment and rocks. Phosphorus is often a limiting nutrient (i.e., in short supply). Consequently, phosphorus is added to most agricultural fertilizers, which can cause environmental problems related to runoff in aquatic ecosystems.
43.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Weight Management Post-Stroke: A Scoping Review.

American journal of lifestyle medicine·2026
Same author

CALM-VLM: CALIBRATION AND SELECTIVE PREDICTION IN VISION-LANGUAGE MODELS FOR RELIABLE BRAIN MRI CLASSIFICATION.

bioRxiv : the preprint server for biology·2026
Same author

Age differences in electrocortical dynamics during uneven terrain walking.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Development and validation of a kinematic hindlimb cycling model for rats.

Scientific reports·2025
Same author

Age differences in electrocortical dynamics during uneven terrain walking.

bioRxiv : the preprint server for biology·2025
Same author

Item-Level Psychometrics for the Functional Gait Assessment in Persons With Stroke.

Archives of rehabilitation research and clinical translation·2025
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

1.3K

FES Cycling in Stroke: Novel Closed-Loop Algorithm Accommodates Differences in Functional Impairments.

Courtney A Rouse, Ryan J Downey, Chris M Gregory

    IEEE Transactions on Bio-Medical Engineering
    |June 7, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study developed a new algorithm for stroke survivors to improve cycling cadence control. The system uses functional electrical stimulation (FES) and a motor to assist or resist pedaling, enhancing rehabilitation outcomes.

    More Related Videos

    Biventricular Assessment of Cardiac Function and Pressure-Volume Loops by Closed-Chest Catheterization in Mice
    08:21

    Biventricular Assessment of Cardiac Function and Pressure-Volume Loops by Closed-Chest Catheterization in Mice

    Published on: June 15, 2020

    4.9K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.8K

    Related Experiment Videos

    Last Updated: Jan 23, 2026

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
    06:37

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

    Published on: July 14, 2023

    1.3K
    Biventricular Assessment of Cardiac Function and Pressure-Volume Loops by Closed-Chest Catheterization in Mice
    08:21

    Biventricular Assessment of Cardiac Function and Pressure-Volume Loops by Closed-Chest Catheterization in Mice

    Published on: June 15, 2020

    4.9K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.8K

    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Robotics
    • Neurorehabilitation

    Background:

    • Stroke survivors often experience impaired motor function, affecting activities like cycling.
    • Maintaining a consistent pedaling cadence is crucial for effective rehabilitation.

    Purpose of the Study:

    • To develop and test a novel control algorithm for cycle ergometry in stroke survivors.
    • To enable individuals with varying functional abilities to pedal within a target cadence range.

    Main Methods:

    • A new algorithm was created to adjust functional electrical stimulation (FES) intensity and electric motor current.
    • The algorithm dynamically switches between assistive, uncontrolled, and resistive modes based on cadence error.
    • Lyapunov-based methods were used for theoretical validation of cadence convergence.

    Main Results:

    • The controller achieved an average root-mean-square (rms) cadence error of 1.90 r/min.
    • This significantly improved upon the 6.16 r/min error observed during volitional-only cycling.
    • Nine chronic stroke survivors participated in the trials.

    Conclusions:

    • The developed algorithm effectively enabled stroke survivors to pedal within a desired cadence range (50-55 r/min).
    • The system's ability to assist and resist volitional efforts accommodates diverse functional impairments.
    • This approach shows promise for FES cycles and other rehabilitation robots in stroke recovery.