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

Muscles that Move the Head01:19

Muscles that Move the Head

2.8K
The muscles that move the head are a dynamic and complex group of structures that work together to facilitate a wide range of head movements, including rotation, flexion, extension, and lateral bending.
The bilateral sternocleidomastoid, or SCM, and the suprahyoid and infrahyoid muscles are significant head flexors. The SCM muscles originate at the sternum and clavicle and attach to the mastoid process of the temporal bone. The SCM contracts bilaterally to bend the head forward, whereas...
2.8K

You might also read

Related Articles

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

Sort by
Same author

A Narrative Review of AI Frameworks for Chronic Stress Detection Using Physiological Sensing: Resting, Longitudinal, and Reactivity Perspectives.

Sensors (Basel, Switzerland)·2026
Same author

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

Sensors (Basel, Switzerland)·2024
Same author

In Vitro Anti-Oxidant, In Vivo Anti-Hyperglycemic, and Untargeted Metabolomics-Aided-In Silico Screening of Macroalgae Lipophilic Extracts for Anti-Diabetes Mellitus and Anti-COVID-19 Potential Metabolites.

Metabolites·2023
Same author

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity.

IEEE transactions on bio-medical engineering·2023
Same author

Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream.

Sensors (Basel, Switzerland)·2022
Same author

A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition.

Sensors (Basel, Switzerland)·2022
Same journal

Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Motion Intention Recognition and DDPG-Based Adaptive Impedance Control for a Robotic Upper-Limb Exoskeleton.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

CNN-Based Modelling Reveals Temporal Brain Dynamics of Auditory Intensity Processing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Pathology-Informed Augmentation Improves Cross-Cohort IMU-to-vGRF Estimation Between Healthy Adults and Adults With Osteoarthritis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Effects of task-driven head orientations on gait and balance during walking in virtual reality.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Wearable sensor-based Mild Cognitive Impairment Identification: A Multi-Domain Gait Analysis Approach with Association Rule Mining.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
See all related articles

Related Experiment Video

Updated: Sep 6, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

734

Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control.

Wahyu Rahmaniar, Alfian Ma'Arif, Ting-Lan Lin

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 30, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Touchless Head-Control (THC), a novel system enabling computer cursor control via head movements using an RGB camera. This assistive technology empowers individuals with limited motor skills to operate PCs and control 3D objects hands-free.

    More Related Videos

    Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
    12:07

    Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

    Published on: July 29, 2009

    17.9K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    1.7K

    Related Experiment Videos

    Last Updated: Sep 6, 2025

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    734
    Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
    12:07

    Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

    Published on: July 29, 2009

    17.9K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    1.7K

    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Assistive Technology

    Background:

    • Traditional computer interaction methods pose challenges for individuals with motor disabilities.
    • Existing assistive technologies often require specialized hardware or sensors.
    • There is a need for intuitive and accessible computer control solutions for disabled users.

    Purpose of the Study:

    • To develop and evaluate a touchless computer cursor control system using head pose estimation.
    • To enable individuals with limited motor skills to interact with computers and control 3D objects.
    • To provide an alternative to traditional input devices through head movements.

    Main Methods:

    • Utilized convolutional neural networks (CNNs) for accurate head pose estimation (yaw, pitch, roll) from RGB camera input.
    • Implemented a system (Touchless Head-Control - THC) to translate head movements into cursor control and mouse button actions.
    • Applied fine-grained feature maps and binned classification for precise head pose angle prediction.

    Main Results:

    • The THC system demonstrated effective control of the PC cursor and 3D object orientation.
    • Evaluation metrics including target selection accuracy, travel time, and path efficiency were measured through user interaction.
    • Head movements (yaw and pitch) controlled cursor position, while head tilt (roll) simulated mouse button clicks.

    Conclusions:

    • Touchless Head-Control (THC) offers a viable and accessible solution for computer interaction for people with motor impairments.
    • The system successfully translates natural head movements into functional computer control without external hardware.
    • This technology significantly enhances computer accessibility, allowing for intuitive control of cursors and 3D objects.