ReviewSensors (Basel, Switzerland)2019
A Review of Force Myography Research and Development.
Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed.
- Review
- A Co-Located sEMG-pFMG Dataset for Hand Gesture Recognition Under Varying Arm-Position Conditions.Sensors (Basel, Switzerland) · 2026Article
- From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.Scientific reports · 2026Article
- Turning a knob: deep learning-based prediction of torque and arm angles using force myography.Physical and engineering sciences in medicine · 2026Article
- Wearable and Implantable Devices for Continuous Monitoring of Muscle Physiological Activity: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Utilizing Data Quality Indices for Strategic Sensor Channel Selection to Enhance Performance of Hand Gesture Recognition Systems.Sensors (Basel, Switzerland) · 2026Article
- Review
- Muscle myography for human-machine interfaces: a review of sensing modalities and control interfaces.Frontiers in bioengineering and biotechnology · 2026Review
- A database of multimodal myography and hand kinematics during realistic daily life activities.Scientific data · 2025Article
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Low Back Exoskeletons in Industry 5.0: From Machines to Perceiving Co-Pilots-A State-of-the-Art Review.Sensors (Basel, Switzerland) · 2025Review
- Article
- A DICOM-based standard for quantitative physical rehabilitation.Computational and structural biotechnology journal · 2025Article
- A Multimodal Bracelet to Acquire Muscular Activity and Gyroscopic Data to Study Sensor Fusion for Intent Detection.Sensors (Basel, Switzerland) · 2024Article
- Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs.Sensors (Basel, Switzerland) · 2024Article
- Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation.Bioengineering (Basel, Switzerland) · 2024Review
- Estimation of joint torque in dynamic activities using wearable A-mode ultrasound.Nature communications · 2024Article
- A survey on the state of the art of force myography technique (FMG): analysis and assessment.Medical & biological engineering & computing · 2024Review
- A Wearable Force Myography-Based Armband for Recognition of Upper Limb Gestures.Sensors (Basel, Switzerland) · 2023Article
- Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals.Journal of neuroengineering and rehabilitation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.