Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
Open accessAbstract
Clinical evaluation of the patellar reflex is one of the most frequent diagnostic methods used by physicians and medical specialists. However, this test is usually elicited and diagnosed manually. In this work, we develop a device specifically designed to induce the patellar reflex and measure the angle and angular velocity of the leg during the course of the reflex test. We have recorded the response of 106 volunteers with the aim of finding a recognizable pattern in the responses that can allow us to classify each reflex according to the scale of the National Institute of Neurological Disorders and Stroke (NINDS). In order to elicit the patellar reflex, a hammer is attached to a specially designed pendulum, with a controlled impact force. All volunteer test subjects sit at a specific height, performing the Jendrassik maneuver during the test, and the medical staff evaluates the response in accordance with the NINDS scale. The data acquisition system is integrated by using a tapping sensor, an inertial measurement unit, a control unit, and a graphical user interface (GUI). The GUI displays the sensor behavior in real time. The sample rate is 5 kHz, and the control unit is configured for a continuous sample mode. The measured signals are processed and filtered to reduce high-frequency noise and digitally stored. After analyzing the signals, several domain-specific features are proposed to allow us to differentiate between various NINDS groups using machine learning classifiers. The results show that it is possible to automatically classify the patellar reflex into a NINDS scale using the proposed biomechanical measurements and features.
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Another study reported an accuracy of 89.62%, according to the NINDS score, using machine learning classifiers.
“another study reported an accuracy of 89.62%, according to the NINDS score, using machine learning classifiers30.”
From Quantification of patellar tendon reflex using portable mechanomyography and electromyography devices (2021) · cites this paper as [30]
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Flagged first, then by citations
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Effects of Rugby Training Exposure on Knee-Jerk Reflex Magnitude: A Feasibility Study
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Verma, V. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33855-7.00003-6
- Authors
- Salazar-Muñoz, Y., López-Pérez, G. A., García-Caballero, B. E., Muñoz-Rios, R., Ruano-Calderón, L. A., & Trujillo, L.
- Affiliations
- Tecnológico Nacional de México/Instituto Tecnológico de Durango, C.P. 34080, Durango, DGO, Mexico
- Durango Institute of Technology (MX)
- Servicios de Salud del Estado de Durango, Hospital General 450, C.P. 34206, Durango, DGO, Mexico
- Tecnológico Nacional de México (MX)
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2019-07-09
- Volume
- 2019
- Pages
- 1-7
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- PMC
- PMC6652033
- OpenAlex
- W2961775041
- Cited by
- 8
- References
- 12
- Citations by year
- 2026: 2 · 2024: 1 · 2022: 2 · 2021: 3
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-07-02)
- Open access
- hybrid
- doi.org
- Locations
- Journal of Healthcare Engineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Secretary of Public Education of Mexico
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- patellar reflex, knee joint angle, angular velocity, inertial measurement unit, Jendrassik maneuver, biomechanical features, machine learning classification, neuromuscular disorders
- MeSH terms
- Adult, Algorithms, Bayes Theorem, Biomechanical Phenomena, Computer Graphics, Female, Humans, Knee Joint, Male, Pattern Recognition, Automated, Reflex, Stress, Mechanical, United States, User-Computer Interface, Reproducibility of Results, Patellar Ligament, Young Adult, National Institute of Neurological Disorders and Stroke (U.S.), Healthy Volunteers
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