Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

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 a...

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Main Authors: Yolocuauhtli Salazar-Muñoz, G. Angelina López-Pérez, Blanca E. García-Caballero, Refugio Muñoz-Rios, Luis A. Ruano-Calderón, Leonardo Trujillo
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Journal of Healthcare Engineering
Online Access:http://dx.doi.org/10.1155/2019/1614963
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spelling doaj-b504288a9fe5472ba4b0c9784ae14a772020-11-25T01:32:33ZengHindawi LimitedJournal of Healthcare Engineering2040-22952040-23092019-01-01201910.1155/2019/16149631614963Classification and Assessment of the Patelar Reflex Response through Biomechanical MeasuresYolocuauhtli Salazar-Muñoz0G. Angelina López-Pérez1Blanca E. García-Caballero2Refugio Muñoz-Rios3Luis A. Ruano-Calderón4Leonardo Trujillo5Tecnológico Nacional de México/Instituto Tecnológico de Durango, C.P. 34080, Durango, DGO, MexicoTecnológico Nacional de México/Instituto Tecnológico de Durango, C.P. 34080, Durango, DGO, MexicoTecnológico Nacional de México/Instituto Tecnológico de Durango, C.P. 34080, Durango, DGO, MexicoTecnológico Nacional de México/Instituto Tecnológico de Durango, C.P. 34080, Durango, DGO, MexicoServicios de Salud del Estado de Durango, Hospital General 450, C.P. 34206, Durango, DGO, MexicoTecnológico Nacional de México/Instituto Tecnológico de Tijuana, C.P. 22430, Tijuana, B.C., MexicoClinical 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.http://dx.doi.org/10.1155/2019/1614963
collection DOAJ
language English
format Article
sources DOAJ
author Yolocuauhtli Salazar-Muñoz
G. Angelina López-Pérez
Blanca E. García-Caballero
Refugio Muñoz-Rios
Luis A. Ruano-Calderón
Leonardo Trujillo
spellingShingle Yolocuauhtli Salazar-Muñoz
G. Angelina López-Pérez
Blanca E. García-Caballero
Refugio Muñoz-Rios
Luis A. Ruano-Calderón
Leonardo Trujillo
Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
Journal of Healthcare Engineering
author_facet Yolocuauhtli Salazar-Muñoz
G. Angelina López-Pérez
Blanca E. García-Caballero
Refugio Muñoz-Rios
Luis A. Ruano-Calderón
Leonardo Trujillo
author_sort Yolocuauhtli Salazar-Muñoz
title Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
title_short Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
title_full Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
title_fullStr Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
title_full_unstemmed Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures
title_sort classification and assessment of the patelar reflex response through biomechanical measures
publisher Hindawi Limited
series Journal of Healthcare Engineering
issn 2040-2295
2040-2309
publishDate 2019-01-01
description 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.
url http://dx.doi.org/10.1155/2019/1614963
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