Angular rate measurement in the assessment of patellar reflex

Salazar, Yolocuauhtli; García-Caballero, Blanca E.; Munoz-Rios, Refugio; López-Pérez, G.; Ruano-Calderón, Luis

doi:10.1109/ciep.2016.7530755

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…In Figure 5, the maximum value reached by the average of the velocity signals of 3+ is 38 degrees per second. This value is the V max feature and is attenuated by 31% in the mean signal of the 2+ group, by 76% for the 1+ group, and by 95% for the 0+ group [20].…”

Section: Resultsmentioning

confidence: 99%

“…According to the previous works of Salazar-Muñoz et al [19, 20] and Moreno-Estrada et al [21], the designed device uses an impact sensor as the start time marker of the test and an inertial measurement unit (IMU) to measure both the angular velocity and angular position of the leg after it receives the hammer stroke on the tendon. The measurement system consists of the following two parts.…”

Section: Methodsmentioning

confidence: 99%

“…In a previous work, this research team designed a device to measure, digitally store, and display the patellar reflex response [19], capturing the relation between velocity and the magnitude of the response [20]. The aim of this study is to analyze the captured biomechanical variables, including the angle of the knee, the velocity of the knee movement, the applied force, and the magnitude of the reflex response, in order to develop an automatic classification algorithm using digital signal processing and machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

Salazar

López-Pérez

García-Caballero

et al. 2019

Journal of Healthcare Engineering

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

Salazar

López-Pérez

García-Caballero

et al. 2019

Journal of Healthcare Engineering

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“…Many previous works have developed techniques to quantify the DTR response with precision unmatched by the human eye. Salazar-Muñoz et al [7] constructed a controlled hammer-strike and leg swing detection system using an angular displacement accelerometer and gyroscope. After applying principal component analysis and running different combinations of data parameters through four data classifier models, the researchers found that a Naïve Bayes classifier could classify the data into the appropriate NINDS scale with 89.62% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Patellar Deep Tendon Reflex Using Millimeter-Wave Radar and Motion Capture Technologies

Bresnahan,

Koziol,

2024

IEEE Access

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Physicians typically measure deep tendon reflexes visually, leading to ambiguity and disagreement over exact reflex classification. Millimeter-wave radar addresses this problem by providing an accurate, unambiguous measurement of reflex limb motion and features noncontact sensing for convenience and patient comfort. Radar spectrograms closely match optical motion capture results, supporting radar's viability as a clinical assessment tool. This study analyzes data from 60 radar and motion capture measurement trials across four subjects. Six reflex characteristics are defined and extracted. The extracted parameters show a high level of agreement between the two different techniques, with a mean relative error of only 10.39%. Additionally, a positive correlation was observed between hammer tap speed and reflex response speed, with maximum leg velocities showing a slope of 0.4. This study also quantifies and discusses the effects of hammer tap speed and leg length. In the future, physicians may use a specialized radar system to assess reflex performance quickly, accurately, and comfortably for a patient under test.INDEX TERMS Human activity recognition, body sensor networks, biomedical applications of radiation, millimeter wave radar, clinical neuroscience.

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Angular rate measurement in the assessment of patellar reflex

Cited by 2 publications

References 16 publications

Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

Investigation of Patellar Deep Tendon Reflex Using Millimeter-Wave Radar and Motion Capture Technologies

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