Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures

Abstract

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.

Quantification of patellar tendon reflex using portable mechanomyography and electromyography devices

“…Other devices have also been used to evaluate the PTR response. An accelerometer was used to demonstrate the difference between myelopathy patients and healthy volunteers in terms of the PTR response 7 , and another study reported an accuracy of 89.62%, according to the NINDS score, using machine learning classifiers 30 . A motion capture system was also used to demonstrate the knee angle increase dependent on the tapping force in healthy participants 15 , 16 and its usability for cerebral palsy patients 31 .…”

Characterization of the patellar tendon reflex response using an indigenously developed system and implementation of a strategic protocol to assess its clinical usefulness

“…4 Pathological alterations in these reflexes are important signs of neurological diseases and distinguishing between delayed or exaggerated PTR responses may help to detect neurological disorders. 5 , 6 The reflex latency and amplitude of PTR response may vary in people with neurological, neuromuscular, and endocrine disorders. 7 , 8 Therefore, observation of irregularities in kinematics of tendon refelexes provides valuable insight into the diagnosis and prognosis of the patient's neurological condition.…”