The physician shall place the arm in the desired position and release it manually. The force applied will be the same for all test subjects to generate their own flexion.
Tapping Sensor. Angular Displacement and Rate Sensor. Control Unit. The GUI was designed in LabView to display the sensor readings in real time and save the captured signals of each test in an lvm file. Each new test generates a new file that is then imported into Matlab for later analysis. The GUI shows the following indicators in real time: the angular displacement, the angular velocity, and the moment of impact on the tendon.
In this work, we use a group of healthy volunteers to evaluate our proposed system. The mean age, height, and body mass for subjects were A volunteer is considered to be healthy for this study if he is not suffering from any diagnosed neurological or neuromuscular disease when the test is realized [ 22 ]. The study was approved by the Ethics and Research Committee from the hospital. Experimental tests were performed under the supervision of the physician.
Two reflex tests are applied to every volunteer to develop an automatic classification algorithm using digital signal processing and machine learning algorithms. We compare the NINDS scale with the biomechanical variables registered by the designed measurement system. The volunteer must be seated in a high chair, this way his right foot never touches the floor. In order to get a high relaxation of the quadriceps muscle, the volunteer is requested to perform the Jendrassik maneuver [ 23 ].
All the tests were performed under the same conditions. Test A. A physician gives a sharp tap on the patellar tendon with a standard clinical hammer. Dafkin et al. Therefore, the trained physician was asked to focus on this feature to provide his rating for the analyzed patients. Test B. After Test A, the sensors are placed on the leg of the volunteer as shown in Figure 1 , and the procedure is as follows: a the taping sensor impact sensor is adhered to the patellar tendon with tape, below the patella to avoid any undesired movements and b the IMU is placed on the ankle using a belt.
The distance between the knee centre of rotation and location of the sensor in all subjects was maintained small following the reference [ 24 ]. The IMU must be positioned parallel to the leg and perpendicular to the floor. The controlled force system hits the patellar tendon. The data acquisition system stores all sensor readings using the GUI that was designed for this experiment.
After this procedure, the measurement system is withdrawn from the leg. This test was performed under the physician who verifies that the reflex response was equivalent to Test A. No test was rejected because it ranked differently from the Test A.
Schematic representation of the experimental system to obtain the patellar reflex response, showing the physical setup and sensor locations. The data stored by the system contain three time series. The first one is the impact signal, which marks the exact moment when the pendulum hits the tendon, denoted by t o. The second time series is the angular position signal, which measures the angle of the leg during the reflex response.
The third time series is the angular velocity of the leg movement during the test. Afterward, the signals of the angular position and angular velocity are characterized by extracting the following set of descriptive features. The extracted features are summarized in Figure 2 for the angular position and in Figure 3 for the angular velocity, each case showing a typical signal captured by the system for each measurement.
From the angular position signal, the extracted features are as follows. In the case of the angular velocity, a single feature is extracted called V max , which is the maximum value of the signal, shown in Figure 3 as the highest peak. To achieve the classification of the realized patellar reflex tests based on the number of crossings in the NINDS scale, basic pattern recognition and machine learning methods are used [ 25 , 26 ].
Specifically, the following four classifiers are used:. Classifiers are tested with different combinations of the extracted features. Because the size of the dataset is relatively small, each classifier is tested using leave-one-out cross validation. Moreover, the data are preprocessed for feature reduction using principal component analysis PCA. First, we analyze the recorded signals from each response level, to determine if there are any general similarities between them.
Figure 4 shows that the movement of the leg after the impact has a wavelike behavior, which decreases with time until it stabilizes to the rest position.
This peak corresponds to the maximum elevation of the leg. To make sure the separation between groups is significant, the Kruskal—Wallis statistical test is applied to every feature. The test is chosen because the data distribution is not Gaussian. Different combinations of features are selected based on the statistical results and used as the input data for the machine learning classifiers.
The tests are carried out using leave-one-out cross validation LOO CV , given the relatively low number of samples in the database. Table 2 shows all of the tested combinations and the classification accuracy of each classifier. In each case, principal component analysis PCA is applied to the input features to perform feature transformation but results are only shown for the case in which PCA improved the performance of at least one classifier. The points are labeled to show the correctly classified sample from each group, using a different mark for each NINDS level and the misclassified samples as well.
The dark round markers shows misclassified tests by naive Bayes classifier, and all other points were correctly classified into their respective groups. The dynamic behavior of the leg during the patellar reflex creates movement patterns that can be automatically classified in the NINDS scale with a useful degree of accuracy. This is shown to be possible using a straightforward feature extraction procedure and pattern recognition techniques.
However, the muscle contraction won't kick your leg upward this time, because you're standing on it. Instead, the contraction simply brings you back to center, preventing you from falling backward. Give Now ». Noon Edition. Home Archives About Contact. Media Player Error Update your browser or Flash plugin. What's the point of this reflex? Receive New Content by Email. Previous Slide Next Slide Dingman weaves classic studies with modern research into easily digestible sections, to provide an excellent primer on the rapidly advancing field of neuroscience.
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