Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson's Disease Using Wearable Sensors

Abstract

Freezing of gait (FOG) is a disabling symptom that is common among patients with advanced Parkinson's disease (PD). External cues such as rhythmic auditory stimulation can help PD patients experiencing freezing to resume walking. Wearable systems for automatic freezing detection have been recently developed. However, these systems detect a FOG episode after it has happened. Instead, in this study, a new approach for the prediction of FOG (before it actually happens) is presented. Prediction of FOG might enable preventive cueing, reducing the likelihood that FOG will occur. Moreover, understanding the causes and circumstances of FOG is still an open research problem. Hence, a quantitative characterization of movement patterns just before FOG (the pre-FOG phase) is of great importance. In this study, wearable inertial sensors were used to identify and quantify the characteristics of gait during the pre-FOG phase and compare them with the characteristics of gait that do not precede FOG. The hypothesis of this study is based on the threshold-based model of FOG, which suggests that before FOG occurs, there is a degradation of the gait pattern. Eleven PD subjects were analyzed. Six features extracted from movement signals recorded by inertial sensors showed significant differences between gait and pre-FOG. A classification algorithm was developed in order to test if it is feasible to predict FOG (i.e., detect it before it happens). The aim of the classification procedure was to identify the pre-FOG phase. Results confirm that there is a degradation of gait occurring before freezing. Results also provide preliminary evidence on the feasibility of creating an automatic algorithm to predict FOG. Although some limitations are present, this study shows promising findings for characterizing and identifying pre-FOG patterns, another step toward a better understanding, prediction, and prevention of this disabling symptom.

Keywords: Parkinson’s disease; classification; data analysis; freezing of gait; inertial measurement unit; machine learning; prediction; wearable sensors.

Figures

Figure 1

Setup that was considered for the analysis.

Figure 2

Workflow of data processing.

Figure 3

An example of the segmentation of the recorded signal in gait and pre-FOG windows. The first plot from the top shows the recorded angular velocities of the sensors on the left and right ankles together with the segmentation of the windows. The second plot is the norm of the angular velocity of the sensors on the left and right ankles. The third plot is the norm of the acceleration of the sensor on the lower back. These two norms are used to perform the check for sufficient motion in a window.

Figure 4

Paired t-test results for each feature. For each feature, two plots are present. On the left the mean and SD values are reported, together with corresponding p-value and statistical significance (*). On the right, the values of each pair that was considered in the statistical testing are reported.

Figure 5

An example of the application of the classifier. The first plot from the top shows the recorded angular velocities of the left and right ankles together with the segmentation of the windows, as in Figure 3. The second plot reports the probability of incoming FOG, as predicted by the classifier. This probability is computed for each gait and pre-FOG window. The threshold on probability is also reported: if the probability is higher than the threshold, then the classifier predicts a FOG (i.e., it identifies the window as pre-FOG), otherwise, the classifier identifies the window as gait. In the last three plots, the values of the three features which are used in the classifier are reported.