Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors

Abstract

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).

Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test

“…The FOG-provoking test was made of short-distance trials with turns and few walking bouts, compared to the datasets from both studies that contained longer recordings of free walking. Difficulties in reproducing the results of previously published methods of automatic FOG detection were already described [23,26,37]. More specifically, the importance of validation of ML methods on different cohorts was recently highlighted [6].…”

“…Still, for some applications, it might be helpful to increase detection performance. This might be achieved by adding additional sensor types (e.g., EMG [38] and ECG [39]) to help differentiate some of the more challenging conditions (e.g., akinetic FOG vs. standing still), by focusing on the subtypes of episodes (e.g., during a turn and start hesitation) or by applying techniques like deep learning [37]. The present work can also be extended to develop an automated and instrumented scoring.…”

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

“…Different locations have been explored for sensor positioning, including waist, shin, thigh, foot and chest [ 25 ]. Most experimental protocols are carried out in laboratory settings [ 26 ], and include a set of activities such as walking, turning, timed up and go (TUG) test [ 27 ] and simulated activities of daily living (ADL) [ 19 , 28 ].…”

Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation

“…Unlike neural networks with a single hidden layer NN, CNN are able to learn how to extract more detailed features as its depth increases. Being based on the visual cortex, CNN models are commonly used in image-based applications, but have also been used in inertial sensor-sensor based applications such as activity recognition [ 31 ] and the detection of Parkinson’s disease-related events [ 32 , 33 ]. In such cases, instead of using an image input, the input is typically replaced by a 2D ( n × m ) matrix containing n samples from m inertial sensor signals.…”