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

Sensors

Susana Costa¹  Nélson Costa¹  Pedro Arezes¹  Guillermo De Arcas²  Ignacio Pavón²  JuanManuel López²  Luis Sigcha² 

[1] ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal;Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain;

关键词: IMU;    accelerometer;    convolutional neural networks;    LSTM;    consecutive windows;    denoising autoencoder;   

DOI  :  10.3390/s20071895

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown