A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction

Abstract

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

“…Standardizing data collection protocols for FOG data across studies can enhance data consistency and facilitate comparisons between different datasets. Moreover, researchers can use advanced techniques for handling class imbalance, such as oversampling [ 97 , 142 , 143 ], undersampling [ 67 , 68 ], or employing ensemble methods [ 58 , 68 , 100 ], to ensure robust performance in classification tasks. By implementing these strategies, researchers can tackle the challenges associated with dataset limitations and class imbalance effectively, thereby improving the reliability and generalizability of their findings in FOG research.…”

“…This choice has a significant effect on the next steps, particularly the preprocessing methods. Preprocessing for FOG detection may involve class imbalance management using techniques like ensemble approaches [ 58 , 68 , 100 ], downsampling [ 67 , 68 ], and oversampling [ 97 , 142 , 143 ]. Additionally, segmenting the signal into windows and experimenting with single or multiple window sizes can optimize detection accuracy.…”

Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson’s disease: A systematic review

“…To improve the accuracy of FOG and fall detection, machine learning algorithms, including SVM ( Tzallas et al, 2014 ; Ahlrichs et al, 2016 ; Iakovakis et al, 2016 ; Rodríguez-Martín et al, 2017 ; Aich et al, 2018 ; Arami et al, 2019 ; Borzì et al, 2019 , 2021 ; Kleanthous et al, 2020 ; Reches et al, 2020 ; Dvorani et al, 2021 ; El-Attar et al, 2021 ; Ghosh and Banerjee, 2021 ; Mesin et al, 2022 ), k-NN ( Aich et al, 2018 ; Borzì et al, 2019 ; Demrozi et al, 2020 ; Halder et al, 2021 ; Mesin et al, 2022 ), decision trees ( Aich et al, 2018 ; Borzì et al, 2019 ; Pardoel et al, 2021 , 2022 ), hidden Markov model ( Tzallas et al, 2014 ; San-Segundo et al, 2019 ), neural network ( Cole et al, 2011 ; Iakovakis et al, 2016 ; Ly et al, 2017 ; Saad et al, 2017 ; Kim et al, 2018 ; Arami et al, 2019 ; Borzì et al, 2019 ; Mikos et al, 2019 ; Kleanthous et al, 2020 ; O’day et al, 2020 , 2022 ; Shi et al, 2020 , 2022 ; Sigcha et al, 2020 ; Ashfaque Mostafa et al, 2021 ; El-Attar et al, 2021 ; Prado et al, 2021 ; Naghavi and Wade, 2022 ), random forest ( San-Segundo et al, 2019 ; Kleanthous et al, 2020 ; Ghosh and Banerjee, 2021 ) and LSTM ( Li et al, 2020 ; Ashfaque Mostafa et al, 2021 ; Esfahani et al, 2021 ; Shalin et al, 2021 ; Guo et al, 2022 ), were used extensively in recent studies. Data were collected from sensors, and a training period is necessary for machine learning.…”

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson’s Disease

“…The approaches based on DL have gained increasing attention, as they allow modelling the data characteristics and motion patterns that best represent FoG and distinguish it from other actions. A wide variety of solutions have been proposed, including convolutional neural networks (CNNs) [31,32], recurrent neural networks [33,34], transformer networks [35], and deep autoencoders [36,37]. A significant improvement in performance has been recorded, with sensitivity and specificity up to 0.92 and 0.98, respectively [38,39].…”