Impact of Data Preparation in Freezing of Gait Detection Using Feature-Less Recurrent Neural Network

Ali Haddadi Esfahani,Steffen Ortmann,Peter Langendorfer,Zoya Dyka

doi:10.1109/access.2021.3117543

Abstract

Many studies showed the feasibility of detecting Freezing of Gait (FOG) of Parkinson’s patients by using several numbers of inertial sensors worn on the body and in back-end computing power. This work uses machine learning approaches for analyzing the data of one single body-worn inertial sensor system to classify and detect FOG. Long-Short-Term-Memory (LSTM) is employed as the FOG detection algorithm and the Daphnet (FOG and normal gait) dataset provides the data for model training and testing in this paper. The model considers raw data from three channels of the acceleration sensor mounted on the patient’s shank and ignores all other data from other sensors. The model is patient dependent and uses sensitivity and specificity metrics to evaluate the model’s performance. In this paper, we propose a novel padding method that is applied to the windows of FOG and non-FOG with zero overlaps on the training set and adapts the padding to the individual regions. This method produces windows of only one type of data and label. The proposed padding method reduces the padding amount by two orders of magnitude compared to bigger batch sizes in the sequence splitting method offered by MATLAB 2019a. The padding amount is independent of the batch size. Raw data is fed to the model in the testing mode without any pre-processing or data transformation. The standard rolling window generates fixed-size windows for the test set without overlap and the higher amount of FOG or Normal walking data which defines the label of the individual window. The model for one-second long windows applied in this work outperformed the literature results with a sensitivity of 92.57% and a specificity of 95.62% compared to 82% and 94% reported by Masiala et al.

Highlights

Parkinson disease (PD) is a long-term effect on selective neurological conditions mostly diagnosed for elderly people [1]
Many studies showed the feasibility of detecting Freezing of Gait (FOG) of Parkinson patients by using several numbers of inertial sensors worn at the body and back-end computing power
We propose a novel padding method that is applied to the windows of FOG and non-FOG with zero overlaps on the training set and adapts the padding to the individual regions

Summary

Introduction

Parkinson disease (PD) is a long-term effect on selective neurological conditions mostly diagnosed for elderly people [1]. The disease degenerates the neurons, reducing the dopamine substance, which leads to alteration on the motor and non-motor symptoms like Parkinson’s tremors [2]. It mainly affects motor symptoms such as gait disturbance, gait festinating, Freezing of Gait (FOG), slowness of movements, gastrointestinal disturbance, postural instability, and falls [3]. We are aware of the fact that the progress of the Parkinson disease impacts parameters indicating FOG. This means that the LSTM network needs to be trained with individual Patients’ data from time to time, but this is out of scope of this paper. There are non-motor impacts of Parkinson such as depression and hallucination, sleep disorder, and personality change [5]

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Discussion

Conclusion