Abstract
Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p>0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.
Highlights
Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease and the most common movement disorder [1]
We investigate for the first time the use of spatio-temporal gait parameters for machine learning to classify a completed foot motion phase into normal or FoGaffected
In case of the classifiers C0 trained on the actual motion phase, the trained Support Vector Machine (SVM) model with all 10 features (SVM_10) performed on average over all patients with 80.2, 85.8, 84.6, and 90.2% for specificity, sensitivity, accuracy, and Area Under the Curve (AUC), respectively
Summary
Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease and the most common movement disorder [1]. It is characterized by four cardinal and motor disabling symptoms, bradykinesia, rigidity, tremor, and postural instability. Deuschl et al [2] reported that the prevalence of PD worldwide is 8.5 million in 2017. The global incidence ranges between 0.85 and 1.2 million. The most common treatment is dopaminergic medication. The effects of dopaminergic medications diminish over time and become less beneficial with the development of the disease. Gait and posture impairments are often resistant to the pharmacological treatment, and worsen as the disease progresses
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