Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors

Abstract

Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.

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

“…The highest AUC (97%) was achieved by Borzì et al (2019) and the lowest AUC (76%) was in Palmerini et al (2017) A few studies utilized f-score to evaluate validation performance ranging from 77.98 to 92.10%. The lowest f-score (77.98%) was reported by Ren et al (2022) and the highest f-score (92.10%) was written by Halder et al (2021). Meanwhile, the measure of validation performance various considerably, including accuracy (73.33%, n = 1), root mean square error (0.16, n = 1) and data difference (n = 1).…”

A Paradigm Shift in the Management of Patients with Parkinson’s Disease

“…Despite significant technological advancements and the incorporation of new features in monitoring devices, wearables still face certain limitations. One major pitfall lies in their accuracy in symptom detection, as there is considerable variability in sensitivity and specificity, ranging from 0.39 to 0.97, across different devices and study designs [9,55,[58][59][60]. It is not surprising that objective measurements do not always align perfectly with subjective symptom estimations, but the correlation between supervised and unsupervised motor function assessments is also not flawless [61].…”

Stage-Wise Data Balancing Promoting Toe-tapping-based Classification of Parkinson's Disease Progression using Smart Insoles

“…From this preprocessed data, we extrapolate a comprehensive set of 120 features in total from 8 axes of . From [7,20], these features had the potential to capture the nuanced movement characteristics affected by PD. These encompass (i) 10 highly-ranked statistical features per axis in the time domain: Mean, Standard deviation (σ), Minimum (Min), Maximum (Max), Range, Root Mean Square (RMS), Skewness, Kurtosis, and Zero Crossing Rate (ZCR) and the Mean Crossing Rate (MCR) per axis; and (ii) 5 highly-ranked features in frequency domain: Peak frequency, Cepstral coe cients (comprising of mean, max, and min coe cients), and Spectral entropy.…”