Yun Ling
14 Papers
Yun Ling is an academic researcher. The author has contributed to research in topics: Medicine & Parkinson's disease. The author has an hindex of 2, co-authored 10 publications.
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Papers
Wearable sensor-based gait analysis to discriminate early Parkinson’s disease from essential tremor
TL;DR: In this paper , gait and postural transition parameters obtained from wearable sensors combined with machine learning had the potential to distinguish between early-stage Parkinson's disease and essential tremor (ET).
Recognition of freezing of gait in Parkinson’s disease based on combined wearable sensors
TL;DR: In this paper , the authors used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods, and selected the optimal features to construct FoG recognition model based on random forest.
Specific Distribution of Digital Gait Biomarkers in Parkinson's disease Using Body-Worn Sensors and Machine Learning.
Guoen Cai,Weikun Shi,Yingqing Wang,Huidan Weng,Lina Chen,Jiao Yu,Zhonglue Chen,Fabin Lin,Kang Ren,Yuqi Zeng,Jun Liu,Yun Ling,Qinyong Ye +12 more
TL;DR: In this article , the authors used whole-body multinode wearable devices combined with machine learning to build a classification model of early PD (EPD) and mild PD (MPD), which exhibited significantly improved accuracy for the EPD and MPD groups compared with the healthy control (HC) group.
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Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision
Wendan Li,Xiujun Chen,Jintao Zhang,Jianjun Lu,Chencheng Zhang,Hongmin Bai,Junchao Liang,Jiajia Wang,Hanqiang Du,Gaici Xue,Yun Ling,Kang Ren,Weishen Zou,Cheng Chen,Mengyan Li,Zhonglue Chen,Haiqiang Zou +16 more
TL;DR: A method to realize remote PD patient FOG recognition based on mobile phone video is presented, which is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.
Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset
TL;DR: In this paper , three feature selection algorithms (LASSO, mRMR, Relief-F) were tested to select the vocal features extracted from speech signals collected in a controlled setting, followed by four classifiers (Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Stochastic Gradient Descent) to detect the disorder.