Fuzzy control in gait pattern classification using wearable sensors

Author(s):  
Yingjun Xu ◽  
Weihai Chen ◽  
Jianhua Wang ◽  
Jianbin Zhang
Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 329 ◽  
Author(s):  
Yunqi Tang ◽  
Zhuorong Li ◽  
Huawei Tian ◽  
Jianwei Ding ◽  
Bingxian Lin

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.


2003 ◽  
Vol 18 (1) ◽  
pp. 114-125 ◽  
Author(s):  
Sara Mulroy ◽  
JoAnne Gronley ◽  
Walt Weiss ◽  
Craig Newsam ◽  
Jacquelin Perry

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuang Wu ◽  
Xu Jiang ◽  
Min Zhong ◽  
Bo Shen ◽  
Jun Zhu ◽  
...  

Background and Purpose. Patients with early-stage Parkinson’s disease (PD) have gait impairments, and gait parameters may act as diagnostic biomarkers. We aimed to (1) comprehensively quantify gait impairments in early-stage PD and (2) evaluate the diagnostic value of gait parameters for early-stage PD. Methods. 32 patients with early-stage PD and 30 healthy control subjects (HC) were enrolled. All participants completed the instrumented stand and walk test, and gait data was collected using wearable sensors. Results. We observed increased variability of stride length (SL) ( P < 0.001 ), stance phase time (StPT) ( P = 0.004 ), and swing phase time (SwPT) ( P = 0.011 ) in PD. There were decreased heel strike (HS) ( P = 0.001 ), range of motion of knee ( P = 0.036 ), and hip joints ( P < 0.001 ) in PD. In symmetry analysis, no difference was found in any of the assessed gait parameters between HC and PD. Only total steps ( AUC = 0.763 , P < 0.001 ), SL ( AUC = 0.701 , P = 0.007 ), SL variability ( AUC = 0.769 , P < 0.001 ), StPT variability ( AUC = 0.712 , P = 0.004 ), and SwPT variability ( AUC = 0.688 , P = 0.011 ) had potential diagnostic value. When these five gait parameters were combined, the predictive power was found to increase, with the highest AUC of 0.802 ( P < 0.001 ). Conclusions. Patients with early-stage PD presented increased variability but still symmetrical gait pattern. Some specific gait parameters can be applied to diagnose early-stage PD which may increase diagnosis accuracy. Our findings are helpful to improve patient’s quality of life.


2002 ◽  
Vol 19 (3) ◽  
pp. 378-391 ◽  
Author(s):  
Sarah J. Woodruff ◽  
Connie Bothwell-Myers ◽  
Maureen Tingley ◽  
Wayne J. Albert

The purpose was to develop an index of walking performance and to examine gait pattern classifications of children with developmental coordination disorder (DCD). The San Diego database (Sutherland, Olshen, Biden, & Wyatt, 1988) provided data for our calculation of the index and for determining that the index was able to differentiate between gait variables of older (ages 3 to 7) and younger (ages 1 to 2.5) children comprising the database. We obtained cinematographical data on 17 biomechanical markers of 6 boys and 1 girl, ages 6 to 7, with DCD, during walking. Analysis of individuals with DCD gait patterns revealed that most had abnormal walking patterns. The means of the time/distance gait variables did not differ between children with DCD and San Diego children, ages 3 to 7. Children with DCD had much larger variances than other children, indicating no systematic pattern in individual gait differences.


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