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Gait Feature Vectors for Post-stroke Prediction using Wearable Sensor 원문보기

감성과학 = Science of emotion & sensibility, v.22 no.3, 2019년, pp.55 - 64  

Hong, Seunghee (Center for Medical Convergence Metrology, KRISS) ,  Kim, Damee (Center for Medical Convergence Metrology, KRISS) ,  Park, Hongkyu (Department of KSB Convergence Research, ETRI) ,  Seo, Young (Center for Medical Convergence Metrology, KRISS) ,  Hussain, Iqram (Center for Medical Convergence Metrology, KRISS) ,  Park, Se Jin (Center for Medical Convergence Metrology, KRISS)

Abstract AI-Helper 아이콘AI-Helper

Stroke is a health problem experienced by many elderly people around the world. Stroke has a devastating effect on quality of life, causing death or disability. Hemiplegia is clearly an early sign of a stroke and can be detected through patterns of body balance and gait. The goal of this study was t...

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제안 방법

  • After standing in static condition, participants performed 20m gaiting test along the guide tape line. Fig.
  • The stroke patients were limited to those who could walk more than 30m independently without a walking frame. All participants performed basic physical examinations (height, weight, electrocardiogram, blood pressure, blood test) before the experiment, and participants were paid participation fee including transportation expenses. In addition, the participants were instructed to give up the experiment at any time according to the health condition of the experiment day Proposed Gait monitoring system is consists of insole foot Pressure sensor and accelerometer which will be attached to foot as shoe insole for gathering gait speed, foot pressure and other gait signals.
  • Hyper-connected self-machine learning engine controls the system. The elements of the system are a knowledge base, big data, network security, real-time data monitoring using wearables, and self-learning engine. The knowledge base would have risk factors, medical health records, psycho-logical factors, gait and motion patterns, and bio-signals.
  • 0 software. The measured features were the pressure per cell, the center of pressure [CoP; CoP_Left_x axis (medial, lateral), CoP_Left_y axis (anterior, posterior), CoP_Right_ x axis (medial, lateral), CoP_Right_ y axis (anterior, posterior)], the acceleration (Acc._left foot, acc._right foot,), and the ground reaction force (GRF; GFR._left foot, GFR._ right foot,).
  • The purpose of this study was to extract the optimal variables to detect abnormalities of stationary posture and walking during daily life, and to find the difference between stroke patients and the normal elderly.

대상 데이터

  • 220 normal elderly people over 65 years old and 63 elderly patients who were less than 6 months after the onset of stroke participated in this study as in the Table 1. Study was performed in A National University Hospital, Daejeon, South Korea.
  • 220 normal elderly people over 65 years old and 63 elderly patients who were less than 6 months after the onset of stroke participated in this study as in the Table 1. Study was performed in A National University Hospital, Daejeon, South Korea. Healthy participants were recruited to the elderly enrolled at the local silver center, and patients were recruited for the elderly who were in hospital or after rehabilitation.

데이터처리

  • The maximum and average accelerations of the feet were measured from the accelerometer embedded in the insole, and paired t-test was performed. In the Fig.
  • After data preprocessing (outlier removal), patient data were amplified to adjust the data distribution ratio of stroke patients and normal persons. Then statistical analysis method was paired t-test at a confidence level of 95%.
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참고문헌 (26)

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