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Development of a Fall Risk Assessment Method Based on COP Data in Parkinson’s Disease

Shokouhi, Shabnam | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50486 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Behzadipour, Saeed
  7. Abstract:
  8. In recent years, there has been an increasing interest in posturography methods as an objective quantitative tool for assessing balance and indicating patient with high risk of fall. Although many posturographic studies tried to describe balance deficiencies in PD, little success has been achieved in utilizing static posturography as a tool for discriminating faller and non-faller PD patients. Furthermore, Dynamic posturography studies have delivered valuable insight into the potentiality of these methods for assessing balance, but these devices are very high in price and cumbersome to move that can limit the feasibility of their use in the clinical settings. The aims of this study were: 1) to devise an affordable device for fall risk assessment using a modified balance board and some motor-cognitive dynamic tests, 2) to determine the relationship between clinical balance scales and posturographic parameters, and 3) To accurately classify PD patients into Faller and Non-faller groups. Forty-five PD subjects (Hoehn & Yahr stage 1–3) participated in the study divided into faller (15 subjects) and non-faller (30 subjects) based on their Berg balance scale and their history of falling. Subjects completed two motor-cognitive dynamic balance test, limit of stability and random control test. Among the measures from the limits of stability test, reaction time, mean velocity, directional control, functional area, and among the measures from random control test, the time fraction showed a significant difference between groups (p<0.01). These parameters also had a relatively strong correlation with clinical scales. This study suggests that a combination of the parameters might show a stronger correlation with clinical scales (r=0.76 with BBS & r=0.72 with ABC ). In addition, a Gaussian radial basis function kernel based Support vector machine classifier can even achieve an excellent overall sensitivity of 100 for this data set
  9. Keywords:
  10. Parkinson Disease ; Attitude Control ; Fall Risk ; Fall Risk Assessment ; Postural Instability

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