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The Effect of Removing the Force Feedback during the Quiet Stance Jyrki Rasku Department of Computer Sciences, University of Tampere, Finland.

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Presentation on theme: "The Effect of Removing the Force Feedback during the Quiet Stance Jyrki Rasku Department of Computer Sciences, University of Tampere, Finland."— Presentation transcript:

1 The Effect of Removing the Force Feedback during the Quiet Stance Jyrki Rasku Department of Computer Sciences, University of Tampere, Finland

2 Outline Motivation Motivation Human postural control system Human postural control system Motor control output Motor control output My previous work My previous work Current work Current work Test setup Test setup Materials Materials Methods Methods Results from different point of views Results from different point of views My future work My future work

3 Motivation Improve the quality of life among the oldest old. (primary) Improve the quality of life among the oldest old. (primary) Search the useful features which characterizes the human swaying processes. (understanding the problem) Search the useful features which characterizes the human swaying processes. (understanding the problem) Predicting the risk of falling. (diagnosis and rehabilitation) Predicting the risk of falling. (diagnosis and rehabilitation)

4 Human postural control system Relation to the environment Somatosensory information Vestibular information Visual information Integration of information Motor Control Repositioning of limbs and eyes Measurable

5 Motor control output Force platform is used to measure the swaying caused by motor control output Result from force platform is called stabilogram

6 My previous work 1/3 Research of useful stabilogram features. (lengths of successive turning points, average acceleration between successive turning points, AR-model parameters of individual components of stabilogram signals, lengths of passpand filtered stabilogram signals) Research of useful stabilogram features. (lengths of successive turning points, average acceleration between successive turning points, AR-model parameters of individual components of stabilogram signals, lengths of passpand filtered stabilogram signals)

7 My previous work 2/3 Prediction of the class of an unknown stabilogram signal. (hidden Markov models, neural networks, k-nearest neighbor) Prediction of the class of an unknown stabilogram signal. (hidden Markov models, neural networks, k-nearest neighbor) Eyes open or closed Eyes open or closed Healthy or an otoneurological patient Healthy or an otoneurological patient Young or an elderly person Young or an elderly person

8 My previous work 3/3 Relation to the environment Somatosensory information Vestibular information Visual information Integration of information Motor Control Repositioning of limbs and eyes Altered

9 Current work Relation to the environment Somatosensory information Vestibular information Visual information Integration of information Motor Control Repositioning of limbs and eyes Altered

10 Test setup 1/2 Vibrator attachment Posture Data acquisition 50Hz

11 Test setup 2/2 Three measurement from every subject Three measurement from every subject First with the eyes open First with the eyes open Second with the eyes closed Second with the eyes closed Third with the eyes closed and the vibrators on Third with the eyes closed and the vibrators on Romberg test Vibration test

12 Materials 82 healthy young medical students 23±2 years. (66 females, 16 males) 82 healthy young medical students 23±2 years. (66 females, 16 males)

13 Methods 1/2 All signals were lowpass filtered in order to suppress the effect of vibrators. All signals were lowpass filtered in order to suppress the effect of vibrators. Romberg quotient R was calculated for every subject Romberg quotient R was calculated for every subject Vibration quotient Q was calculated for every subject Vibration quotient Q was calculated for every subject

14 Methods 2/2

15 Results (subject’s point of view) The ensemble average velocity graph from the vibration tests. Subjects had difficulties to maintain balance

16 Results (research point of view) Quotients Q and R are not correlated Quotients Q and R are not correlated Correlation coefficient between Q and R was 0.074 Correlation coefficient between Q and R was 0.074 p-value for accepting the null hypothesis (coefficients for linear model are 0) was 0.5 p-value for accepting the null hypothesis (coefficients for linear model are 0) was 0.5 Quotietens Q and R measure different aspects from human postural control system. Vision and somatosensory system augment each other Quotietens Q and R measure different aspects from human postural control system. Vision and somatosensory system augment each other

17 My future work Repeat the measurements on otoneurological patients and elderly subjects Repeat the measurements on otoneurological patients and elderly subjects Research if it possible to get better results in classification of stabilogram signals than earlier with quotient Q included Research if it possible to get better results in classification of stabilogram signals than earlier with quotient Q included

18 Discussion Thank you for listening… Thank you for listening…


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