SlideShare ist ein Scribd-Unternehmen logo
1 von 22
Downloaden Sie, um offline zu lesen
Prediction of Motion Sickness Incidence: Modeling Efforts based on Human Physiology ABCD Meeting 2006 “Human Performance at Sea: Influence of Ship Motions on Biomechanics and Fatigue"   By Lieutenant P. Matsagas, M.Sc., Hellenic Navy [email_address] ,  [email_address]   M.E. McCauley, Ph.D., Naval Postgraduate School [email_address]
Motion Sickness ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cause of motion sickness ,[object Object],Current sensory input Neural store Vestibular system Vision Proprioception Error signal
Motion Sickness Incidence (MSI) ,[object Object],[object Object]
HFR model (1974) Model Characteristics Vertical Acceleration Only true motion MSI: % of people who vomit Two-hour nauseogenic period Nauseogenic frequency range 0.05 – 0.7 [Hz] Central nauseogenic frequency 0.167 [Hz]
Proposed Model Characteristics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Proposed Model Assumptions ,[object Object],[object Object],[object Object]
Current (2006) Model in Detail ,[object Object],[object Object],[object Object],[object Object],[object Object]
Error Estimation Subsystem Oman (1982) Glasauer & Merfeld (1997) Bles et al.  (1998) Merfeld et al. (1993)
Visual System Sandini et al. (2001) Legend Independent variables Intermediate variables Dependent variables Neural Store
Current Model (2006) Adaptation Mechanism   in detail Predicted difference between sensory input and motion in the neural store   Sensory contents in Neural Store 1 2 3 Legend Independent variables Intermediate variables Dependent variables Neural Store
Normalization & Linear Combination of 2 Sources of Error = MSI
Current Model(2006) in Detail
Predicted MSI Proposed Model Characteristics Vertical Acceleration MSI: % of people who vomit Two-hour nauseogenic period Nauseogenic frequency range 0.05 – 0.6 [Hz] Central nauseogenic frequency 0.17 [Hz]
Model Validation True Motion Settings Proposed model HFR model MSI Comparison between Proposed and HFR models 1 2 3
MSI Accumulation Characteristics Vertical Acceleration MSI: % of people who vomit Two-hour nauseogenic period Nauseogenic frequency range 0.05 – 0.6 [Hz] Central nauseogenic frequency 0.17 [Hz]
MSI Habituation
MSI Habituation and Retention
Model significance  I. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Model significance II. ,[object Object],[object Object],[object Object],[object Object],[object Object]
Future Research ,[object Object],[object Object],[object Object],[object Object]
[object Object]

Más contenido relacionado

Ähnlich wie Abcd Presentation

Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...
Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...
Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...pmatsang
 
Access Presentation Matsangas
Access Presentation MatsangasAccess Presentation Matsangas
Access Presentation Matsangaspmatsang
 
Matsangas And Mc Cauley (2005) Model For Predicting Motion Sickness, Adapta...
Matsangas And Mc Cauley (2005)   Model For Predicting Motion Sickness, Adapta...Matsangas And Mc Cauley (2005)   Model For Predicting Motion Sickness, Adapta...
Matsangas And Mc Cauley (2005) Model For Predicting Motion Sickness, Adapta...pmatsang
 
Nps Hsv Motion Presentation
Nps   Hsv Motion PresentationNps   Hsv Motion Presentation
Nps Hsv Motion Presentationpmatsang
 
Balance - Amirhashem.pptx
Balance - Amirhashem.pptxBalance - Amirhashem.pptx
Balance - Amirhashem.pptxAmir724277
 
MAYO CLINIC_BRUNO OTILIO
MAYO CLINIC_BRUNO OTILIO MAYO CLINIC_BRUNO OTILIO
MAYO CLINIC_BRUNO OTILIO Bruno Otilio
 
NMRS 2010 Mirror Therapy Brief
NMRS 2010 Mirror Therapy BriefNMRS 2010 Mirror Therapy Brief
NMRS 2010 Mirror Therapy BriefSteve Hanling
 
Sirevaag_et_al-2016-Psychophysiology
Sirevaag_et_al-2016-PsychophysiologySirevaag_et_al-2016-Psychophysiology
Sirevaag_et_al-2016-PsychophysiologySara Casaccia
 
MEASSuRE_product description
MEASSuRE_product descriptionMEASSuRE_product description
MEASSuRE_product descriptionOliver Graudejus
 
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...Nicole Freitag
 
IEEE Medical image Title and Abstract 2016
IEEE Medical image Title and Abstract 2016 IEEE Medical image Title and Abstract 2016
IEEE Medical image Title and Abstract 2016 tsysglobalsolutions
 
Measuring and Quantifying Sympathetic Control of the Cutaneous Microvasculature
Measuring and Quantifying Sympathetic Control of the Cutaneous MicrovasculatureMeasuring and Quantifying Sympathetic Control of the Cutaneous Microvasculature
Measuring and Quantifying Sympathetic Control of the Cutaneous MicrovasculatureInsideScientific
 
Closed loop muscle relaxant infusion
Closed loop muscle relaxant infusionClosed loop muscle relaxant infusion
Closed loop muscle relaxant infusionClaudio Melloni
 
2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisi2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisiGUIDO MARIA FILIPPI
 
2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisi2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisiGUIDO MARIA FILIPPI
 
Bionic arm using muscle sensor v3
Bionic arm using muscle sensor v3Bionic arm using muscle sensor v3
Bionic arm using muscle sensor v3IJARIIT
 
Neurotransmitter And Maximal Contractile Response Essay
Neurotransmitter And Maximal Contractile Response EssayNeurotransmitter And Maximal Contractile Response Essay
Neurotransmitter And Maximal Contractile Response EssayRebecca Harris
 

Ähnlich wie Abcd Presentation (20)

Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...
Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...
Model For The Prediction Of Motion Sickness Incidence, Peripheral Hcii Presen...
 
Access Presentation Matsangas
Access Presentation MatsangasAccess Presentation Matsangas
Access Presentation Matsangas
 
Matsangas And Mc Cauley (2005) Model For Predicting Motion Sickness, Adapta...
Matsangas And Mc Cauley (2005)   Model For Predicting Motion Sickness, Adapta...Matsangas And Mc Cauley (2005)   Model For Predicting Motion Sickness, Adapta...
Matsangas And Mc Cauley (2005) Model For Predicting Motion Sickness, Adapta...
 
Nps Hsv Motion Presentation
Nps   Hsv Motion PresentationNps   Hsv Motion Presentation
Nps Hsv Motion Presentation
 
Balance - Amirhashem.pptx
Balance - Amirhashem.pptxBalance - Amirhashem.pptx
Balance - Amirhashem.pptx
 
MAYO CLINIC_BRUNO OTILIO
MAYO CLINIC_BRUNO OTILIO MAYO CLINIC_BRUNO OTILIO
MAYO CLINIC_BRUNO OTILIO
 
NMRS 2010 Mirror Therapy Brief
NMRS 2010 Mirror Therapy BriefNMRS 2010 Mirror Therapy Brief
NMRS 2010 Mirror Therapy Brief
 
Sirevaag_et_al-2016-Psychophysiology
Sirevaag_et_al-2016-PsychophysiologySirevaag_et_al-2016-Psychophysiology
Sirevaag_et_al-2016-Psychophysiology
 
MEASSuRE_product description
MEASSuRE_product descriptionMEASSuRE_product description
MEASSuRE_product description
 
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...
Feasibility of using 3D MR elastography to determine pancreatic stiffness in ...
 
IEEE Medical image Title and Abstract 2016
IEEE Medical image Title and Abstract 2016 IEEE Medical image Title and Abstract 2016
IEEE Medical image Title and Abstract 2016
 
Measuring and Quantifying Sympathetic Control of the Cutaneous Microvasculature
Measuring and Quantifying Sympathetic Control of the Cutaneous MicrovasculatureMeasuring and Quantifying Sympathetic Control of the Cutaneous Microvasculature
Measuring and Quantifying Sympathetic Control of the Cutaneous Microvasculature
 
Closed loop muscle relaxant infusion
Closed loop muscle relaxant infusionClosed loop muscle relaxant infusion
Closed loop muscle relaxant infusion
 
HDMICS Koutsiaris 2010d
HDMICS Koutsiaris 2010dHDMICS Koutsiaris 2010d
HDMICS Koutsiaris 2010d
 
2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisi2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisi
 
2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisi2004 anterior cruciate ligament assisi
2004 anterior cruciate ligament assisi
 
Bionic arm using muscle sensor v3
Bionic arm using muscle sensor v3Bionic arm using muscle sensor v3
Bionic arm using muscle sensor v3
 
Neurotransmitter And Maximal Contractile Response Essay
Neurotransmitter And Maximal Contractile Response EssayNeurotransmitter And Maximal Contractile Response Essay
Neurotransmitter And Maximal Contractile Response Essay
 
Human tecar viss
Human tecar vissHuman tecar viss
Human tecar viss
 
Robotics and simulation in neurosurgery
Robotics and simulation in neurosurgeryRobotics and simulation in neurosurgery
Robotics and simulation in neurosurgery
 

Mehr von pmatsang

Polysomnographic Variables Describing Comorbid Insomnia and Mild Obstructive...
Polysomnographic Variables Describing Comorbid Insomnia and Mild  Obstructive...Polysomnographic Variables Describing Comorbid Insomnia and Mild  Obstructive...
Polysomnographic Variables Describing Comorbid Insomnia and Mild Obstructive...pmatsang
 
Sjwc 2011 Sleep And Motion
Sjwc 2011   Sleep And MotionSjwc 2011   Sleep And Motion
Sjwc 2011 Sleep And Motionpmatsang
 
Personnel physical activity levels on naval vessels (HPAS 2010)
Personnel physical activity levels on naval vessels (HPAS 2010)Personnel physical activity levels on naval vessels (HPAS 2010)
Personnel physical activity levels on naval vessels (HPAS 2010)pmatsang
 
Mast Presentation
Mast PresentationMast Presentation
Mast Presentationpmatsang
 
The High Speed Navy
The High Speed NavyThe High Speed Navy
The High Speed Navypmatsang
 
Ncw Suav Presentation
Ncw Suav PresentationNcw Suav Presentation
Ncw Suav Presentationpmatsang
 

Mehr von pmatsang (6)

Polysomnographic Variables Describing Comorbid Insomnia and Mild Obstructive...
Polysomnographic Variables Describing Comorbid Insomnia and Mild  Obstructive...Polysomnographic Variables Describing Comorbid Insomnia and Mild  Obstructive...
Polysomnographic Variables Describing Comorbid Insomnia and Mild Obstructive...
 
Sjwc 2011 Sleep And Motion
Sjwc 2011   Sleep And MotionSjwc 2011   Sleep And Motion
Sjwc 2011 Sleep And Motion
 
Personnel physical activity levels on naval vessels (HPAS 2010)
Personnel physical activity levels on naval vessels (HPAS 2010)Personnel physical activity levels on naval vessels (HPAS 2010)
Personnel physical activity levels on naval vessels (HPAS 2010)
 
Mast Presentation
Mast PresentationMast Presentation
Mast Presentation
 
The High Speed Navy
The High Speed NavyThe High Speed Navy
The High Speed Navy
 
Ncw Suav Presentation
Ncw Suav PresentationNcw Suav Presentation
Ncw Suav Presentation
 

Abcd Presentation

  • 1. Prediction of Motion Sickness Incidence: Modeling Efforts based on Human Physiology ABCD Meeting 2006 “Human Performance at Sea: Influence of Ship Motions on Biomechanics and Fatigue" By Lieutenant P. Matsagas, M.Sc., Hellenic Navy [email_address] , [email_address] M.E. McCauley, Ph.D., Naval Postgraduate School [email_address]
  • 2.
  • 3.
  • 4.
  • 5. HFR model (1974) Model Characteristics Vertical Acceleration Only true motion MSI: % of people who vomit Two-hour nauseogenic period Nauseogenic frequency range 0.05 – 0.7 [Hz] Central nauseogenic frequency 0.167 [Hz]
  • 6.
  • 7.
  • 8.
  • 9. Error Estimation Subsystem Oman (1982) Glasauer & Merfeld (1997) Bles et al. (1998) Merfeld et al. (1993)
  • 10. Visual System Sandini et al. (2001) Legend Independent variables Intermediate variables Dependent variables Neural Store
  • 11. Current Model (2006) Adaptation Mechanism in detail Predicted difference between sensory input and motion in the neural store Sensory contents in Neural Store 1 2 3 Legend Independent variables Intermediate variables Dependent variables Neural Store
  • 12. Normalization & Linear Combination of 2 Sources of Error = MSI
  • 14. Predicted MSI Proposed Model Characteristics Vertical Acceleration MSI: % of people who vomit Two-hour nauseogenic period Nauseogenic frequency range 0.05 – 0.6 [Hz] Central nauseogenic frequency 0.17 [Hz]
  • 15. Model Validation True Motion Settings Proposed model HFR model MSI Comparison between Proposed and HFR models 1 2 3
  • 16. MSI Accumulation Characteristics Vertical Acceleration MSI: % of people who vomit Two-hour nauseogenic period Nauseogenic frequency range 0.05 – 0.6 [Hz] Central nauseogenic frequency 0.17 [Hz]
  • 18. MSI Habituation and Retention
  • 19.
  • 20.
  • 21.
  • 22.

Hinweis der Redaktion

  1. Motion sickness is a general term that describes a number of symptoms related to discomfort and associated emesis (vomiting) induced by numerous kinds of motions. Symptoms of motion sickness may be: Discomfort Headache Pallor Unwillingness to continue working Vomiting. A problem with nausea is that there exists a large inter-subject variability in the degree to which a person feels sick when sensing a provocative motion. Some people will be severely sick, others will feel sick but not to the extent of stopping their task. Some vomit once and then they feel OK, others continue vomiting until the provocative motion ceases. Finally, almost 5% never feel motion sick. Unfortunately, motion sickness effects are evident in numerous provocative motion environments, such as ships, aircraft, automobiles, and air-cushioned vehicles. The term “Motion Sickness” is a misnomer: “Sickness” implies that it is a type of disease, when in fact it is a perfectly normal response of a healthy individual without any functional disorders ; and it can be induced in simulators and Virtual Environments where there is no actual motion . Motion sickness occurs in environments with either actual or implied motion such as seasickness, airsickness, space sickness, and simulator sickness.
  2. The most widely accepted theory is the conflict mismatch theory, sensory rearrangement theory [2] or neural mismatch theory. According to these theories, the cause of motion sickness is that the vestibular system provides information about self motion that does not match the sensations of motion generated by visual or kinaesthetic (proprioceptive) systems, or what is expected from previous experience [3]. So, in more general terms : Motion sickness is the outcome of the comparison between what we sense and the neural store (sensory input memory) The neural mismatch hypothesis is comprised of two basic components [4]: A neural storage unit that retains the informational characteristics of the previous sensory input, A comparator unit that matches the contents of the store with the informational characteristics of the prevailing sensory influx from the motion and position senses. It is obvious that the aforementioned comparison is a continuous and dynamic physiological process. Therefore, we al Vestibular system: Collective term for the three semicircular canals and the two vestibular sacs (utricle and saccule) within the labyrinth of the inner ear. The vestibular system is involved in the perception of spatial orientation. Proprioception: The perception of information about the position, orientation and movement of the body and its parts. (Involves the somatosensory system ) .
  3. A convenient index of motion sickness severity is the Motion Sickness Incidence (MSI), which is the percentage of people who vomit when exposed to nauseogenic environment. Positive aspects of MSI metric Easily and objectively identifiable. Measures of the number and severity of symptoms during the progression of the syndrome are varialble and idiosyncratic, whereas emesis is an observable, behavioural marker. Negative aspects Does not take into account the numerous symptoms of motion sickness It is not related, in a straight-forward manner, to human performance
  4. The model used for the validation of the proposed work will be the HFR model. In a number of Human Factors Research (HFR) experiments (O’Hanlon & McCauley, 1974; McCauley, Royal, Wylie, O’Hanlon & Mackie, 1976) a regression model was proposed for MSI estimation. The alternative would be to use the observation data from large passenger ferries reported by Griffin (1990). We chose the first data because the latter are derived from environmental conditions and corresponding ships’ motions not precisely defined. The HFR model, although useful, has two drawbacks: Is not etiologic, and It refers only to vertical oscillation (only real motion, not vection) During the 2-hour nauseogenic period, the subject is seated inside a closed, lighted compartment (sea motion simulator), without being able to receive visual or auditory information, from the outside environment. Furthermore, the subject is assumed to be passive and stationary while exposed to externally induced motion.
  5. The proposed model is conceptually based on the ideas from existing theories and research The whole concept is based on Reason’s (1978) neural mismatch theory, The subjective discomfort model by Oman (1982), Merfeld et al. (1993) model, TNO motion sickness model by Bos et al. (2002), The system is structured in combination with observer theory concepts from control systems. The proposed MSI is based on two major sources of error, a) The estimation of gravity, and b) the residual optical flow The model is linear and time invariant. This gave us the ability to work with transfer functions and derive analytical solutions at the end. Some non-linear aspects were modeled through corresponding non-linear functions. The model version presented today deals only with simple sinusoidal motion in the Z axis (vertical).
  6. Model input parameters True motion characteristics (Vertical acceleration frequency and amplitude), which are the input to the vestibular system Apparent motion characteristics (Vertical acceleration frequency and amplitude) which are input to the visual system
  7. The model inputs are a) the external motion sensed by the vestibular system. Obviously we assume whole body motion, and b) motion of the visual surroundings sensed by the subject. ROF is the residual optical flow, which refers to the retinal slip due to less-than-perfect compensation of external motion Wr is the angular velocity of the visual surroundings (in space referenced frame)
  8. The visual system model used is the one proposed by Panerai, Sandini and their colleagues. Their model extends to translational VOR and is derived from Robinson’s model (1977) which deals with rotational VOR. The VOR signal is fed to the visual system by the VOR-interface derived from Merfeld, Young, Oman and their colleagues (1993). The optokinetic reflex is the mechanism that generates compensatory eye movements, and thus stabilized gaze, through visual input alone. The generated eye movements are called optokinetic nystagmus (OKN). In general, the optokinetic reflexes operate in closed-loop, are slower than vestibular reflexes, and have a better response at lower frequencies (Baarsma & Collewijn, 1974; Michael & Jones, 1966). The OKN has the opposite performance characteristics compared to VOR. Its latency is much longer due to the time needed to process visual input. At frequencies lower than 0.1 Hz, OKN is more accurate than VOR. At frequencies between 0.1 and 1 Hz its gain is decreased and a phase lag is developed (Peterka, Black, & Schoenhoff, 1987).
  9. The adaptation mechanism is derived from Reason’s neural mismatch model (Reason, 1978) simplified to include only passive motions (involuntary; externally-driven). It includes the two basic components of the neural mismatch hypothesis, the neural store and the comparator unit. The inputs are the perceived motions from the vestibular sensors, the peripheral visual system, and the somatosensory system. The neural memory concept is the basis for the change of the existing neural store information to accommodate the new sensory input. We assume that earlier motion characteristics exist in the Neural Store as memory traces with decaying strength. The more distant (from present) the traces, the less significant they are. The process of saving new motion characteristics in the Neural Store is dynamic and continuous in time because we always analyze and store sensory information.
  10. The combined error, which is used to derive the estimation of the MSI, is the linear combination of the absolute values of the “gravity estimation” normalized error and the “residual optical flow” normalized error.
  11. Evidence of habituation in a nauseogenic environment is considered to be a decline in MSI (the probability of emesis) (McCauley et al., 1976). The shown plot depicts the close correlation between the habituation process found in the HFR experimental data and the MSI predicted by the proposed model. The habituation procedure is a set of five daily two-hour exposures to the same motion stimuli. Proposed model output linearly adjusted for comparison purposes. HFR data parameters: ARMS=0.22 [g], f=0.25 [Hz] It is obvious that the two curves look identical, which is a promising result for more “tuning” of the model to experimental data.
  12. The retention process refers to the subject’s re-adaptation to the “normal” environment motion characteristics. In the presented slide you can see the predicted MSI when five daily two-hour exposures to the same motion are given to the subjects. The “retention” two-hour exposure is given to the subjects one week after the final day of the habituation exposures.
  13. The modeled independent parameters of MSI prediction are implemented parametrically, thus the model can be easily extended to include multiple nausiogenic combinations of environmental conditions The model’s predicted MSI as presented in this work, is derived without adjustment to the experimental data. We chose acceptable values for the parameters leading to a simplified and stable model (e.g. +1 or -1). Thus, the predicted precision may be easily increased. The timeline of MSI and the adaptation process is adequately approximated and the corresponding results are following the ones given by the HFR experiments. The error region (+ or – 5%) is small compared to other models on this topic. It is etiologic. Takes into account main human physiology subsystems which are known to contribute to motion sickness incidence. Therefore, it The HFR model is a regression approximation It’s linear and time invariant, thus it is easily analyzed. Of course, human physiology processes are non-linear, but in this work the linearity assumption has led to acceptable precision.
  14. The modeled independent parameters of MSI prediction are implemented parametrically, thus the model can be easily extended to include multiple nauseogenic combinations of environmental conditions The timeline of MSI and the adaptation process are adequately approximated and the corresponding results are following the ones given by the HFR experiments. The error region (+ or – 5%) is small compared to other models on this topic. The proposed Model is etiologic. Takes into account main human physiology subsystems which are known to contribute to motion sickness incidence.
  15. Include motion in 6 degrees of freedom Implementation of all physiological systems contributing to motion sickness development For example, proprioception CNS non-linear characteristics time delays Non-linear detection of motion amplitude implemented at the sensor Ecological validity is a form of validity in an experiment. In order for an experiment to possess ecological validity, the methods, materials and setting of the experiment must approximate the real-life situation that is under study E xternal validity is the ability of a study's results to generalize.