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               Although  falling  is a result  of complex  and  multi-factorial  problem,  lack  of postural  control  is one
               of  the  major  contributing  factors.  Aging  effects  on  the  sensory  feedback  have been  hypothesized
               to  be  a  key  factor  in  adjusting  posture  to  maintain  their  balance  against  unpredictable  external
               or  internal  variations  of  gait.  In  addition,  recent  randomized  controlled  trials  that  have  tested
               the  effectiveness  of  the  intervention  for  elderly  have  indicated  that  exercise  training  significantly
               increase  their  aerobic  capacity  and  muscle  strength,  which  might  result  in  improvement  of  the
               postural  stability.

               Conventionally,  clinicians  have  been  assessed  personnel  walking  ability  based  on  performance  of
               static  balance tests  and  measure  of simple gait  factors  (walking speed,  cadence, step  length,  etc.),
               mostly  focusing  on  quantifying  regional  amount  of  body  sway,  variability  of  gait  factors  or  joint
               angles.  Those  methods  provide  a  practical  evaluation,  however,  the  measure  of  static  balance
               or  gait  variability  itself  does  not  mean  that  of  dynamic  stability  of  walking.  Dynamic  stability
               represents  a  resilient  ability  to  maintain  certain  continuous  cyclic  movement  by  accommodating
               internal or external perturbations  (Hurmuzlu  et al. 1994).  On the other hand variety  of instruments
               have  been  used  to  quantify  walking  characteristics  in  a  more  precise  manner,  by  means  of  the
               video-based  motion  capture  system,  goniometry,  or  force plates.  However,  those methods  requires
               considerable  setups,  then  limited  to  laboratorial  environments.  Recently,  mechatronics  progress
               made  it  possible  to  realize  small  and  low  power  consumptive  accelerometry  as  a  testing  tool
               applicable  in  the  field  of  medical  therapy(Aminian  et  al.  2002,  Ohtaki  et  al.  2001, 2005).  Some
               advanced  algorithms  have been  also proposed to evaluate  gait  performances  and  dynamic  walking
               stability  basing  on  a  simple  accelerometry(Dingwell  et  al.  2000,  2001,  Buzzi  et  al.  2003.  Arif
               et  al.  2004).  Further  application  of  those  method  to  a  physical  assessment  is  strongly  required
               to  enhance  efficiency  and  effectiveness  of  interventions.  Nevertheless,  it  is  still  insufficient  to
               investigate  subsequent  improvements  on  walking  abilities  in  terms  of  the  stability  of  dynamical
               system.

               This  study  was  intended  to  present  a  practical  method  to  assess  walking  stabilit}'  by  using  a
               portable  instrument,  then  to  investigate  its  usefulness  in the  physical  assessment  for  elderly  peo-
               ple.  The  method  employed  a  measurement  of  three-dimensional  acceleration  of  the  body,  and
               an  application  of  nonlinear  time-series  analysis  which  directly  assess  stability  of  the  dynamical
               system.  Straight  level-walking  of young  and  elderly  subjects  were investigated  in the  experiment.
               Moreover, its feasibility  in assessing  effects  and  efficacies  of the  five-month  interventions  including
               aerobic  exercise training  was  investigated.


               METHODOLOGY

               In this study,  we focused  on local dynamic stability  which  is defined  as  a. sensitivity  of the  dynam-
               ical  system  to  small  perturbations  in  gait  variability  which  produced  by  one's  locomotor  system
               itself.  Lyapunov  exponent  estimation  was applied  to  evaluate the  local dynamic stability  of walk-
               ing.  Firstly,  state  space  was reconstructed  from  the  obtained  acceleration  data  after  determining
               appropriate  time  delay  and  embedding  dimension:

                                    y(t)  =  (x(t),x(t  + r),---,x(t  + {d-  l)r)).    (1)
               Where,  y(t)  is the  d dimensional  state  vector,  x{t)  is the  original  acceleration  data,  r  is the  time
               delay, and d is the embedding dimension.  A Schematic representation  of the reconstruction  process
               was  shown  in  Figure  1.  A  valid  state  space  must  include  a  sufficient  number  of  coordinates  to
               unequivocally  define  the  state  of  the  attractor  trajectories.  Time  delay  r  was  determined  as  a
               time  when  autocorrelation  coefficient  of the  data  gets  lower  than  the  reciprocal  value  of  natural
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