Alberto Ferrari Department of Electrical, Electronic, and Information Engineering, University of Bologna, Italy

Cupid logo, image, affiliation

Cupid at a glance

CuPiD:  Closed-­‐loop  system  for  personalized  and  at-­‐home  rehabilita6on  of  people  with  Parkinson's  Disease  (PD)  Aim:  develop  and  validate  a  combina0on  of  services  for  at  home  rehabilita0on  and  training  of  major  motor  impairments  caused  by  PD,  u0lizing  BSNs  and  motor  learning    paradigms      3.5  years,  EU  FP7  ICT  project  (2011-­‐2015)  8  members:  UNIBO,  ETH,  OCC,                                          EXEL,  KUL,  TASMC,  IBIT,  ST    

Parkinson’s Disease

Parkinson’s Disease (PD)  is  the  second  most  common  neurodegenera0ve  disease  in  the  general  popula0on:  •  worldwide  affects  ~3%  of  popula0on  >  65  years  of  age  •  costs  (EU):  direct  +  indirect  €20  billion  

PD symptoms

PD  provokes  severe  limita0ons  in  motor  and  cogni0ve  func0ons:  •  cogni<ve  deteriora<on:  profound  difficulty/slowness  in  motor  

planning  and  ini0a0on,  and  on  aden0on-­‐demanding  tasks  •  major  motor  impairments:  balance,  postural  transfers  and  walking  

(stoop  trunk  posture,  shuffling  steps,  freezing  of  gait)  

PD symptoms

drive gait pattern

continuous assessment

of gait pattern enlarge steps

augment clearance

control rhythm upright trunk

posture

attempt to correct

talking

thinking

…

vicious circle

PD treatments Current  therapies:  •  dopaminergic  L-­‐Dopa    •  surgery  DBS              are  inadequate  to  preserve  mobility  as                the  disease  progresses    

•  Pa0ents  undergo  rehabilita<on  sessions  to  teach  pa0ents  on:  –  how  to  react  to  motor  altera0ons  –  keep  correct  posture  –  produce  effec0ve  and  safe  gait  padern  

 

PD treatments Current  therapies:  •  dopaminergic  L-­‐Dopa    •  surgery  DBS              are  inadequate  to  preserve  mobility  as                the  disease  progresses    

•  Pa0ents  undergo  rehabilita<on  sessions  to  teach  pa0ents  on:  –  how  to  react  to  motor  altera0ons  –  keep  correct  posture  –  produce  effec0ve  and  safe  gait  padern  

 

augment step length!

keep upright!

Challenge keep

upright!

“Virtual  clinician”  con6nuously  assessing  and  vocally  correc6ng  pa6ents’  ineffec6ve  or  unsafe  gait  paIerns    

talking

thinking

…

drive gait pattern

continuous assessment

of gait pattern enlarge steps

augment clearance

increase rhythm

upright trunk posture

attempt to correct

vicious circle

Challenge

Solution: CuPiD app Hardware:  •  iner<al  sensors  transmifng  Bluetooth  3D  accelera6ons  and  3D  

angular  veloci6es  (@100Hz)  to  smartphone  

Sensors on the feet

Advantages 1.  easy donning/doffing

2.  process signals (accelerations and velocities) with higher

amplitude à increase accuracy in detection of gait events

3.  apply hypothesis of Zero Velocity during the 2nd rocker à obtaining the position from the double integration of the acceleration during shorter time-windows

Solution: CuPiD app App  •  able  to  perform  in  real-­‐0me  an  accurate  gait  analysis  and  to  act  as  

an  intelligent  tutoring  system  feeding  back  the  vocal  instruc0ons  usually  provided  by  physiotherapists  

•  to  be  used  at  home  independently  by  pa0ents  

User Interfaces Pa<ent  mode:  1  single  buJon  

Operator  mode  

Software description

Start Sensor connetion

Gait calirated

Perform calibration

No 1.  Calibra<on  Recording  the  best  performance  under  clinical  supervision  

Please, walk at your best

Software description

Start Sensor connection

Gait calirated

Perform calibration

Patient walking

Yes

No

StepR +1

StepL +1

1. Calibra0on  2.    Daily  usage  at  home  (preferably  outdoor)  

Software description

Start Sensor connection

Gait calirated

Perform calibration

Patient walking

Yes

No

StepR +1

StepL +1

1. Calibra0on  2.    Step  detec<on  3.    Gait  Parameters  

Initial contact

Foot off

Angular velocity Temporal gait params

Software description

Start Sensor connection

Gait calirated

Perform calibration

Patient walking

Yes

No

StepR +1

StepL +1

1. Calibra0on  2.    Step  detec<on  3.    Gait  Parameters  

Initial contact

Foot off

Angular velocity Inertial sensor

Accelerometer

Gyroscope

↷ dt∫dt∫

dt∫

position velocity orientation

⊕ -g

Mechanization eq.

ZUPT K

Kalman filter correction

Kalman Filter

Temporal gait params Spatial gait params

Step  length  errors  <  4%  

Software description

Start Sensor connection

Gait calirated

Perform calibration

Patient walking

Yes

No

StepR +1

StepL +1

1. Calibra0on  2. Step  detec0on  3. Gait  parameters  4.  Audio-­‐feedback  Online  gait  params  compared  with  reference:    •  Verbal  instruc<ons  

when  pa0ent  walks  out  of  his/her  best  performance  

•  reinforce  when  pa0ent  walks  close  to  best  performance    

Gait parameter calculation

ABF Restitution

Software description

Start Sensor connection

Gait calirated

Perform calibration

Patient walking

Yes

No

StepR +1

StepL +1

1. Calibra0on  2. Step  detec0on  3. Gait  parameters  4. Audio-­‐feedback  5. Automa0c  increase/decrease  difficulty  once  the  person  is  able  to/not  to  remain  constantly  in  the  target  zone  

6. Automa0c  adjustment  of  messages  verbosity  

Gait parameter calculation

ABF Restitution

Good performance

Increase difficulty

Yes No Decrease difficulty

Telemedicine

Background  service    •  remote  configura0on  of  sefngs  •  automa0c  data  synchroniza0on  

(via  WiFi)  

Performance outline

200 400 600 800 1000 1200 1400 1600

−5

0

5

Asymmetry

Diff

eren

ce [%

]

Param trendUp tolLo tolRewardTutor abf

200 400 600 800 1000 1200 1400 1600−20

−10

0

10

20 Step Length

Diff

eren

ce [%

]

Steps

CuPiD app

Validation study

Follow up

Post-test

Intervention

Pre-test

Recruitment

Therapeu<c  advice:  3x/week  30  min  of  

walking    

Therapeu<c  advice  +  CuPiD:  

3x/week  30  min  of  walking  with  CuPiD    

40  PD  pa<ents  

6  weeks  

4  weeks  

20 patients 66 (±8) years - H&Y: 2.2(±0.4)

 

20 patients 67 (±8) years - H&Y: 2.4(±0.4)

Post test Post test

Follow up Follow up

Validation study – Results

Average values on CuPiD group  Number of trials 20 Distance travelled [km] 1.8 Training duration [min] 24 Total number of strides 2844 Cadence [strides/min] 58 Stride length [cm] 128 Gait speed [cm/s] 124 Number of tutor messages 28 Number of praise messages 68 Total number of messages 96

Pre-test Week1 Week2 Week3 Week4 Week5 Week6 Post test 4motnh follow-up

Validation study – Results

Pre-test Week1 Week2 Week3 Week4 Week5 Week6 Post test 4motnh follow-up

pre post retention

60

80

100

120

140

160

180

[cm

/s]

Gait speed

1615

16

26

33

23

Gait Speed (GaitRite)

0 0.5 1 1.5 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Patient 1Patient 2Patient 3Patient 4Patient 5Patient 6

pre post retention

60

80

100

120

140

160

180

[cm

/s]

Gait speed

1615

16

26

33

23

Distribution (in box-plot) of patient trials performances"

Validation study – Conclusions

Pre-test Week1 Week2 Week3 Week4 Week5 Week6 Post test 4motnh follow-up

Main  outcomes  •  CuPiD  Group  vs  Control:  

CuPiD  Group  was  as  effec0ve  as  therapist  advice  alone    •  Great  apprecia0on  of  objec0ve  feedback  during  unsupervised  

performance  =  original  aim  of  CuPiD  •  2  pa0ents  asked  to  hold  the  system  with  them  

 Cri<cal  aspects  •  Touch  screen  was  some0mes  difficult  to  handle  •  Clinicians  asked  to  improve  pa0ents  report  towards  concept  of  

serious  gaming  

Thank you! !

http://www.cupid-project.eu/