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Alberto Ferrari - EU Falls Festival Footwear and Sensing... · Alberto Ferrari Department of...
Transcript of Alberto Ferrari - EU Falls Festival Footwear and Sensing... · Alberto Ferrari Department of...
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Alberto Ferrari Department of Electrical, Electronic, and Information Engineering, University of Bologna, Italy
Cupid logo, image, affiliation
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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
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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
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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)
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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
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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
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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!
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Challenge keep
upright!
“Virtual clinician” con6nuously assessing and vocally correc6ng pa6ents’ ineffec6ve or unsafe gait paIerns
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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
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Solution: CuPiD app Hardware: • iner<al sensors transmifng Bluetooth 3D accelera6ons and 3D
angular veloci6es (@100Hz) to smartphone
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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
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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
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User Interfaces Pa<ent mode: 1 single buJon
Operator mode
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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
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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)
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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
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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%
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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
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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
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Telemedicine
Background service • remote configura0on of sefngs • automa0c data synchroniza0on
(via WiFi)
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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
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CuPiD app
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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
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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
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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"
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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
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Thank you! !
http://www.cupid-project.eu/