Telerehab for stroke patients

1. 1Copyright © 2013 Tata Consultancy Services
Limited
Tele-rehabilitation
Kingshuk Chakravarty
Brojeshwar Bhowmick
Aniruddha Sinha
Dr. Arpan Pal3-July-2013

2. 2
Neurological Conditions by the Numbers
annual cost in EURO
in European economy:
twice the cost of
cancer1
798 billion
people worldwide
need rehabilitation
services1
do no receive
rehabilitation
treatment after
discharge1
2/3
[1] Statistics published and presented at conference RehabWeek 2015 by NeuroAtHome.
http://www.neuroathome.net/p/home.html
1 billion
Active
aging
Brain
Injuries
Musculo-
Skeletal
Injuries
Neuro-
degenerative
Conditions
Spinal
Cord
Injuries
Chronic
Health
Conditions

3. 3
Other Challenges
•Very costly devices and high maintenance
•Difficult for patients to frequently visit hospitals
Existing Quantitative Gait Analysis systems
(Goniometers, markers, VICON system) costs
approx. $200K & not readily available in the
market. Expensive maintenance costs

4. 4
Care Model
Acute Care Hospital
Rehabilitation
Hospital
Outpatient
clinic
Most patients go straight home after few days

5. 5
TCS Envisaged Solution: Rehabilitation Platform
Rehab
Platform
For Use in
Home
Settings
Physical
and
Cognitive
Exercises
For Use in
Clinical
Settings
With
Detailed
Clinical
Monitoring
Low cost, affordable for home use
Ease of
Access
Fun @
Exercise
Improved
Outcome
Affordable
and
Reliable

6. 6
Tele-Rehabilitation Architecture
Cloud
Store Raw
Data
Patient’s
Exercise
Parameter
Patient
History
Extract
Parameters
Doctor’s Portal

7. 7
Gamification to Increase Motivation
VR based games for Physical Therapy2
[2] Burdea, Grigore, et al. "Virtual reality-based orthopedic telerehabilitation."Rehabilitation Engineering, IEEE Transactions on 8.3 (2000): 430-432.

8. 8
How a session is designed?
 Physical therapy mainly related to
– 1. Static balance 2. Dynamic balance 3. Hip range of motion 4. Co-
ordination 5. Trunk control 6. Lateral displacement 7. Gait ability
 Cognitive therapy mainly based on
– 1. Attention 2. Inhibition 3. Working Memory 4. Perception
5. Categorization 6. Sequencing 7. Calculation 8. Expression.
 Session Features
– Session can be completed independently or with therapist assistance
– Session results summarized by exercise
– Exercise results summarized by session date
– Session i.e. game difficulty level can be adjusted based on performance.
– Doctor can provide online or offline feedback
– Augmented audio-video feedback will help patients to perform exercise.

9. 9
Multiple Input Devices
TCS Rehab Software

10. 10
How would our solution work in home settings?

11. 11
How would our solution work in clinical settings?
Health Care
Professional
Cloud
Patient 1
Patient 3
Patient 2
Daily therapy for
patients - comfort
of their own room
Daily monitoring of
every patients -
mobile or tablet or
laptop
Make discussion
with other doctors
on patients or
therapy

12. 12
How our solution is adaptable to end-users?
Make discussion on particular
therapy
Personalized
exercises - patient’s
need and capabilities
Automatic therapy
adaptation - based on
patient performance
Therapy design in terms of
exercises for different disorders
or diseases
Patient can log
their feedback
View potential
conflict among
therapies and
patient’s
impairments Doctor Patient

13. 13
How doctor can build therapy session?

14. 14
Feature Selection using different algorithms
Algorithms MINE mRMR FEAST HSICLasso
Feature
Subset
• Y-axis-
KneeRight-
AnkleRight,
• X-axis-
FootRight-
AnkleRight,
• HipRight-
KneeRight-
AnkleRight,
• ElbowLeft-
ShoulderLeft-
HipLeft,
• X-axis-
ElbowRight-
WristRight
• Y-axis-
KneeRight-
AnkleRight,
• X-axis-
ElbowRight-
WristRight,
• X-axis-
FootRight-
AnkleRight,
• HipRight-
KneeRight-
AnkleRight,
• ElbowLeft-
ShoulderLeft
-HipLeft
• X-axis-
FootRight-
AnkleRight,
• Y-axis-
KneeRight-
AnkleRight,
• Y-axis-
KneeRight-
HipRight
• Y-axis-
ShoulderRigh
t-ElbowRight,
• ElbowRight-
ShoulderRigh
t-HipRight.
• X-axis-
FootRight-
AnkleRight
• Y-axis-
KneeRight-
AnkleRight
Methods of Analyzing Abnormal Gait Pattern:
Feature Selection

15. 15
Analysis of selected feature subset for natural and unnatural gait pattern
Methods of Analyzing Abnormal Gait Pattern:
Analysis of Selected Feature Subset

16. 16
Left Heel: Line of Progression Right Heel: Line of Progression
Methods of Analyzing Abnormal Gait Pattern:
Extracting Parameter Line of Progression

17. 17
Prevention Program
Mild
Cognitive
Impairments
Abnormalities
in Daily
Activities
Fatigue and
Muscular
Weakness
Fall
Prediction
and
Detection

18. 18
Conclusion
• Consists of all features of iOT namely,
• Capturing data from sensors fitted to the patient
• Remote data capture & remote control of programs
• Analytics - Collect patient data, analyze & send the cleaned up report to
the rehab-doctors
• A dashboard for the doctor to control and plan each patient´s exercises
• Mobility – capture Evaluation and graphical analysis of patient’s progress
on mobile
Complete end-to-end
solution
• Solution uses easily available IMU, EMG sensors and Kinect
• Existing Quantitative Gait Analysis systems (Goniometers, markers,
VICON system) costs approx. $200K & not readily available in the market.
Expensive maintenance costs
Affordable
• Can easily be used in Hospital or at homePortable
• Proposed solution does not require much set up time & is easy to use
• Existing systems need skilled technicians to place markers on patients
and to administer these tests. Thus they require require calibration before
every use
Ease of use
• Patients can simply walk into the setup and start taking the test in no time
• Due to minimal set up time more number of patients can undergo
rehabilitation
Efficient

19. 19
Achievement and Future Roadmap
Future roadmap
 Fusion of vision and body sensor networks to improve post-stroke monitoring.
 Post stroke fatigue detection using EMG and other sensors.
 Post-stroke balance rehabilitation and fall prediction.
 Tremor modeling for different patients.
Recent Achievements
 Filed patent on this “A DEVICE AND METHOD FOR FACILITATING HEALTH MONITORING
OF A PATIENT”.
 One paper “A comprehensive toolbox for online gait analysis and rehabilitation” got accepter
in INEREM 2015

20. 20
MBBS , MD - General Medicine , DM – Neurology,
Director of Jain Misrilal Padmawati Foundation Medical
Rehabilitation Centre, Institute of Neurosciences, Kolkata
(IN-K), Director of Neuro-rehabilitation Program and a
Consultant Neurologist practicing in IN-K.
Dr. Abhijit Das is a neurologist and a serial inventor. He completed his training in Neurology at SCTIMST,
Trivandrum in the year 2009. He joined the postdoctoral fellowship under the Advanced Rehabilitation
Research Training (ARRT) program funded by the National Institute on Disability and Rehabilitation
Research (NIDRR) at the Kessler Foundation Research Center, West Orange, NJ in 2010. On his way to
fellowship, he collected numerous awards like American Academy of Neurology (AAN) Resident
Research Award in 2009, Best Abstract Award by the Association of Indian Neurologists in America
(AINA). In addition to these his work also got selected for the NIDRR Young Investigators Presentation
at the 2012 American Congress of Rehabilitation Medicine - American Society of Neurorehabilitation
(ACRM-ASNR) annual conference.
External Collaboration

21. Thank You