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1Copyright © 2013 Tata Consultancy Services
Limited
Tele-rehabilitation
Kingshuk Chakravarty
Brojeshwar Bhowmick
Aniruddha...
2
Neurological Conditions by the Numbers
annual cost in EURO
in European economy:
twice the cost of
cancer1
798 billion
pe...
3
Other Challenges
•Very costly devices and high maintenance
•Difficult for patients to frequently visit hospitals
Existin...
4
Care Model
Acute Care Hospital
Rehabilitation
Hospital
Outpatient
clinic
Most patients go straight home after few days
5
TCS Envisaged Solution: Rehabilitation Platform
Rehab
Platform
For Use in
Home
Settings
Physical
and
Cognitive
Exercises...
6
Tele-Rehabilitation Architecture
Cloud
Store Raw
Data
Patient’s
Exercise
Parameter
Patient
History
Extract
Parameters
Do...
7
Gamification to Increase Motivation
VR based games for Physical Therapy2
[2] Burdea, Grigore, et al. "Virtual reality-ba...
8
How a session is designed?
 Physical therapy mainly related to
– 1. Static balance 2. Dynamic balance 3. Hip range of m...
9
Multiple Input Devices
TCS Rehab Software
10
How would our solution work in home settings?
11
How would our solution work in clinical settings?
Health Care
Professional
Cloud
Patient 1
Patient 3
Patient 2
Daily th...
12
How our solution is adaptable to end-users?
Make discussion on particular
therapy
Personalized
exercises - patient’s
ne...
13
How doctor can build therapy session?
14
Feature Selection using different algorithms
Algorithms MINE mRMR FEAST HSICLasso
Feature
Subset
• Y-axis-
KneeRight-
A...
15
Analysis of selected feature subset for natural and unnatural gait pattern
Methods of Analyzing Abnormal Gait Pattern:
...
16
Left Heel: Line of Progression Right Heel: Line of Progression
Methods of Analyzing Abnormal Gait Pattern:
Extracting P...
17
Prevention Program
Mild
Cognitive
Impairments
Abnormalities
in Daily
Activities
Fatigue and
Muscular
Weakness
Fall
Pred...
18
Conclusion
• Consists of all features of iOT namely,
• Capturing data from sensors fitted to the patient
• Remote data ...
19
Achievement and Future Roadmap
Future roadmap
 Fusion of vision and body sensor networks to improve post-stroke monito...
20
MBBS , MD - General Medicine , DM – Neurology,
Director of Jain Misrilal Padmawati Foundation Medical
Rehabilitation Ce...
Thank You
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Tcs tele rehab-hod-0.4

Telerehab for stroke patients

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Tcs tele rehab-hod-0.4

  1. 1. 1Copyright © 2013 Tata Consultancy Services Limited Tele-rehabilitation Kingshuk Chakravarty Brojeshwar Bhowmick Aniruddha Sinha Dr. Arpan Pal3-July-2013
  2. 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. 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. 4 Care Model Acute Care Hospital Rehabilitation Hospital Outpatient clinic Most patients go straight home after few days
  5. 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. 6 Tele-Rehabilitation Architecture Cloud Store Raw Data Patient’s Exercise Parameter Patient History Extract Parameters Doctor’s Portal
  7. 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. 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. 9 Multiple Input Devices TCS Rehab Software
  10. 10. 10 How would our solution work in home settings?
  11. 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. 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. 13 How doctor can build therapy session?
  14. 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. 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. 16 Left Heel: Line of Progression Right Heel: Line of Progression Methods of Analyzing Abnormal Gait Pattern: Extracting Parameter Line of Progression
  17. 17. 17 Prevention Program Mild Cognitive Impairments Abnormalities in Daily Activities Fatigue and Muscular Weakness Fall Prediction and Detection
  18. 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. 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. 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. 21. Thank You

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