1 Copyright © 2013 Tata Consultancy Services Limited

Tele-rehabilitation

Kingshuk ChakravartyBrojeshwar BhowmickAniruddha SinhaDr. Arpan Pal3-July-2013

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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 rehabilitationtreatment after discharge1

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[1] Statistics published and presented at conference RehabWeek 2015 by NeuroAtHome.http://www.neuroathome.net/p/home.html

1 billion

Active aging

Brain Injurie

s

Musculo-

SkeletalInjuries

Neuro-degenerati

veConditions

Spinal Cord

Injuries

Chronic Health

Conditions

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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

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Care Model

Acute Care Hospital Rehabilitation Hospital

Outpatientclinic

Most patients go straight home after few days

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TCS Envisaged Solution: Rehabilitation Platform

Rehab Platfor

m

For Use in Home Settings

Physical and

Cognitive Exercises

For Use in Clinical Settings

With Detailed Clinical Monitori

ng

Low cost, affordable for home use

Ease of Access

Fun @ Exercise

Improved Outcome

Affordableand

Reliable

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Tele-Rehabilitation Architecture

Cloud

Store Raw Data

Patient’s Exercise

Parameter

Patient History

Extract Paramete

rs

Doctor’s Portal

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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.

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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.

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Multiple Input Devices

TCS Rehab Software

Physic

al

Exercis

es

Cognitive

Exercises

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How would our solution work in home settings?

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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

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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

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How doctor can build therapy session?

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Feature Selection using different algorithmsAlgorithms

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-ShoulderRight-ElbowRight,

• ElbowRight-ShoulderRight-HipRight.

• X-axis-FootRight-AnkleRight

• Y-axis-KneeRight-AnkleRight

Methods of Analyzing Abnormal Gait Pattern:Feature Selection

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Analysis of selected feature subset for natural and unnatural gait pattern

Methods of Analyzing Abnormal Gait Pattern:Analysis of Selected Feature Subset

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Left Heel: Line of Progression Right Heel: Line of Progression

Methods of Analyzing Abnormal Gait Pattern:Extracting Parameter Line of Progression

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Prevention Program

Mild Cognitive ImpairmentsAbnormalities in Daily ActivitiesFatigue and Muscular WeaknessFall Prediction and Detection

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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 costsAffordable

• 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

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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

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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

Thank You