a wearable platform for the monitoring of health condition and sport performance and the realtime...

8/2/2019 A Wearable Platform for the Monitoring of Health Condition and Sport Performance and the RealTime Prevention of

1/17

Dimitrios I . FotiadisUniversity of Ioannina,

Dept. of Comput er Science

Unit of Medical Technology and

Int el l igent Information Systems

A Wearable Platform for theMonitoring of Health Condition andSport Performance and the Real-Time Prevention of Sport Injuries

Contents

Objectives

Architecture

DesignSystem Evaluation


2/17

Objectives

DROMEAS aims at developing a w earable platfor m forthe monitoring of the athletes health condition, during

training.

The system will recognize possible health problems andcommunicate them to experts in order to prevent possible

sport injuries.

Our aim is to develop a real-time monitoring system thatwill enable athletes to rapidly regain their normal

functionality by avoiding injury relapses.

Technical Obj ect ives

Design of a real-time wearable monitoringplatform for athletes during rehabilitation

Synchronized physiological (vital signs) and

motion signal collection during exerciseReal-time processing and feedback

Multi-modal signal processing

Rehabilitation and Performance assessmentby intelligent multi-signal processingtechniques (information fusion)


3/17

Monitoring

Medical Evaluation for the assessment of:Orthopedic rehabilitation and for the relativereduction of the rehabilitation period bypreventing injury relapses

Overall health condition

Effectiveness of physiotherapy

Performance evaluation for:Adjusting training programs

Assessing endurance and sporting condition

Evaluating performance

System Users

Professional Athletes

Habitual Athletes

Patients in Rehabilitation after SeriousInjury

Trainers

Medical and Paramedical Professionals


4/17

Athlete

Trainer

MedicalProfessionals

Researchers:

Medical Prof.Trainers

WEARABLEPLATFORM

REHABSTATION

DROMEASPORTAL

Architecture

Sub-Systems

Kjhsjb

Oyiy

ouou

Uoih

ihoih

Athlete Sub-System Rehab Station DROMEAS Portal

Athletes Sub-System:

Collects information from the athlete, through the motion andmedical sensors

Makes a preliminary medical analysis.

Rehab Station:Analyses and evaluates the medical measurements.Presents the processed information to the users.Provides the communication link between the athlete and therehabilitation experts.

DROMEAS Portal:Collects and processes information from the Rehab Stations overlarge periods of time.


5/17

Components

Athlete Subsystem

Medical dataCollector

Motion dataCollector

EvaluatorModule

PreliminaryDiagnosis

Module

Alert

Generator

Evaluation Unit

Kjhsjb

Oyiy

ouou

Uoih

ihoih

Athlete Rehab

Station


6/17

REHAB Stat ion

TRACKI NGMODULE

AnthropometricProfiles

MovementInput

MedicalSensorsMovement

SensorsEvaluator

Unit

Glasses

Earphones PDAsLaptop/PC

WirelessCommunication

MovementSimulator

!!

DIAGNOSISMODULE

MedicalProfiles

KnowledgeBase

MedicalInput

Synchronize

VISUALISATIONMODULE

UserProfiles

TRAI NINGREPOSI TORY

Portal

RESEARCHREPOSI TORY

Update Service

Network

TRAININGREPOSI TORY



RehabStations

Mobile Agents

Network

UserProfile

s

PDAsLaptop/PC


7/17

Communication Links

BLUETOOTHIEEE 802.11

technologies over LAN

Athletes

transceiver

Rehabilitation

advisors group

outdoors

Gateway

REHAB

station

DROMEASportal

IP Core

Network

High speed

Network

Rehabilitationadvisors group

indoors

Preliminary

I nformation Flow

Synchronizationmodule

usersthresholds

Evaluator

PreliminaryDiagnosismodule

Alertgenerator

module

MeasurementsBuffer

WearableMedicalSensorsModule

MedicalSignals

Collector

WearableMotion

SensorsModule

MotionSignals

Collector

users high-riskfactors

Communication Module

MedicalMeasurements

DB

MotionMeasurements

DB

Medical SignalClassification Module

MedicalProfiles

MovementSimulator Module

AnthropometricsProfiles DB

TrainingRepository

User Profiles

Visualization

Module

Communication Module

Update Service ResearchRepository

VisualizationModule User Profiles

Medical SensingSystem

Motion SensingSystem

Evaluation Unit

Diagnosis Module

Tracking Module

PORTAL

REHAB STATIONATHLETE SUBSYSTEM


8/17

Signals Acquisit ionAthlete monitoring includes:

Cardiovascular measurementsECG

Blood Pressure (before and after exercise)

Breath rateRespiratory effort

Breath patterns

TemperatureMeasurement at site of injury and contralateral

site for detecting deterioration of injury

Signals Acquisit ion

Pain AssessmentPain characterizations

None

Occasional and slight pain during activities

Always present but bearable (slight restriction)

Present and unbearable (unable to continue)

Through direct input (pain button)

Motion measurementsJoint angles with 6 Electrogoniometers (for bothlegs at hip, knee and ankle)

Speed


9/17



All signal inputs are fed to the Wearable Computer forfurther processing.

Pain descriptions are directly logged to the WearableComputer through the Athlete / System interactioncomponents

The synchronized data are wirelessly transmitted tothe Rehab Station


10/17

Signal AnalysisThresholding

Application of signal thresholds to generate alerts

Comparison / Correlation of SignalsComparison of signals and application ofthresholds to generate alerts / assessments(temperature, joint angle measurements)

Correlation of Signals for indirect assessment

Medical Rules

Use of rules provided by medical experts toprovide assessments

Combination of rules to provide assessments

Signal Analysis

Multi-Modal Signal AnalysisCombined use of medical knowledge /rules and heuristic knowledge

Information fusionUse of inputs from many sensors through afuser to provide a better assessment thanwith any individual sensor.

Use of medical knowledge for designingfuser rules


11/17

Signal Analysis

Information / Decision Fusion layer

Composed of a number of fusion objects

Fusion objects are responsible to fuse decisions fromlocal decision objects to decide on diagnosis

Fusion objects incorporate intelligent functions suchas:

Rule-Based Systems

Fuzzy LogicNeural Networks

Output: Diagnosis / Assessment vector

Signal Analysis(Decision Process)

Sensor #1

Sensor #2

Sensor #N

Sensor #1Sensor #1

Sensor #2Sensor #2

Sensor #NSensor #N

Feature estimator #1

Feature estimator #2

Feature estimator #N

Feature estimator #1Feature estimator #1

Feature estimator #2Feature estimator #2

Feature estimator #NFeature estimator #N

Analysis/local

decision #1

Analysis/local

decision #2

Decision fusion

Decision Process

Analysis/local

decision #1

Analysis/local

decision #1

Analysis/local

decision #2

Analysis/local

decision #2

Decision fusionDecision fusion

Decision Process

Signals Features

Local

Decision

Global

Decision


12/17

Signal Analysis(Preliminary Diagnosis)

Feature

estimator #1

Signal

Pre-processing

Modul e

Feature

estimator #2

Featureestimator #N

Analysis /local

decis ion #1

Analysis /local

decis ion #1

Analysis /local

decis ion #2

Analysis /local

decis ion #2

Decision fusionDecision fusion

Preliminary Diagnosis

Alert

Decision Process

Users

Thresholds

Sensor #1Sensor #1

Sensor #2Sensor #2

Sensor #NSensor #N

ECG Analysis

Detection of Heart Rate (HR)

Detection of Heart Rate Variability (HRV)

HRV Analysis

Statistical Analysis

Time- Frequency AnalysisFeature Extraction

Neural Network Training

Use of Decision Criteria

Classification

RR-interval duration signal analysis

Rule based System


13/17

ECG Analysis

Preprocessing

Extraction of HRV of each ECG

Partition of HRV in 32-point segments (3.426segments)

Detection using HRV Features

Extraction of HRV statistical characteristics (SDNN,

r_MSSD, SDSD, pNN5, pNN10, pNN50)63 combinations of characteristics

NN training for each combination

ECG Analysis

DETECTION with HRV Time-FrequencyAnalysis

Computation of Time-Frequency distributions(STFT and 18 distributions belonging to Cohenclass)

Feature extraction form STFT and eachdistribution

Neural Network Training for STFT and eachdistribution

DECISION CRITERIAMean Value

Voting

Selective Voting


14/17

ECG Analysis(Detection Results)

89.48%87.53%Selective Voting

78.43%82.60%Voting

78.18%80.68%Mean value

SPECIFICITYSENSITIVITYCRITERION

92,91%89.95%Selective Voting

89.25%86.84%Voting

88.65%87.64%Mean valueSPECIFICITYSENSITIVITYCRITERION

Detection with Time-Frequency Analysis

Detection with Statistical Analysis

Analysis of Other Signals

Joint Angle Signals

Input: Joint angles from hip, knee and ankle foreach leg

Processing: Detection of characteristic angles inpre-selected points of the gait cycle

Analysis: Detection of Abnormalities in Walking Running comparing healthy injured legs

Output: Assessment of rehabilitation / recovery


15/17


Breath RateInput: Respiratory Effort

Processing:Detection of Breath Rate

Detection of Breath Patterns

Analysis:Assessment of Breath Pattern alterations during physicalactivity rehabilitation

Indirect estimation of VO2 max

Output: Breath Rate and Breath Patternassessments to be correlated with cardiovascularassessments for assessing physical condition


Temperature MeasurementsInput: Temperature inputs of injury siteand contralateral site

Processing and analysis: Comparison oftemperature levels between sites Providean assessment when difference exceeds athreshold

Output: Assessment of improvement /deterioration of injury


16/17

Technical Considerat ionsSignal distortion in real-time ECG

Baseline wandering

Distorted morphology

Desired Signal ) Baseline wanderingB) Distorted morphology

Technical Considerat ions

Wearability (size, weight, comfort)

Noise from overlying tissues

Distortion of signals due to movement

Accurate positioning and repositioning of

sensors (repeatability of measurements)Algorithm selection

Minimize pre-processing

Unsupervised Training

Amount of Sensor data


17/17

System Evaluat ion

System evaluated according to standardclinical protocols

Heart Stress testing

Gait analysis

Controlled environment

a wearable platform for the monitoring of health condition and sport performance and the realtime...

Documents