Andreas Schrempf, Thomas Minarik Upper Austria University of Applied Sciences Medical Engineering

Andreas Schrempf, Thomas MinarikUpper Austria University of Applied SciencesMedical Engineering

Linz, Austria

[email protected]

An interactive activity- monitoring device for use in home- rehabilitation

Upper Austria University of Applied Sciences

4th International Conference MRH | Pilsen June 30th 2011 Page 2

Upper Austria University of Applied Sciences


Faculty of Applied Health and Social Sciences


Education: Applied Health and Social SciencesResearch: - Medical Engineering

- Assisting Technologies and Processes for the Elderly

Campus Linz ...

Emergency HospitalLinz

Rehab. Amb.Linz

General HospitalLinz

Pediadric and Gynecological Clinic Linz

GKK-OOEHealth Ins. Comp.

HospitalDiakonissen

Neurological Clinic Wagner Jauregg Linz

Most of the medical partners are within a square km.

University of Applied SciencesCampus Linz

Activity Measurement Application AreasApplication and Benefits :– preventive care

– measures taken to prevent diseases– motivation factor

– therapy evaluation – objective measures of the therapy

progress– Documentation– assessment

– therapy control – compare exercise measurement data

against golden reference– adjust intensity according progress, – provide feedback


Device DevelopmentActilab – Features• developed in cooperation with g.tec medical engineering

– Device development – g.tec medical engineering– Algorithm development – FH-OOE

• Mobile system– duration, intensity, frequency of activity

• Intended to become a medical device• Triaxial accelerometer

– measuring range: ±18g– Resolution: ~ 6.25mg

• Barometric pressure sensor– Measurement range 30-120kPa– Resolution: ~10cm

• GSM module


Device DevelopmentNEON – Features• developed in cooperation with spantec GmbH

• Triaxial accelerometer– measuring range: ±8g– Resolution: ~ 3.9mg

• Barometric pressure sensor – 30 kPa - 120 kPa measuring range– Resolution: 1.5 Pa ( ∼ 10cm at s.l.)

• GPS module– accuracy position: 5m CEP (Circular error probable)– accuracy velocity: 0.1m/s– update rate: 1Hz


Device DevelopmentNEON – Features


mini USB connector

LEDs

ANT module

Device DevelopmentNEON – Features• Compatible with all ANT(+) devices

– e.g. Garmin HRM1G

• micro SD-card– 2 GB data memory

• UART connection to local PC

• ANT module– 2.4GHz wireless networking protocol designed

for wireless sensor networks– network (internet) connection possible (bridging)


Device DevelopmentCommunication Concepts


Actilab

NEON

AccelerometryCOM moves during Walking


Center Of Mass (COM) moves during walking according to the different phases of the walking cycle



Frequency and amplitudes change dependingon walking speed



Putting an activity measurement device near to the COM allows to measure these movements in terms of the accelerations.

vertical acceleration:

AccelerometryPA - Energy Expenditure


Total Energy Expenditure (TEE)

TEE = BMR + DIT + PA• BMR: basal metabolic rate BMR• DIT: diet induced thermogenesis• PA: physical activity

Physical activity level (according to WHO) (1.2 sitting, 1.8-1.9 walking)



Estimation ( modified Goldman equation)

• T: terrain factor• : overall efficiency• m: body mass• v: walking speed• a(v): total average acceleration,

depends on walking speed• dh/dt: change of elevation per time unit• g: gravity constant

condition Tasphalt 1.0gravel 1.4soft underground 1.8loose sand 2.1deep snow (25cm) 3.3



• estimation based on mean acceleration (indoor/outdoor)

• measured by GPS (outdoor)• measured by acceleration

sensors

• measured by barometric pressure sensor (indoor/outdoor)

• measured by GPS (outdoor)

• overall efficiency, depends on age, fitness level, gender, …



mean total body acceleration• High-pass filtering (offset,

artefacts)• rectifying (power)• Low-pass filtering (mean

acceleration)

• distinguishing between no activity, walking and running

• estimation of walking speed

a(t) LP | . | HP

walking

running



Estimation of walking speed (based only on accelerations)

walking

running



heart-rate measurementsallows to estimate the EE:• depends on age,• depends on fitness level,• depends on gender,• different for walking and

running,• hysteresis,• direct measurement of the

energy expenditure (allows to quantify the efficiency)

HR [bpm]

dE/d

t [kc

al/m

in/k

g]

AccelerometryAccuracy Assessment


altitude: GPS vs. pressure sensor• pressure sensor requires stable weather

conditions,• GPS accuracy limited, when low signal

quality• GPS may loose signal, does not work

indoor

forest (low GPS signal quality)

AccelerometryAccuracy Assessment


walking speed: GPS vs. barometric press. sensor• pressure sensor requires

stable weather conditions,• GPS may loose signal,

does not work indoor• --> sensor fusion

stairs down

altitude GPS (low signal quality)

round trip

stairs up, then down

altitude barometer

walking speedGPS

walking speed prediction byaccelerometer(problem: up and down)

AccelerometrySensorfusion/Autom. Calibration


Problems:• GPS signal only available

outdoor• GPS: altitude low accuracy,

especially for small altitude differences

• barometric height measurement only valid for short time or during stable weather conditions

• Speed prediction by accelerometer not accurate enough when going up or down

• Efficiency not constant varies between different subjects (depending on age, fitness level or pathology)V

Solutions:• Sensor fusion combining GPS

and barometric pressure sensor. Weighting of signal depends on signal quality and/or availability

• Individual calibration of speed prediction algorithm, during good GPS-signal quality

• Individual calibration of efficiency using heart-rate monitor

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Andreas Schrempf, Thomas Minarik Upper Austria University of Applied Sciences Medical Engineering