Additional tasks related to activity recogntion - PowerPoint Presentation

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Additional tasks related to activity recogntion - PPT Presentation

Mitja Luštrek Jožef Stefan Institute Department of Intelligent Systems Slovenia Tutorial at the University of Bremen November 2012 Outline Fall detection with accelerometers Fall detection with location sensors ID: 778157

activity energy detection recognized energy activity recognized detection fall acceleration machine expenditure learning lying classifier ground reliable met sitting

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Slide1

Additional tasks related to activity recogntion

Mitja Luštrek

Jožef Stefan InstituteDepartment of Intelligent SystemsSlovenia

Tutorial at the University of Bremen, November 2012

Slide2

OutlineFall detection with accelerometersFall detection with location sensors

Fall detection with both sensor typesHuman energy expenditure estimation

Slide3

OutlineFall detection with accelerometers

Fall detection with location sensorsFall detection with both sensor typesHuman energy expenditure estimation

Slide4

Acceleration threshold

1–1.5 g

IF acceleration > threshold

THEN

fall

Slide5

OrientationIF a

cceleration > threshold AND AFTERWARDS not upright for 10 seconds

THEN fallNot upright = not –30ᵒ < ϕ < 30ᵒ for 90% of the timeϕ can be adapted to the person

Slide6

Activity and movementIF

acceleration > threshold AND AFTERWARDS recognized (lying

OR on all fours OR sitting on the ground) for 10 seconds AND user immobile THEN fallImmobile = s < thresholdCan skip some activities or movement or switch conjunction/disjunction

Slide7

OutlineFall detection with accelerometers

Fall detection with location sensorsFall detection with both sensor typesHuman energy expenditure estimation

Slide8

Fall detection

Sensors

PreprocessingActivity recogntion

Detection of unusual behavior

Fall detection

User interface

Alarm

Warning

Initialization

False alarm

Machine learning

Expert rules

Merging

Slide9

Machine learningAttributes:Percentages of activities in the last 5, 7.5 and 10 seconds (fall is detected after 10 seconds of lying)

The time of last recognized fallingIs the user in bed

Slide10

Machine learningAttributes:Percentages of activities in the last 5, 7.5 and 10 seconds (fall is detected after 10 seconds of lying)

The time of last recognized fallingIs the user in bedTwo classifiers:

SVM and C4.5 decision treesFall if both return fall

Slide11

Expert rulesIF recognized

falling AND AFTERWARDS recognized

lying/sitting on the ground outside bed AND user immobile THEN fall

Slide12

Expert rulesIF recognized

falling AND AFTERWARDS recognized

lying/sitting on the ground outside bed AND user immobile THEN fallIF recognized falling AND AFTERWARDS recognized lying/sitting on the ground outside bed for a longer time THEN fall

Slide13

Expert rulesIF recognized

falling AND AFTERWARDS recognized

lying/sitting on the ground outside bed AND user immobile THEN fallIF recognized falling AND AFTERWARDS recognized lying/sitting on the ground outside bed for a longer time THEN fallIF recognized lying/sitting on the ground outside bed for an even longer time

AND

user

immobile

THEN

fall

Slide14

Expert rulesIF recognized

falling AND AFTERWARDS recognized

AND

user

immobile

THEN

fall

recognized

lying/sitting on the ground outside bed

for a very long time

THEN

fall

Slide15

MergingFall in two cases:

Machine learning and expert rules return fallOne of the modules returns fall continuously for 3 seconds

Slide16

OutlineFall detection with accelerometersFall detection with location sensors

Fall detection with both sensor typesHuman energy expenditure estimation

Slide17

Accelerometers + location sensorsIF recognized (lying

OR on all fours OR sitting on the ground) for 10 seconds AND location = floor

AND user immobile THEN fallActivity recognition (mainly) from accelerometersMobility from accelerometersLocation from location sensorsAgain variations possible

Slide18

Experimental results – activity recogntion

Slide19

Experimental results – fall detection

Slide20

OutlineFall detection with accelerometersFall detection with location sensors

Fall detection with both sensor typesHuman energy expenditure estimation

Slide21

IntroductionMotivation:

Chiron project – monitoring of congestive heart failure patientsThe patient’s energy expenditure (= intensity of movement) provides context for heart activity

Slide22

IntroductionMotivation:

Chiron project – monitoring of congestive heart failure patientsThe patient’s energy expenditure (= intensity of movement) provides context for heart activity Method:

Two wearable accelerometers → accelerationAcceleration → activityAcceleration + activity → energy expenditureMachine learning

Slide23

Direct calorimetryMeasures the total heat output of a person

Separate “dry” and “wet” measurements

Slide24

Indirect calorimetry

Measure inhaled, exhaled O2, CO2Compute the amount of O2 consumed and CO

2 produced by metabolismCompute energy consumption from these amounts

Slide25

Doubly labeled waterWater with deuterium and oxygen-18 administered

Oxygen-18 lost through breathing out CO2Both elements lost through water in urine, sweat ...The difference can be used to compute the amount CO2

produced by metabolismThis amount proportional to the energy consumed

Slide26

DiaryWrite down the activitiesUse reference energy consumption value for each activity

Compendium of Physical Activities with values for over 500 common activities

Slide27