ΥΡΙΓΟ ΜΙΧΑΗΛ Επιβλέπων: ΠΟΛΥΖΟ ΓΕΩΡΓΙΟ

2ος Αξιολογητής: ΞΥΛΩΜΕΝΟ ΓΕΩΡΓΙΟ

Design & implementation of a system to

record & recognize Activities of Daily

Living

ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΗΜΙΟ ΑΘΗΝΩΝΠΡΟΓΡΑΜΜΑ ΜΕΣΑΠΣΤΧΙΑΚΩΝ ΠΟΤΔΩΝ

ΣΗΝ ΕΠΙΣΗΜΗ ΣΩΝ ΤΠΟΛΟΓΙΣΩΝ

Outline Problem definition

System to record actions

Implementation of a system to record actions

Data representation & interoperability

Activity recognition

Reduction to group of activities

Conclusions

25/6/20092

Problem definition

People on earth are getting older:

Greece

29,9% raise in population for people over 65 in 1994-2005.

31% of population the elder since 2050.

USA

14% of population the elder since 2010

Taiwan

10% of population the elder at 2007.

Elder prefer their home to live

Economists support this

Need to create a system to record indoor daily actions.

25/6/20093

System to record actions

Selection of passive RFID sensors Longer lifetime

Low cost

Low weight

Unlimited operation stamina

Challenges

Use of one technology only

Startup data collection

Need for confirmed activities.

25/6/20094

Implementation of a system to record

actions

Sensor setting

Happens only once

Sensor ID

Location

Sensor reading

Periodically

Sensor ID

Date-Time

Storing

Locally at first

25/6/20095

Data representation & Interoperability

Data representation language definition

Expansion ability

Collect data from different technologies

(Temperature, Blood pressure, …)

Description

ID,

value,

Sensor type.

Different types of data

Description using the same schema

Different types of readings

25/6/20096

Dataset send

Copy the dataset in an XML file

Using a specific schema

Send to the Remote Server

Filename: Date_User.xml

25/6/20097

Dataset we use

Use of Dataset from a project at MIT

2 weeks duration

Data from 2 apartments

1st apartment: person 30 years old

2nd apartment: elder person

Activities are confirmed

By the person with the use of a PDA

At the time the person did the activity

25/6/20098

Activity recognition (1/2)

Time associations insertion

Time rules

Association rules extraction

Use of tool “Weka”

Algorithm Apriori

Pattern: {Α, Β, Γ, ...} Ω Support ≥ minsup

Confidence ≥ minconf

25/6/20099

Activity recognition (2/2)

Score

Sum(#rule views * confidence)

Accuracy

#Correct recognitions/#Recognitions

Results:

25/6/200910

Activity recognition (2 weeks training)

Accuracy

• 0.85

• 0.88

• 0.92

• 1.0

• 0.5

Activity

• Bathing

• Grooming

• Doing Laundry

• Preparing lunch

• Toileting

Reduction to group of activities

Number the different activities

Shaping data in order to use them in higher level

Techniques we tried

Association rules

Classification

Neural Networks

25/6/200911

Year day

• 109

• 109• 109• 109• 109

Start time(seconds)

• 10613

• 17937

• 19793

• 25045

• 29892

End time(seconds)

• 12090

• 21291

• 29008

• 25996

• 30205

Activity

• 'Toileting'

• 'Toileting'

• 'Watching TV'

• 'Preparing breakfast'

• 'Washing dishes'

Date

• [19,4,2003]

• [19,4,2003]

• [19,4,2003]

• [19,4,2003]

• [19,4,2003]

Start time

• [2,56,53]

• [4,58,57]

• [5,29,53]

• [6,57,25]

• [8,18,12]

End time

• [3,21,30]

• [5,54,51]

• [8,3,28]

• [7,13,16]

• [8,23,25]

Activity ID

• 1• 1• 15• 2• 9

Associations Rules (1/3)

Create group of activities with different size

ID1 Result

Many views with different RHS

Impossible rule extraction

25/6/200912

Associations Rules (2/3)

ID1,ID2 Result

Similar results, but we see 2 rules that start to appear

Low confidence

25/6/200913

Associations Rules (3/3)

ID1,ID2,ID3 Result & ID1,ID2,ID3,ID4 Result

We have rules now

No ability of verification

25/6/200914

Neural Networks - General

25/6/200915

Typical Neuron Feed Forward

Supervised learning

Input

Desired

Output

Neural Networks - implementation

Split dataset in train and validate part

60%-40%

Tries: ID1 Result

ID1,ID2 Result

ID1,ID2,ID3 Result

ID1,ID2,ID3,ID4 Result

25/6/200916

Results of the neural (1/2)

For ID Result

Almost every time wrong

Even in train part

ID1,ID2 Result

Similar results

25/6/200917

Results of the neural (2/2)

For ID1,ID2,ID3 Result

For ID1,ID2,ID3,ID4 Result

Train Error Report Validate Error Report

25/6/200918

Conclusions

Contribution to the Project “Archangel”

Data collection applications implementation

Description of the interoperability of the system

Reduction from sensor readings activity to

group of activities next activity

Necessary step

Implementation of a Dataset

Longer in duration

From an elder person

Activities must be confirmed

For the training part

25/6/200919