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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Associations Rules (1/3)
Create group of activities with different size
ID1 Result
Many views with different RHS
Impossible rule extraction
25/6/200912
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Associations Rules (2/3)
ID1,ID2 Result
Similar results, but we see 2 rules that start to appear
Low confidence
25/6/200913
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Associations Rules (3/3)
ID1,ID2,ID3 Result & ID1,ID2,ID3,ID4 Result
We have rules now
No ability of verification
25/6/200914
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Neural Networks - General
25/6/200915
Typical Neuron Feed Forward
Supervised learning
Input
Desired
Output
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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
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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
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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
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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