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Human Activity Simulation Modeling in
Smart Spaces
Sirui (Quincy) Liu
Ph.D. Dissertation Defense
1. Review

1) Motivation

2) Activity Playback Modeling

2. Activity Visualization Modeling

1) Persim 3D System Overview

2) Action Modeling

3) Object Modeling

4) Sensor Modeling

5) Character Modeling

6) Validation

3. Conclusions
Human Activity Simulation Modeling in Smart Spaces
1. Review

1) Motivation

2) Activity Playback Modeling

2. Activity Visualization Modeling

1) Persim 3D System Overview

2) Action Modeling

3) Object Modeling

4) Sensor Modeling

5) Character Modeling

6) Validation

3. Conclusions
Human Activity Simulation Modeling in Smart Spaces
What are Activities of Daily Living (ADLs)?
The things we normally do in daily living including any daily activity
we perform for self-care such as…
Eating Bathing Dressing Toileting
Why model and simulate activities of daily living?
Gator Tech Smart House
Difficulties in Conducting Experiments in Real Smart Spaces
• Significant cost and elaborate groundwork

• Recruiting participants to live in an intrusive environment

• Recording all human activities over a long period of time

• Hard to be modified to test new theories, sensors and applications

• Regulatory limitations prohibit use of human subjects in all scenarios
Model and simulate human activities.
• Have control over attributes of activities

• Reflect effects of activities on the smart space

• Capable of producing realistic visualizations
Requirements of Activity Simulation Model
Activity Playback Modeling
Persim 3D
Simulation
Manager
Visualization
Engine
Activity Playback
Modeling
Activity Playback
Algorithms
Activity Model
Output
Activity
Visualizations
Sensor
Datasets
Action 1 Action 2 Action 3Action 1 Action 2
Activity = Action Sequence
Action 1 Action 1 Action 2 Action 3Action 2
Activity = Action Sequence
1. Review

1) Motivation

2) Activity Playback Modeling

2. Activity Visualization Modeling

1) Persim 3D System Overview

2) Action Modeling

3) Object Modeling

4) Sensor Modeling

5) Character Modeling

6) Validation

3. Conclusions
Human Activity Simulation Modeling in Smart Spaces
1. Review

1) Motivation

2) Activity Playback Modeling

2. Activity Visualization Modeling

1) Persim 3D System Overview

2) Action Modeling

3) Object Modeling

4) Sensor Modeling

5) Character Modeling

6) Validation

3. Conclusions
Human Activity Simulation Modeling in Smart Spaces
Persim 3D
Simulation
Configuration
Actual Dataset
Validate Through
Similarity Comparison
Synthesized Dataset
Persim 3D
Simulation
Configuration
Actual Dataset
Validate Through
Similarity Comparison
Visualization
Visual Feedback
Synthesized Dataset
Validate Through
User Study
Simulation*GUI
Space&Design
Action&Design
Activity&Design
Context&Design
Simulation&Control
Sensor/Action*Simulation*Module
Sensor&Simulation&
Engine
Visualization&
Engine
Action&Performer/Manager
Simulation*Interface
Activity/Context*Simulation*Module
Activity&Playback&
Engine
Context@driven&
Simulation&Engine
Simulation&Driver/Manager
Persim 3D Architecture
Simulation*GUI
Space&Design
Action&Design
Activity&Design
Context&Design
Simulation&Control
Sensor/Action*Simulation*Module
Sensor&Simulation&
Engine
Visualization&
Engine
Action&Performer/Manager
Simulation*Interface
Activity/Context*Simulation*Module
Activity&Playback&
Engine
Context@driven&
Simulation&Engine
Simulation&Driver/Manager
Persim 3D Architecture
Simulation*GUI
Space&Design
Action&Design
Activity&Design
Context&Design
Simulation&Control
Sensor/Action*Simulation*Module
Sensor&Simulation&
Engine
Visualization&
Engine
Action&Performer/Manager
Simulation*Interface
Activity/Context*Simulation*Module
Activity&Playback&
Engine
Context@driven&
Simulation&Engine
Simulation&Driver/Manager
Persim 3D Architecture
Simulation*GUI
Space&Design
Action&Design
Activity&Design
Context&Design
Simulation&Control
Sensor/Action*Simulation*Module
Sensor&Simulation&
Engine
Visualization&
Engine
Action&Performer/Manager
Simulation*Interface
Activity/Context*Simulation*Module
Activity&Playback&
Engine
Context@driven&
Simulation&Engine
Simulation&Driver/Manager
Persim 3D Architecture
Activity Simulation
Character Model
Action Model Activity ModelObject Model
Sensor ModelVisualization Engine
GUI
Activity Simulation
Character Model
Action Model Activity ModelObject Model
Sensor ModelVisualization Engine
GUI
Character Profiling
GUI
State Space
Context Simulation APIs
Context Simulation
Activity Simulation
Character Model
Action Model Activity ModelObject Model
Sensor ModelVisualization Engine
GUI
Character Profiling
GUI
State Space
Context Simulation APIs
Activity 1Activity 2Activity 3Activity 4Activity 5
Early Persim 3D
No. Type Activity
1 Normal Washing Face
2 Normal Washing Hands
3 Normal Drinking Water
4 Normal Combing Hair
5 Normal Shaving
6 Normal Sleeping
7 Normal Using Bathroom
8 Normal Leaving Home
9 Normal Using Cellphone
10 Normal Taking Medication
11 Normal Watching TV
12 Abnormal Falling
No. Action No. Action
1 Turn On Light 14 Pick Up
2 Turn Off Light 15 Put Down
3 Wash Hands 16 Comb Hair
4 Dry Hands 17 Flush Toilet
5 Wash Face 18 Open Door
6 Dry Face 19 Close Door
7 Take A Pill 20 Fall
8 Sit Down 21 Lie Down
9 Stand Up 22 Turn Off Lamp
10 Use Toilet 23 Sleep
11 Drink 24 Get Up
12 Use Cellphone 25 Use TV Remote
13 Shave 26 Watch TV
Action SensorObject
• Name

• ID

• Model

• Transform: position + rotation

• Character’s transform

• In-hand transform

• Status

• Animation: door opening/closing
• Name
• Object
• Animation
• Effect: changes object’s status
• Importance: optional or necessary
• Max occurrence
• Prerequisite
• ID

• Type

• Attaching entity

• Value

• Sensing function
Contact Sensor
Motion Sensor
RFID TagRFID Reader
RFID Reader Function
Distance(m)
0
1
2
3
4
Animation Frame
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420
Distance Between RFID Reader and Tag Return Value
IR Sensor
Type Installation Use
Contact Attached to doors, windows, drawers, etc. Detect if two objects are in contact.
Motion Installed in floor, chairs, beds, etc. Detect motion of and around the object.
IR Installed in faucets and toilets. Detect if there are people within range.
RFID Attached to objects. Identify people and objects.
Activity Visualization Engine
• Character Animator
• Animations
• Movement Control
• RFID Reader
• Navigation System
• Action Processor
• Physics Engine
• Object Manager
Grounded
Sit Down/
Stand Up
Lie Down/
Get Up
Pick Up/
Put Down
Other
Actions
Actions While
Sitting
Bed Actions Use Object
Character Animator
Animations
Asset Store MayaMotion Capture
+
Motion Capture
+
Ethan Pearl
Transitioning Movements
Position A, Posture A Position B, Posture B
• Character’s body movement: walking, turning, etc.

• Not action, but fill the gap between actions.
Transitioning Movements
Next Action
Change Object’s Status
Perform Transitioning Movements
Play Next Action’s Animation
Update Sensor Value
Activity Visualization Engine
Action 2
Action 3
…
Action n
Action 1
Activity Playback Engine
Action Queue
Activity Simulation Module
Turn On Light Wash Hands Dry Hands Turn Off Light
Washing Hands
Turn On Light
Dry Hands
Walk to Bathroom Light Light On Turn Body Walk to Sink
Wash HandsTurn BodyWalk to TowelTurn Body
Walk to Bathroom Light Turn Off Light Light Off Walk Out of Bathroom
Action
Transitioning Movement
Action Effect
Washing Hands
Movement Control
Navigation System
Physics Engine
Action Processor
Object Manager
Persim 3D System Data Structure
Environment Configuration
Objects Sensors Characters
Actions
Activities
Contexts
Simulation Log
Sensor Readings
Character’s Movement
Character’s Activities
Character’s Actions
Persim 3D
Simulation
Configuration
Actual Dataset
Visualization
Visual Feedback
Synthesized Dataset
Validate Through
Similarity Comparison
Validate Through
User Study
Dataset Similarity Analysis User Study
Simulation Data Real Datavs
• R = sensor data of one activity from the real dataset

• S = sensor data of the same activity from the synthesized dataset

• ri = ith sensor event in R

• si = ith sensor event in S

• l = max {|R|, |S|}

• 𝜅 = number of common sensor events in R and S
• 𝛼 = number of sensor events that didn’t occur in the counterpart

• 𝜂 = number of sensor events in R and S combined
Weighted Euclidean Distance
• m = number of sensor events in R

• n = number of sensor events in S
Dataset Similarity
Activity Average Max Min Occurrence
Sleeping 1.00 1.00 1.00 11
Using Bathroom 0.98 1.00 0.83 11
Taking Medication 0.86 1.00 0.57 3
Watching TV 0.76 1.00 0.50 4
Leaving Home 1.00 1.00 1.00 10
Dataset Similarity
Online User Study
• Amazon Mechanical Turk.

• 7 activities, 3 videos each activity.

• Users rated realism on scale of 1 (very poor) to 10 (very high).

• 952 workers. Every video was rated by 100 workers.

• Age ranged from 15 to 77 (avg = 33.96, SD = 10.85).
AverageRating
0
2.5
5
7.5
10
W
ashing
FaceW
ashing
H
andsDrinking
W
ater
C
om
bing
H
air
Shaving
U
sing
C
ellphone
Falling
6.89
7.55
6.84
7.427.737.46
7.02
Visual Realism Rating
Demo
1. Review

1) Motivation

2) Activity Playback Modeling

2. Activity Visualization Modeling

1) Persim 3D System Overview

2) Action Modeling

3) Object Modeling

4) Sensor Modeling

5) Character Modeling

6) Validation

3. Conclusions
Human Activity Simulation Modeling in Smart Spaces
1. Review

1) Motivation

2) Activity Playback Modeling

2. Activity Visualization Modeling

1) Persim 3D System Overview

2) Action Modeling

3) Object Modeling

4) Sensor Modeling

5) Character Modeling

6) Validation

3. Conclusions
Human Activity Simulation Modeling in Smart Spaces
Simulation*GUI
Space&Design
Action&Design
Activity&Design
Context&Design
Simulation&Control
Sensor/Action*Simulation*Module
Sensor&Simulation&
Engine
Visualization&
Engine
Action&Performer/Manager
Simulation*Interface
Activity/Context*Simulation*Module
Activity&Playback&
Engine
Context@driven&
Simulation&Engine
Simulation&Driver/Manager
Persim 3D Architecture
Future Work
• Virtual environment

• Character modeling

• Animation

• User interface

• Context simulation integration

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