smartphone-based activity recognition for pervasive healthcare - utilizing cloud infrastructure for...
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Smartphone-based Activity Recognition for Pervasive Healthcare- Utilizing Cloud Infrastructure for Data Modeling
Bingchuan Yuan, John HerbertUniversity College Cork, Ireland
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Outline
2
Introduction1
Activity Recognition Approach2
Cloud-based Data Modeling3
4
Conclusion5
Experiment & Result
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Introduction
Pervasive Healthcare
Traditional clinical setting Home-centered settingWireless Sensor Networks (WSNs)&Communication technologies
3
WSN
Internet
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Introduction
CARA for Pervasive HealthcareCARA (Context-Aware Real-time Assistant)Real-time Intelligent At-home healthcare
Activity Recognition in CARAActivity of Daily Living (ADL) monitoringAnomaly detection
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Introduction
State of The Art- Environmental sensor-based approach Pros: ambient assistant monitoringCons: intrusive, large installation
- Wearable sensor-based approachPros: small, low cost, non-invasiveCons: customized, impractical, processing power
- Smartphone-based approachPros: ubiquity, sensing and computingCons: battery, insufficient accuracy
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Activity Recognition
Our ApproachSmartphone-basedWearable wireless sensor integratedHybrid ClassifierCloud-based data modeling
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Activity Recognition
ADLs in a Home Environment
- Static Posture:Sitting, Standing, Lying, Bending and Leaning back
- Dynamic Movement:Walking, Running, Walking Stairs, Washing Hands, Sweeping and Falling
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Activity Recognition
Overview
8
Load Classification Model
Data Collection
Feature Extraction
Distinguish Static and Dynamic
Activity
Activity Classification
Classification Model
Optimization
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Inertial Sensor R
eading
0
5
10
15
20
Absolute AccelerationAbsolute Rotation
Activity Recognition
Feature Extraction
9
Walking Running Sweeping
Washing Hand
1s - Window
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Activity Recognition
Feature Extraction
10
Feature Trunk Acceleration
Thigh Acceleration
Thigh Orientation
Min X, Y, Z, |ACC| X, Y, Z, |ACC| Azimuth, Pitch, Roll, |GYRO|
Max X, Y, Z, |ACC| X, Y, Z, |ACC| Azimuth, Pitch, Roll, |GYRO|
Mean X, Y, Z, |ACC| X, Y, Z, |ACC| Azimuth, Pitch, Roll, |GYRO|
Standard Deviation
X, Y, Z, |ACC| X, Y, Z, |ACC| Azimuth, Pitch, Roll, |GYRO|
Zero Cross X, Y, Z X, Y, Z Azimuth, Pitch, Roll
Mean Cross |ACC| |ACC| |GYRO|
Angular X, Y, Z X, Y, Z
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Activity Recognition
Distinguish static and dynamic Activity
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Dynamic Activity
Static Activity
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Activity Recognition
Real-time Activity Classification Using Hybrid Classifier
- Static activity: Threshold-based method- Dynamic activity: Machine learning classification model
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Activity Recognition
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)cos(a180
= degreesg
accrc
Inclination Angle:
Static Activity
-90o 90o
0o
0oVertical
BendingLeaning
Horizontal
α
Trunk Vertical -30o <α < 30o
Trunk Bending 30o <β < 60o
Trunk Leaning -60o <γ < -30o
Trunk Horizontal 60o <δ < 90o
-90o <δ < -60o
β γ
δ
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Activity Recognition
Dynamic Activity
Weka* for data miningMachine learning algorithms:- Bayesian Network- Decision Tree- K-Nearest Neighbor- Neural Network
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* Weka 3: Data Mining Software (Developed by University of Waikato)
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Activity Recognition
Transition of Activity States
S0: Transitional State
S1-S5: State of each activity
R: Transition Rule
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Cloud-based Data Modeling
Activity Data Modeling
Training the classification models: tradeoff
between accuracy and cost
- Personalized model:
One for each individual (better accuracy)
- Universal model:
One size fits all (lower cost)
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Cloud-based Data Modeling
Model Adaptation
17
Build/Update Models
Real-time Classification
New Training Dataset
Best Model
UploadDataset
Model Evaluation
DownloadModel
Unsupervised Learning
Client
CloudMisclassified
Filtering
Default Model
Adapted Model
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Cloud-based Data Modeling
Cloud-based Data Analysis Framework
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Cloud Environment
Blob
D4D3D2D1Data Queue
U4U3U2U1User Register Queue
MResult Queue
M
U1
U4
Clients
Controller
DTask Queue
D
D
D
DALL
Universal Model
Neural Networkl
Decision Tree
Bayesian Network
MnM3M2M1Model Queue
Evaluation & Filter(Get Best Model)
Worker Roles
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Experiment and Result
Data Collection
Eight volunteersHome settingActivity tasksSupervised learningGround truth testing set
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Experiment and Result
Data SetActivity instances of the Default Model
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Walkin
g
Running
Walkin
g St
airs
Swee
ping
Was
hing
Han
ds
Fallin
g
Stan
ding
Sitti
ng
Layin
g
Bending
Lean
ing
Back
Rollin
g
1680
1237
2169
1316
833
69
510615 561 586
462
180
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Experiment and Result
Confusion Matrix Table (Default Model)
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Activity a b c d e f g h i j k l ACC
WALKING (a)1852
0 10 0 0 0 6 0 0 0 0 0 99.14%
RUNNING (b) 11105
32 1 0 0 0 0 0 0 0 0 97.01%
WALK STAIRS (c) 47 01937
4 0 0 1 4 0 0 2 0 97.09%
SWEEPING (d) 6 0 11378
14 0 4 0 0 0 0 0 98.22%
WASHING HANDS (e) 0 0 0 0 873 0 7 0 0 2 1 0 98.87%
FALLING (f) 0 0 2 1 2 32 6 1 5 3 0 0 61.54%
STANDING (g) 0 0 0 0 0 0 822 0 0 0 0 0 100%
SITTING (h) 0 0 0 0 0 0 972 0 0 0 0 0 100%
LYING (i) 0 0 0 0 0 0 0 1 649 0 0 0 99.85%
BENDING (j) 0 0 0 0 0 0 0 0 0 718 0 0 100%
LEANING BACK (k) 0 0 0 0 0 0 0 0 0 0 557 0 100%
ROLLING (l) 0 0 4 10 1 1 1 5 8 0 1 187 85.78%*Default Model built by the KNN classifier and evaluated using 10-fold cross-validation
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Experiment and Result
Performance Overview
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1st Run 2nd Run 3rd Run 4th Run
Bayes Net-work
0.843848 0.925727 0.929306 0.930358
Decision Tree 0.907383 0.930201 0.947651 0.948546
K-NN 0.912304 0.941387 0.944072 0.955705
Neural Net-work
0.8783 0.906935 0.918568 0.940492
81%
85%
89%
93%
97%
1st Run 2nd Run 3rd Run 4th Run
Bayes Net-work
0.84454 0.896719 0.954814 0.959656
Decision Tree 0.683701 0.958042 0.966111 0.965352
K-NN 0.723507 0.795051 0.828402 0.884346
Neural Net-work
0.742335 0.970414 0.984938 0.985476
63%
73%
83%
93%
Overall model accuracy for the female user B
Overall model accuracy for the male user A
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Experiment and Result
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Classifier TP Rate FP Rate Precision Recall F-Score Time(ms) Accuracy
First Run (1980 instances 1874 instances)
Decision Tree 0.684 0.034 0.089 0.684 0.657 1838 68.37%
Bayesian Network 0.845 0.011 0.931 0.845 0.860 2725 84.45%
K-Nearest Neighbor 0.724 0.044 0.811 0.724 0.684 4849 72.35%
Neural Network 0.742 0.040 0.811 0.742 0.720 84682 74.23%
Second Run (3493 instances 3392 instances)
Decision Tree 0.958 0.006 0.960 0.958 0.957 1248 95.80%
Bayesian Network 0.897 0.010 0.943 0.897 0.899 1482 89.67%
K-Nearest Neighbor 0.795 0.036 0.860 0.795 0.763 5504 79.51%
Neural Network 0.970 0.004 0.972 0.970 0.970 145111 97.04%
Third Run (5482 instances 5403 instances)
Decision Tree 0.966 0.006 0.967 0.966 0.966 1358 96.61%
Bayesian Network 0.955 0.005 0.967 0.955 0.958 1727 95.48%
K-Nearest Neighbor 0.828 0.031 0.873 0.828 0.810 7019 82.84%
Neural Network 0.985 0.002 0.985 0.985 0.985 227774 98.49%
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Conclusion
Key PointsSmartphone-basedWearable wireless sensor integratedHybrid ClassifierCloud-based data modeling
Future WorkAutomatically distinguish static and dynamic activityDynamically allocate system resource in the cloud
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University College Cork