Elderly activity recognition presentation. Based on S Chernbumroong paper.

1. Elderly activities recognition and
classification for applications in assisted
living
MOBILE AND PERVASIVE SYSTEMS – PROF. MARCO AVVENUTI
Egidi Sara
Villardita Alessio
Chernbumroong, Cang, Atkins, Yu

2. Roadmap
● Introduction to the problem
Activity selection
Embedded sensors
Goals
● Overview of implementation
Hardware aspects
Software
● Experimental results
● Discussion of further works
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3. Introduction and overview
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4. • Rising average life span
• Higher demand in long-term care
• Higher cost for health care and ineffective and insufficient care
• Need for a continuous monitoring of elderly people health
• Foster home-based care
• Elder people independence and enhance living quality
How? Activity recognition applications
Introduction
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5. System requirements (Kleinberger 2007)
Acceptance
Adaptation
Usability
Main approaches:
Wearable sensors
Cameras
Ambient sensor (on object monitoring)
Small, low cost and non intrusive sensors
Practical assisted living requirements
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6. IADLs: Instrumental Activities of Daily Livings
BADLs: Basic Activities of Daily Livings, i.e. necessary for self-care
Activity selection
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7. • Inertial sensors (IMU – Inertial measurement unit)
Accelerometer, Gyroscope, Magnetometer
• Altimeter
• Hearth Rate (HR)
• Barometer
• Light
• Temperature
Embedded sensors
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8. Two hypotheses:
• Achieve high classification rate
• Combining data from multiple sensors
improves recognition accuracy
To the aim of:
• Health care
• Ambient Intelligence
• Abnormal behaviour detection
Goals
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9. Overview of implementation
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10. Wrist-worn equipment:
- Accelerometer
- Gyroscope
- Magnetometer
- Bio-sensors
Study specific:
- Temperature
- Altimeter
eZ430-Chronos watch,
Texas Instruments
Sensors
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11. Method design
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12. Sensor Data Time-domain Frequency-domain
Acceleration X-axis,
Acceleration Y-axis,
Acceleration Z-axis,
Acceleration magnitude,
Temperature, Altitude
Mean, Min, Max, Standard
Deviation, Variance, Range,
Root-Mean-Square,
Correlation, Difference,
Main Axis
Spectral energy, spectral
entropy,key coefficient
Total number of features 45 18
Features
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13. From 63 to 16 features, using feature
combination:
- Clamping to order features by impact
- Forward selection
This method allows weaker features to be
selected
Feature combination
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14. Classification accuracy
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15. Experimentation and results
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16. Data collection carried out to replicate natural living environment
12 participants worn 2 eZ430-Chronos watches
11 activities, 5 min each
19.2h of sensors data collected
Supervised by a researcher
Acceleration data collected using Matlab
Temperature and altitude directly recorded on watches internal memory
Experimental settings
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17. High classification rates on: sleeping, sweeping, watching TV, walking and feeding
High misclassification rates on : dressing, ironing, wash dishes, brush teeth
Sensor combination Accuracy (%)
Accelerometer 82.7694
Accelerometer, Temperature 87.5764
Accelerometer, Altimeter 89.3736
Accelerometer, Temperature, Altimeter 90.2250
Results
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18. Discussion and further work
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19. Imbalanced dataset fixed with under-sampling based on one of the misclassified activities
from 17843 to 7245 patterns
Dataset
12 elderly people for 19h of sensor data
Discussion points
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No ensemble, using the best classifier: SVM

20. More patterns (over 30k in the second paper)
Deep learning
Improvements and further work
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21. Thank you for listening