Elderly activities recognition and classification for applications in assisted livingMOBILE AND PERVASIVE SYSTEMS – PROF. MARCO AVVENUTIEgidi SaraVillardita Alessio Chernbumroong, Cang, Atkins, Yu

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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Introduction and overview

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• 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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System requirements (Kleinberger 2007)AcceptanceAdaptationUsability

Main approaches:Wearable sensorsCamerasAmbient sensor (on object monitoring)

Small, low cost and non intrusive sensors

Practical assisted living requirements

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IADLs: Instrumental Activities of Daily Livings

BADLs: Basic Activities of Daily Livings, i.e. necessary for self-care

Activity selection

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• Inertial sensors (IMU – Inertial measurement unit)

Accelerometer, Gyroscope, Magnetometer

• Altimeter

• Hearth Rate (HR)

• Barometer

• Light

• Temperature

Embedded sensors

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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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Overview of implementation

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Wrist-worn equipment:

- Accelerometer

- Gyroscope

- Magnetometer

- Bio-sensors

Study specific:

- Temperature

- Altimeter eZ430-Chronos watch,

Texas Instruments

Sensors

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Method design

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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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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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Classification accuracy

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Experimentation and results

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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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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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Discussion and further work

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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

More patterns (over 30k in the second paper)

Deep learning

Improvements and further work

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Thank you for listening