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Human Activity Recognition from
Smart-Phone Sensor Data using a
Multi-Class Ensemble Learning in
Home Monitoring
HEALTH AND BIO-SECURITY
Soumya GHOSE , Jhimli MITRA 1, Mohan KARUNANITHI 1 and Jason DOWLING 1
1. The Australian e-Health and Research Centre, Commonwealth Scientific and Industrial
Research Organization (CSIRO), Australia
2 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Population Demographics - Australia
Source : https://aifs.gov.au/publications/ageing-yet-diverse
3 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Population Demographics - Australia
The number of Australians aged 65 and over is expected to
increase rapidly from 13 per cent of the population to 25 per cent
of the population by 2042 [1].
Assisting the chronically ill and aged population accounted for
over 70% of Australia's 103.6 billion dollar health expenditure
during 2007-2008, that is only likely to increase [2].
This expenditure may be reduced by remote monitoring of aged
or chronically ill patients that in turn may potentially reduce the
frequency of hospitalizations.1. Australian Government, Australia's Demographic Challenges,
http://demographics.treasury.gov.au/content/_download/australias_demographic_challenges/html/adc-04.asp,
accessed on [14th March, 2015]
2. CSIRO, Home Monitoring of Chronic Disease for Aged Care, http://www.csiro.au/
en/Research/DPF/Areas/Digital-health/Improving-access/Home-monitoring, accessed on [14th March 2015].
4 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Remote Monitoring of Patients
Source : http://www.mdtmag.com/articles/2013/05/wireless-enabled-remote-patient-
monitoring-solutions
5 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Remote Monitoring of Patients - Challenges
Source : http://www.mdtmag.com/articles/2013/05/wireless-enabled-remote-patient-
monitoring-solutions
Data Volume Real-time DecisionsReal Time
Predictive Analysis
6 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Remote Activity Monitoring of Patients – Smartphones
Source: http://www.rcrwireless.com/20150302/opinion/reader-forum-sensor-adoption-in-smartphones-tag10
7 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Smartphone Sensor Data
UCI machine learning repository, Human Activity Recognition
Using Smartphone Dataset
archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using
+Smartphones, accessed on [14th March 2015].
The experiments were carried out on a group of 30 volunteers
within an age bracket of 19-48 years.
Each participant performed six activates (sitting, standing,
walking, laying, walking upstairs and walking downstairs)
wearing a Samsung Galaxy S2 smart-phone.
The experiments were further video tagged to aid in data labelling
(Ground truth).
8 |
Training
Data
Feature
Extraction
Supervised
Learning
Prediction
Model
Test
Data
Feature
Extraction
Real Time
Predictions
Training
Data –Smartphone Sensor Data
Feature
Extraction-Temporal
Motion features
Supervised
Learning –Random Forest
Prediction
Model
Activity Recognition – Building Prediction Model
Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
9 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Activity Recognition – Decision Trees and Forest
http://accidentalepicurean.com/2013/01/accidental-funnies-bacon-decision-tree-chart/
Weak Biased Predictor –
Decision Tree
BMI, Cholesterol, Exercise routine(Good Features)
Ensemble of Decision Trees / Random Forest
= Strong Predictor
Bad Feature
10 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Activity Recognition – Features
The data from the accelerometer and gyroscope, measuring 3-
axial linear accelerations and angular velocity respectively at a
constant rate of 50Hz were captured for activity recognition.
Data pre-processing - The sensor data were pre-processed by
applying a Gaussian noise reduction filter and then re-sampled in
fixed-width sliding windows of 2.56 sec with 50% overlap.
Feature Extraction - From each window, a vector of time and
frequency domains features of the accelerometer signal is
extracted. Mean, standard deviation, signal magnitude area,
entropy, signal-pair correlation, and Fourier Transform were
used to extract the frequency components of the signal.
11 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Activity Recognition – Features
Features - Mean, standard deviation, signal magnitude area,
entropy, signal-pair correlation, and Fourier Transform
(frequency)-> 3-axial linear accelerations and angular velocity,
(sampling resolution 2.56 secs)
Test Data
Prediction (Walking, Sitting etc)
12 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Activity Recognition – Validation
The public dataset was randomly partitioned into 70% training and 30% testing
samples. The common evaluation metrics like the true positive rate, false positive rate
and precision were measured and validated with the ROC curve.
13 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Conclusions
An automatic activity recognition system has been proposed
with a random forest classifier.
In future, this work may be extended to develop a risk
prediction system from patient's activity state to alert health
personnel.
Accurate activity prediction system for the elderly with the
proposed model may potentially reduce the risk associated with
remote monitoring.
Additional sensor data from heart rate monitoring and body
temperature may be incorporated as features in the classification
system to design a patient-specific and illness-specific remote
risk assessment system.
14 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring
Thank You. Questions?