Case Study :“Human Daily Activity Recognition With Sparse

Representation Using Wearable Sensors”IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL.17, NO. 3, MAY 2013 Mi Zhang, Student Member, IEEE, and Alexander A. Sawchuk, Life Fellow, IEEE

Giuseppe Gagliano , Fabio Greco Pisa, 24/05/2016

Introduction on Pervasive Healthcare :

who and why ?

Among all the human activity sensing technologies, wearable sensing system has the advantage of being with people throughout the day, enabling continuously collecting people’s activity information.

for what ?

To deliver long-term personalized fitness monitoring, preventive and chronic healthcare, and elderly support.

how ?

This is a pattern recognition problem so...

Framework

Feature Extraction

● Which features do I extract?

● Should I select features or should I use Random Projection?

Training

Feature Extraction

● Which features do I extract?

● Should I select features or should I use Random Projection?

Overcomplete Dictionary Construction & Sparse Representation

● Feature Vectors

Training

Feature Extraction

● Which features do I extract?

● Should I select features or should I use Random Projection?

Overcomplete Dictionary Construction & Sparse Representation

● Feature Vectors

Training

D1 Dk

Feature Extraction

● Which features do I extract?

● Should I select features or should I use Random Projection?

Overcomplete Dictionary Construction & Sparse Representation

● Feature Vectors

● Overcomplete Dictionary

● Sparse coefficient

Training

D1 Dk

Recognition

1. Sparse recovery via l1 minimization

Recognition

1. Sparse recovery via l1 minimization

Recognition

1. Sparse recovery via l1 minimization

2. Classification via Sparse Representation (SR)

3. Sparsity confidence measure

Recognition

1. Sparse recovery via l1 minimization

2. Classification via Sparse Representation (SR)

3. Sparsity confidence measure

Example

Experiments

● Leave-one-subject-out Cross Validation

● Noise tolerance

●

Effect of the Feature Dimension and Comparison to Baseline Algorithms

NN - Nearest Neighbour

NBC - Naive Bayesian Classifier

SVM - Support Vector Machine

SR - Sparse Representation

Effect of the Choice of Features and Random Projection

SCI as a Measure of Confidence

CONCLUSION AND FUTURE WORK

Implementing this approach on mobile phones Test on larger datasets containing more types of human activities