Abstract of case study—Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, is presented a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. This approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a L^1 minimization problem. Experimentally is validated the effectiveness of the sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects.This approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore ( in the original paper form Mi Zhang, Student Member, IEEE, and Alexander A. Sawchuk, Life Fellow, IEEE) is demonstrated that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within this framework.

1. 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

2. 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...

3. Framework

4. Feature Extraction
● Which features do I extract?
● Should I select features or should I use Random Projection?
Training

5. 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

6. 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

7. 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

8. Recognition
1. Sparse recovery via l1
minimization

9. Recognition
1. Sparse recovery via l1
minimization

10. Recognition
1. Sparse recovery via l1
minimization
2. Classification via Sparse Representation (SR)
3. Sparsity confidence measure

11. Recognition
1. Sparse recovery via l1
minimization
2. Classification via Sparse Representation (SR)
3. Sparsity confidence measure

12. Example

13. Experiments
● Leave-one-subject-out Cross Validation
● Noise tolerance
●

14. Effect of the Feature Dimension and Comparison to
Baseline Algorithms
NN - Nearest Neighbour
NBC - Naive Bayesian Classifier
SVM - Support Vector Machine
SR - Sparse Representation

15. Effect of the Choice of Features and Random Projection

16. SCI as a Measure of Confidence

17. CONCLUSION AND FUTURE WORK
Implementing this approach on mobile phonesTest on larger datasets containing
more types of human activities