1. Online classifier construction algorithm for human activity detection using a tri-axial accelerometer Yen-Ping Chen, Jhun-Ying Yang, Shun-Nan Liou, Gwo-Yun Lee, Jeen-Shing Wang Yen-Ping Chen, Jhun-Ying Yang, Shun-Nan Liou, Gwo-Yun Lee, Jeen-Shing Wang Presenter : Yi-Che Liu From ： From ： Applied Mathematics and Computation Citation : 2 I.F. : 0.961

2. Introduction Purpose : Purpose : Recognizing different types of human daily activities using a tri-axial accelerometer. Recognizing different types of human daily activities using a tri-axial accelerometer. Online add new training samples. Online add new training samples. Online add additional classes. Online add additional classes. Online delete an existing class. Online delete an existing class.

3. Method Dynamic linear discriminant analysis Dynamic linear discriminant analysis Update within-class scatter matrix and between- class scatter matrix. Update within-class scatter matrix and between- class scatter matrix.

4. Online classifier construction algorithm Data preprocessing segment the acceleration data into windows with 50% overlap. segment the acceleration data into windows with 50% overlap. Feature extraction Mean Mean Correlation between axes Correlation between axes Energy Energy Interquartile range Interquartile range Mean absolute deviation Mean absolute deviation Root mean square Root mean square Variance Variance Standard deviation Standard deviation Fuzzy basis function classifier

5. The acceleration data used in our experiments was collected using the MMA7260Q tri-axial accelerometer The acceleration data used in our experiments was collected using the MMA7260Q tri-axial accelerometer The fundamental requirements of the acceleration device:lightness, sensing, and wireless transmission The fundamental requirements of the acceleration device:lightness, sensing, and wireless transmission Experimental design and results

6. The accelerometer ’ s sensitivity was set from -4.0g~+4.0g The accelerometer ’ s sensitivity was set from -4.0g~+4.0g The output signal of the accelerometer is sampled at 100Hz by a 10-bit ADC The output signal of the accelerometer is sampled at 100Hz by a 10-bit ADC Experiments were performed on windows XP OS,with P4 2.4GHz CPU and 512 MB memory Experiments were performed on windows XP OS,with P4 2.4GHz CPU and 512 MB memory

7. Eight common domestic activities: Eight common domestic activities: standing, sitting, walking, running, vacuuming, scrubbing, brushing teeth, working at a computer. We gathered acceleration data from seven normal, healthy subjects(4 females, 3males;age 24.1 ± 1.8 years) in a controlled laboratory environment. We gathered acceleration data from seven normal, healthy subjects(4 females, 3males;age 24.1 ± 1.8 years) in a controlled laboratory environment.

8. A single tri-axial accelerometer module was mounted on the dominant wrist,which is better for discriminating activities involving upper body movements. A single tri-axial accelerometer module was mounted on the dominant wrist,which is better for discriminating activities involving upper body movements. All were asked to perform each activity for two minutes. All were asked to perform each activity for two minutes. Sampling frequency is 100Hz.So the total number of the acceleration samples for each activity is 12,000. Sampling frequency is 100Hz.So the total number of the acceleration samples for each activity is 12,000.

9. In the experiments, we applied the proposed online classifier construction scheme to the three cases : 1.Adding additional new samples to the existing activities 2.Adding new human activities 3.Deleting existing activities

10. Adding additional new samples to the existing activities This graph shows the average recognition rates of consecutively adding additional 10% new samples to the eight activity classes.

11. This graph shows the execution times of the dynamic LDA and the conventional LDA.

12. This graph shows the dynamic LDA only requires a fixed size of memory to store the statistical information of the currently available training data for the update of the scatter matrices.

13. Adding new human activities This graph shows the average recognition rates of different numbers of activities. This is reasonable because the recognition task becomes more difficult as the number of classes increases.

14. This graph shows the execution times of the dynamic LDA and the conventional LDA. This is because when a new activity is added, the conventional LDA has to re-compute “SW” and “SB” using the whole training data.

15. This graph shows the memory requirements. Because the conventional LDA has to re-compute “SW” and “SB” too.

16. Deleting existing activities This graph shows the execution times of the dynamic LDA and the conventional LDA. For the dynamic LDA, we used Eqs. (16)–(18) to update the scatter matrices without any computation from the training data. For the conventional LDA, we re-computed the scatter matrices by excluding the data of removed activities from the training data.

17. This graph shows the memory requirements. Because the conventional LDA has to re-compute “SW” and “SB” too.

18. The proposed dynamic LDA is capable of online updating the scatter matrices with the same results as those obtained by the conventional LDA operated in an offline mode. The proposed dynamic LDA is capable of online updating the scatter matrices with the same results as those obtained by the conventional LDA operated in an offline mode. The storage of the complete training dataset is not required for the dynamic LDA. The storage of the complete training dataset is not required for the dynamic LDA. The memory usage and computational efficiency is greatly improved. The memory usage and computational efficiency is greatly improved. Conclusions