1. Chen Jimena Melisa Parodi 302424544 Menashe Shalom 301376869
Allowing Early Inspection of Activity Data from a Highly Distributed Bodynet with a Hierarchical-Clustering-of-Segments Approach
Chen Jimena Melisa Parodi Menashe Shalom

2. Body-wide inertial sensing systems
Purpose: The output delivered by body-wide inertial sensing systems has proven to contain sufficient information to distinguish between a large number of complex physical activities.
מערכת סנסורים הפרושים על הגוף לאפיון תנועה.

3. Body-wide inertial sensing systems
Problem: The high dimensionality of the raw sensor signals with the large set of possible features tends to increase rapidly causing a problem in the parts that calculate and select features.

4. Body-wide inertial sensing systems
Existing methods for reducing large input dimensions- traditional feature selection methods such as:
Boosting-based approach
Component Analysis
Wrapper methods

5. Body-wide inertial sensing systems
New proposed method:
Hierarchical-Clustering-of-Segments Approach

6. Hierarchical-Clustering-of-Segments Approach
Relies on the hierarchical clustering of discovered patterns of both inertial trajectories and angular data, across all of the body-worn sensors. Allows thorough human inspection of which data segments are discovered as meaningful for given activities.

7. Hierarchical-Clustering-of-Segments Approach
Five phases of the proposed system

8. Hierarchical-Clustering-of-Segments Approach
The contributions:
Showing that it is possible to obtain an early insight into which sensors and features can be expected to perform well in distinguishing the different target classes.

9. Hierarchical-Clustering-of-Segments Approach
Showing that it is possible to inspect at an early stage which classes are prone to misclassification between each other for particular sensors and features.

10. Hierarchical-Clustering-of-Segments Approach
Showing that many problems appearing at classification can be identified already at the clustering stage.

11. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology Dataset 19 activities from a car maintenance scenario, workers wearing a body sensor network integrated in a jacket with sensors in their torso, upper an lower arms and hands.

12. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology

13. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology Visual Inspection at the clustering level 1. How can the high dimensional data be analyzed at the clustering level for its suitability for activity recognition, without resorting to feature selection?

14. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology Visual Inspection at the clustering level 2. How do the generated clusters correlate to the final activity recognition results?

15. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology
Visual Inspection at the clustering level
Bright colors- High similarity.
Diagonal white line- Cluster’s precision value of pairs of the same class.
Subplots- symmetric divided by the diagonal white line.

16. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology
Visual Inspection at the clustering level
The visual Inspection showed that it is possible to:
Identify channels which tend to mix up different clusters from those who have a good class distinctiveness.
See for single channels which classes data tend to be close to each other and might get easily mixed up.

17. Hierarchical-Clustering-of-Segments Approach
Experiment and methodology Comparison with the Recognition results The ability to detect classes that are hard to distinguish at the clustering phase would not only lead to a faster way of inspecting the data: we could also examine which sensors (or body locations) would contribute to such difficulties.

18. Algorithm description
Problem
dimensionality of the resulting data
Solution
concentrate only on the important signals
concentrate only on parts of the data that are significant for the activity

19. Algorithm description
Maybe dig more about the purpose of the alg.

20. Algorithm description
Data preprocessing and segmentation
Clustering
Cluster Analysis
Classification

21. Algorithm description
Data preprocessing and segmentation
data from the sensors is used to construct a body model
Low pass filtering to segment the continuous signal
להסביר קצת על חיישני כיוון וחיישני זיויות וכו'.

22. Algorithm description
B. Clustering
Total of 34 channels
Goal – find significant channels and segments for each activity we want to spot
Hierarchical clustering
Dynamic Time Warping (DTW)

24. Algorithm description
C. Cluster Analysis
To get an idea of the distinctiveness between classes of single channels we calculate the cluster precision on the cluster.

25. Algorithm description
C. Cluster Analysis
Precision –
Take all the clusters of one channel and one class and find clusters centers
Calculate distances between cluster centers in both classes using dynamic time warping.
The sum of the smallest distances divided by the number of clusters provides the cluster precision.

26. Algorithm description
C. Cluster Analysis
The higher the Precision the more dissimilar are the cluster center of the two classes on this channel
Figure 2 –
white = similar
Black = different

27. Algorithm description
C. Cluster Analysis
Examples –
Z axis of the hand tip, wrist and also of the elbow have a good distinctiveness
shoulder is not very significant

28. Algorithm description
Classification
Calculate distances between test data segment and all cluster centers.
Found activity
Thresholds –
DTW where 95%of the class members still nearer to cluster center
Mean of all DTW from cluster members to cluster centers.

29. Algorithm description
Classification
Channels are sorted by summing all cluster precision values for each channel (Table 2)

31. OBSERVATIONS AND DISCUSSION
Cluster precision insight of channel performance
determine which channel has the most distinguishable character between the different classes
up and down movements seem to be quite characteristic for the activities of our dataset
worst channels are the shoulder in y and z direction
Only the x axis of the shoulder ranks higher, demonstrating that leaning forward and backward can give hints to some of our activities.

32. OBSERVATIONS AND DISCUSSION
B. Cluster precision insight of class performance
Classes can Easley mix up
Summing up all cluster precision values of one class on all channels shows close values