Fall DetectionPRESENTED BY PRIAGUNG KHUSUMANEGARA

Timeseries Classification DTW

Data Accelerometer

Total Accelerationa =

Fall Template

Activity Template

Classification Report Accuracy Data Testing

Fall 1.00 20 Running 1.00 20 Upstairs 1.00 20 Walking 0.80 20

avg / total 0.95 80

Timeseries Classification: KNN & DTW

K-Nearest Neighbors algorithm (k-NN) is a non parametric method used for classification.

Algorithm:

1. Choose a value for k

2. Find the distance between unlabeled point and training points.

3. Find the k-nearest points to unlabeled points

4. Classify unlabeled point by a majority vote of its neighbors

Classification Report

Accuracy Data Testing

Fall 1.00 20 Running 1.00 20 Upstairs 1.00 20 Walking 1.00 20

avg / total 1.00 80