This document summarizes a fall detection system that uses accelerometer data and time series classification algorithms like Dynamic Time Warping (DTW) and K-Nearest Neighbors (KNN) to classify activities into categories like falling, running, walking upstairs, and walking. It reports the accuracy of the classification on test data, achieving an average accuracy of 95% using DTW and 100% accuracy using KNN.

1. Fall Detection
PRESENTED BY PRIAGUNG KHUSUMANEGARA

2. Timeseries Classification DTW

3. Data Accelerometer
Total Acceleration
a = 𝑋2 + 𝑌2 + 𝑍2

4. Fall Template

5. Activity Template

7. 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

8. 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

9. 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