1. Chao Xu, Parth H. Pathak, et al. HotMobile’15
Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch
Chao Xu, Parth H. Pathak, et al. HotMobile’15

2. What They Did Classify gesture type: Finger, Hand or Arm
Recognize 37 gestures
Recognize finger writing with smartwatch

3. What They Did Classify gesture type: Finger, Hand or Arm
Recognize 37 gestures
Recognize finger writing with smartwatch

4. Experiment and Observation

5. Methodology Data Collection Feature extraction Classification
Use Shimmer device to collection data
Data from accelerometer sensor and gyroscope sensor at 128 Hz
Feature extraction
Formula
Magnitude values are FFT coefficients calculated over the time window
Classification

6. What They Did Classify gesture type: Finger, Hand or Arm
Recognize 37 gestures
Recognize finger writing with smartwatch

7. Gesture Types & Theory
Gesture Types
Recognize Theory

8. Feature Extraction & Evaluation
Features Used for Classification
Information Gain on Each Feature

9. Identification Performance
Gesture Recognition Accuracy
TP rate of Naïve-Bayes Classifier

10. What They Did Classify gesture type: Finger, Hand or Arm
Recognize 37 gestures
Recognize finger writing with smartwatch

11. Data Collection The size of the alphabet is 2.5” in width and height
Accelerometer and gyroscope data is collected
Each of the 26 alphabets is repeated 10 times
The features are the same as those used in gesture recognition

12. Performance
Logistic regression outperforms the other two classifiers in accuracy
Average accuracy is 94.6%

13. Discussion
This paper reveal the potential that the smartwatch can be used to detect fine-grained movements of user’s fingers
There are still challenges to realizing the true potential of smartwatch
In experiments, the user’s wrist and arm are affixed to the chair arm
Different people write and perform different gestures in different ways
Finger-writing in the air
Detecting continuous writing to form words and sentences