ashishanand7 / human_activity

Human Activity Recognition

This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.

This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.

File details:

HAR_EDA.ipynb presents Exploratory Data Analysis on the dataset alongside some observations on data dimensionality.
HAR_PREDICTION_MODELS.ipynb explores approaches related to classical Machine Learning models and ensembles thereof.
HAR_LSTM.ipynb details training of a LSTM Model for the same purpose.

Conclusion

Simpler models work better with applied domain knowledge, EDA and appropriate feature engineering.

Appendix - How data was recorded

By using the sensors(Gyroscope and accelerometer) in a smartphone, they have captured '3-axial linear acceleration'(tAcc-XYZ) from accelerometer and '3-axial angular velocity' (tGyro-XYZ) from Gyroscope with several variations.

prefix 't' in those metrics denotes time.

suffix 'XYZ' represents 3-axial signals in X , Y, and Z directions.

• Accelerometer and Gyroscope readings are taken from 30 volunteers(referred as subjects) while performing the following 6 Activities.

  1. Walking
  2. WalkingUpstairs
  3. WalkingDownstairs
  4. Standing
  5. Sitting
  6. Laying

• Readings are divided into a window of 2.56 seconds with 50% overlapping.

• Accelerometer readings are divided into gravity acceleration and body acceleration readings, which has x,y and z components each.

• Gyroscope readings are the measure of angular velocities which has x,y and z components.

• Jerk signals are calculated for BodyAcceleration readings.

• Fourier Transforms are made on the above time readings to obtain frequency readings.

• Now, on all the base signal readings., mean, max, mad, sma, arcoefficient, engerybands,entropy etc., are calculated for each window.

• We get a feature vector of 561 features and these features are given in the dataset.

• Each window of readings is a datapoint of 561 features.