Nowadays, wearable sensors have become ubiquitous in our daily lives, from fitness trackers to smartwatches and health monitoring devices. However, analyzing the data and getting meaningful insights can be challenging. In this workshop, we will investigate the use of supervised machine learning methods, such as Support Vector Machine, Decision Trees, and Artificial Neural Networks, in understanding data from wearable sensors. The workshop will include a hands-on project that will require data collection, data processing, feature extraction and selection and training and evaluation machine learning models.

Day 1

  •  Introduction to Wearable Sensors (1 hour)
  •  Supervised Machine Learning + Hands-on Examples (4.5 hours)
    Data collection and Feature extraction
    Support Vector Machine
    Decision Tree
    Artificial Neural Network
  • Introduction to the Hands-on Project (30 minutes).

Day 2

  • Group Project work (4.5 hours)
    The project will require data collection, preprocessing the data, feature
    extraction/selection, training of supervised machine learning models and
  •  Time to prepare a 10 minutes presentations (30 minutes)
  •  10 minutes Presentation/Team (1 hour)

About the Speaker

Manizheh Zand

Research Assistant at Santa Clara University