EECS 395, 495: Wireless and Mobile Health (mHealth)

Quarter Offered

Winter : TuTh 5-6:30 ; Alshurafa


With the increasing research activity in the field of mobile health there has been increased interest in passive sensing and activity recognition systems. The ultimate goal of this research is to improve our understanding of human activity and behavior and to design interventions and solutions that improve health outcomes for individuals, reduce healthcare costs and improve quality of life. The number of challenges in designing, implementing and evaluating these mHealth systems is growing, and so is the need for experts in this field.

This course aims to provide hands-on introduction and experience to the field of mHealth, with a focus on passive sensing data analytics (and more specifically time-series data generated by these sensors). I will teach the concept of a Passive Sensing Data Analytic chain (PASDAC) as a general-purpose framework for designing and evaluating mHealth systems including: pre-processing, segmentation, feature extraction/selection, classification, decision fusion and performance evaluation.

  • For CS students: This course fulfills the Systems Depth and Project Course requirements.
  • For CE students: This course fulfills your CE Embedded Systems Area (via petition form).

This course is intended for advanced CS and CE undergraduates and graduate students. If you’re interested in this class, but not sure you have the background, please contact the instructor.

COURSE INSTRUCTOR: Prof. Nabil Alshurafa (


EECS 214 AND [Any intro to ML, AI, HCI or Data Sciences course] OR EECS 214 AND EECS 212 OR EECS 214 AND [EECS 213 OR (EECS 205 & EECS 211)] OR Instructor Consent