Wired for Zen: Quantifying Meditation via Wearables
Additional information
Type of assignment: BSc. thesis
Internal/external:
How many students? 1
Supervision: Individually
Includes data collection? Yes
Type of research: Quantitative
Number of ECTS? 15 ECTS
Research assignment
While meditation is often described subjectively as a state of "calm" or "grounding," the objective physiological correlates of this state remain difficult to track outside of a controlled lab setting. Specifically, we don't know if the "relaxation response" reported by practitioners translates to robust, longitudinal changes in autonomic regulation, specifically the balance between sympathetic arousal (stress) and parasympathetic recovery (rest). To understand this, we apply signal processing techniques to data from wearable biosensors (e.g., Empatica E4/Embrace) to analyze Heart Rate Variability (HRV) and Galvanic Skin Response (GSR) during meditation training. You will learn to navigate the challenges of noisy real-world sensor data, cleaning and extracting features to determine if a wrist-worn device can reliably "detect" the meditative state and track physiological neuroplasticity over time.
Who do we look for?
Student should be good in R and curious to be a problem solver to find solutions for unique analysis and find solutions for artifact rejection and signal processing of wearable PPG and EDA data is supported with an explanation of the device architecture and provided with existing analysis scripts.
A Method for Stress Detection Using Empatica E4 and Machine Learning