From Noisy Sensors to Events: Event Detection in Sensor data with Kalman Filters and Hidden Markov Models
Sensors operating in complex environments produce noisy data. Determining exactly when a system transitions between states — and what values it is recording — is surprisingly hard: vibrations, environmental changes, and gradual shifts all conspire against simple threshold approaches. This talk walks through a real-world Python pipeline that solves this problem, starting with classical signal processing, exposing its failure modes, and then building a principled solution using a Kalman filter for noise reduction coupled with a Hidden Markov Model (HMM) for state inference. Attendees will leave understanding how to frame sensor problems as state estimation tasks and how to apply these techniques in Python using necessary libraries.
