From Noisy Sensors to Events: Event Detection in Sensor data with Kalman Filters and Hidden Markov Models
Heavy industrial vehicles operate in harsh environments where weight sensors produce noisy data. Determining exactly when a vehicle is loaded — and how much it is carrying — is surprisingly hard: vibrations, terrain changes, and gradual load 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 loading-state inference. Attendees will leave understanding how to frame industrial sensor problems as state estimation tasks and how to apply these techniques in Python using necessary libraries.
