Hidden Markov Model-Based Fall Detection With Motion Sensor Orientation Calibration: A Case for Real-Life Home Monitoring.

Falls are a major threat for senior citizens' independent living. Motion sensor technologies and automatic fall detection systems have emerged as a reliable low-cost solution to this challenge. We develop a hidden Markov model (HMM) based fall detection system to detect falls automatically using a single motion sensor for real-life home monitoring scenarios. We propose a new representation for acceleration signals in HMMs to avoid feature engineering and developed a sensor orientation calibration algorithm to resolve sensor misplacement issues (misplaced sensor location and misaligned sensor orientation) in real-world scenarios. HMM classifiers are trained to detect falls based on acceleration signal data collected from motion sensors. We collect a dataset from experiments of simulated falls and normal activities and acquired a dataset from a real-world fall repository (FARSEEING) to evaluate our system. Our system achieves positive predictive value of 0.981 and sensitivity of 0.992 on the experiment dataset with 200 fall events and 385 normal activities, and positive predictive value of 0.786 and sensitivity of 1.000 on the real-world fall dataset with 22 fall events and 2618 normal activities. Our system's results significantly outperform benchmark systems, which shows the advantage of our HMM-based fall detection system with sensor orientation calibration. Our fall detection system is able to precisely detect falls in real-life home scenarios with a reasonably low false alarm ratet.