fallanalyser

Fallanalyser is a software tool designed to analyze fall events and related risk factors across domains such as elder care, workplace safety, sports science, and robotics. The goal of fallanalyser is to identify patterns that precede a fall, quantify the severity of falls, and support interventions that reduce the incidence of injury.

The system typically ingests data from multiple sources: inertial measurement units including accelerometers and gyroscopes, video recordings, environmental sensors, and incident logs. It combines signal processing, computer vision, and statistical or machine learning methods to detect fall events, classify types of falls, and estimate metrics such as fall duration, impact, velocity, and post-fall risk.

Applications include monitoring older adults in assisted living and home settings, evaluating workplace safety programs, analyzing athletes' fall mechanics to improve technique, and supporting research in biomechanics and rehabilitation.

Implementation aspects often feature a modular architecture with data import adapters, an analysis engine, visualization dashboards, and reporting tools. Common data formats include CSV or JSON for events and measurements, video in standard codecs, and streaming sensor data via APIs. Some implementations are open source, others are commercial software suites.

Limitations and considerations include reliance on data quality and sensor placement, the need for expert labeling for accurate annotations, potential biases in machine learning models, and privacy and consent concerns when handling video or health data. Results are typically used to inform safety protocols and are most effective when combined with domain expertise and appropriate interventions.