Fatigue Monitoring System (FaMS)
Module type(s) | Reasoning Engine |
Description | FMS uses an artificial intelligence (AI) model relying on machine learning which estimates exertion level of subjects based on static data (e.g. age, weight, etc.) and dynamic data (e.g. HR, EDA, skin temperature). |
Adopted technologies and methods | Apache Spark ML framework; Scala; Apache Avro; Docker; Random Forest Classifier. |
Input | Workers physiological data from wearable devices on workers (e.g. HR, HRR, etc.); static data collected through questionnaire (e.g. age, needs, expectations, experience, etc.). |
Output | Worker fatigue level to be used by “decision maker” modules to make human aware decisions. |
Features | Adoption of machine learning to create use case specific AI model; Calculation in real-time of exertion stress level; Dynamic features use; Multi-device compatibility. |