Recognizing Human Activities and Detecting Falls in Real-time (original) (raw)

The paper presents a system that recognizes human activities and detects falls in real-time. It consists of two wearable accelerometers placed on the user's torso and thigh. The system is tuned for robustness and real-time performance by combining domain-specific rules and classifiers trained with machine learning. The offline evaluation of the system's performance was conducted on a dataset containing a wide range of activities and different types of falls. The F-measure of the activity recognition and fall detection were 96% and 78%, respectively. Additionally, the system was evaluated at the EvAAL-2013 activity recognition competition and awarded the first place, achieving the score of 83.6%, which was for 14.2 percentage points better than the secondplace system. The competition's evaluation was performed in a living lab using several criteria: recognition performance, user-acceptance, recognition delay, system installation complexity and interoperability with other systems.