Abstract. Ambient assisted living (AAL) systems need to understand the user's situation, which makes activity recognition an important component. Falls are one of the most critical problems of the elderly, so AAL systems often incorporate fall detection. We present an activity recognition (AR) and fall detection (FD) system aiming to provide robust real-time performance. It uses two wearable accelerometers, since this is probably the most mature technology for such purpose. For the AR, we developed an architecture that combines rules to recognize postures, which ensures that the behavior of the system is predictable and robust, and classifiers trained with machine learning algorithms, which provide maximum accuracy in the cases that cannot be handled by the rules. For the FD, rules are used that take into account high accelerations associated with falls and the recognized horizontal orientation (e.g., falling is often followed by lying). The system was tested on a dataset containing a wide range of activities, two different types of falls and two events easily mistaken for falls. The Fmeasure of the AR was 99 %, even though it was never tested on the same persons it was trained on. The F-measure of the FD was 78 % due to the difficulty of the events to be recognized and the need for real-time performance, which made it impossible to rely on the recognition of long lying after a fall.

No abstract

No abstract

-The increasing size of the elderly population is driving the development of ambient assisted living systems and telehealth. The recognition of the user's everyday activities and detection of alarming situations are important components of such systems. Moreover, the monitoring of vital signs, like the ECG, has a key role in telecare and telemonitoring systems. Therefore, in this paper we propose a system that monitors the user by combining an ECG sensor and two accelerometers. Our system recognizes the user's activities and detects falls using the accelerometer data. The ECG data is analyzed in order to extract relevant physiological signals: heart rate, respiration rate, etc. In order to improve the reliability and robustness of the system, the measured accelerometer signals can be combined with the ECG signal in order to detect anomalies in the user's behavior and heart-related problems. The proposed proof-of-concept system could contribute significantly to the quality, unobtrusiveness and robustness of the health care and patient safety.

No abstract