Wearable wireless accelerometer with embedded fall-detection logic for multi-sensor ambient assisted living applications

Abstract

In this paper we present a wearable wireless accelerometer device for people fall detection in ambient assisted living (AAL) applications, communicating with care holders and relatives of the assisted person through an ADSL based gateway. The wearable system is a PCB with a commercial three-axes, digital-output MEMS accelerometer chip, a ZigBee transceiver and a programmable logic (FPGA). The raw acceleration stream from the MEMS sensor is processed by suitable algorithms on the FPGA, whose aim is to detect potential falls and their level of confidence, delivering an alarm information to the gateway by means of the ZigBee transceiver. Several sessions of data collection with real actors have been performed to define and characterize fall detection algorithms. Experimental results show that the accelerometer is a fundamental component of a multi-sensor network system and on this basis a custom device has been realized.

Inexpensive and Power-Efficient Wireless Health Monitoring System for the Aging Population

“…It is noticeable that in the pstudies of falls and ADLs evaluated above, all used an analog accelerometer except for [5,11]. However, [5,11] used it in a way no different than analog accelerometers, i.e., raw accelerometer data was simply read and streamed to a remote base unit for processing.…”

“…In studies of falls or ADLs evaluated above, except for a few which chose to store data on an SD card [17,10,12] for off-line processing, all took a remote processing approach to stream raw accelerometer data wirelessly to a remote unit via either Blue-tooth [8,18,9,25] or ZigBee [5,11,19]. This approach is acceptable and usually the most convenient for research studies.…”

“…However, [5,11] used it in a way no different than analog accelerometers, i.e., raw accelerometer data was simply read and streamed to a remote base unit for processing.…”

“…Some [5,6,7,8,9,10,11] used a single triaxial accelerometer attached to torso (e.g. waist, chest or pelvic).…”

“…[14] and [15] separately implemented fall detection algorithms right within Android phones, taking advantage of their built-in accelerometers and high-level APIs (application programming interface). [5,16,6,7,17,8,18,10,11] used a single triaxial accelerometer attached to certain parts of the torso (waist, chest, back or pelvic) to study falls. Some used more than one accelerometer.…”