A hardware-software framework for high-reliability people fall detection

Abstract

This paper presents a hardware and software framework for reliable fall detection in the home environment, with particular focus on the protection and assistance to the elderly. The integrated prototype includes three different sensors: a 3D time-of-flight range camera, a wearable MEMS accelerometer and a microphone. These devices are connected with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each of the three sensors, an optimized algorithm for fall-detection has been developed and benchmarked on a collected mulitimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.

Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector

“…The implemented OC-SVM shows improvements in the specificity and sensitivity with respect to a threshold-based approach, and this can be verified in comparison with both the frameworks presented in [17,28], choosing the algorithms where the same parameters of fall are used (the impact detection and posture monitoring). For the comparison of the fall detection algorithms were used the same hardware (shown in Figure 1), benchmark dataset and training/test sets described above.…”

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

“…By exploiting the above mentioned routines different thresholds have been evaluated using data from several sessions for training. After that, the performance of the algorithm has been evaluated on the remaining collected data and the results are shown in Table III [6]. An example of a plot of the acceleration along one axis (Y) has been reported in Fig.…”

“…For these reasons, in the last few years, the use of portable devices in the health monitoring of chronic patients has considerably increased. Furthermore, in order to prevent false or missed alarms, which in our work basically refer to falls, the use of multi-sensor systems is mandatory and the wearable accelerometer has the fundamental role to validate system decisions [6]. In particular, always regarding fall detection, with respect to vision or acoustic sensors, the accelerometer module has the advantage of not having to be set up and installed in all rooms of the AAL house, as it is instead required for instance for 3D video trackers or acoustic scene analyzers.…”

Fall Detection and Prevention for the Elderly: A Review of Trends and Challenges

“…Algorithms for fall detection for several environments and the subject's physical condition were rather troublesome; however, a combination of movement sensors and signal-processing technologies can provide more accurate and precise fall detection and prevention approaches. Data fusion based on multi-sensing technology [59][60][61] offers many challenges for providing more accurate approaches for fall detection and prevention. Multi-sensor data fusion is the area focusing on creating multi-modal systems, which receive data from several providers and perform correlation or fusion upon it in order to increase the accuracy and reliability of the proposed systems.…”