Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection

Abstract

During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient’s mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture.

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

“…The present study will extend a previous work [ 11 ] to systematically evaluate the application of different learning machine strategies when they are applied to the traces captured by a hybrid multisensory FDS architecture (consisting of a set of sensing motes and a smartphone). The study tries to optimize the election of the acceleration statistics that characterize the body mobility as well as to identify the sensor placement combinations that produce the best detection performance.…”

“…However, it has been have shown [ 11 ] that the position of the sensor that monitors the movements of the subject is crucial for the effectiveness of the FDS. In this regard, recommended locations such as the chest or waist are not comfortable positions to place a smartphone (which is conventionally transported in a hand-bag or in a loose pocket, where the phone may exhibit a certain freedom of movements that can affect the representativeness of the mobility measurements provided by its sensors).…”

Vision-Based Human Action Classification Using Adaptive Boosting Algorithm

“…We tested the effectiveness of our approach by using publicly available benchmarks: the University of Rzeszow's fall detection dataset (URFD) [37] and UMAFD dataset [26]. The URFD dataset consists of 70 videos (30 sequences correspond to fall incidents and 40 sequences represent daily gestures).…”

“…The choice of the AdaBoost algorithm is mainly motivated by its capacity to exploit many relatively weak classifiers to resolve complex recognition problems [24]. We tested the proposed approach using two experimental available datasets, the UR Fall Detection [25] and the Universidad de Málaga fall detection (UMAFD) datasets [26]. We considered six classes of activities namely: walking, standing, bending, lying, squatting, and sitting.…”

A Framework for Activity Monitoring and Fall Detection Based on the Characteristics of Indoor Channels

“…Note that the dataset in [10] includes only ADL activities, not fall related data. Public databases containing acceleration data for falls can be found in [11]- [14]. In [11], the authors present a dataset for mimicked falls.…”