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.
Highlights
The definition of reliable, automatic and cost effective Fall Detection Systems (FDSs), which can profit from the latest advances in the field of electronics, signal processing and wireless communications, has become a popular research topic during the last decade
Last decade has witnessed a considerable number of research efforts that propose to utilize smartphones as fall detectors, benefiting from the low cost, the communication interfaces and the computing and sensing capabilities which are provided by these personal daily life devices
In order to attain an accurate characterization of the user mobility, the use of a smartphone as a wearable fall detection sensor obliges to transport it in an unnatural way
Summary
The definition of reliable, automatic and cost effective Fall Detection Systems (FDSs), which can profit from the latest advances in the field of electronics, signal processing and wireless communications, has become a popular research topic during the last decade.Falls are a significant cause of morbidity and mortality among the people over age 64 [1], as well as an important source of expenditure for the health system in Western countries [2], with medical costs that are projected to reach $67.7 billion by 2020 only in the USA [3]. The definition of reliable, automatic and cost effective Fall Detection Systems (FDSs), which can profit from the latest advances in the field of electronics, signal processing and wireless communications, has become a popular research topic during the last decade. Wearable FDSs can be considered a sub-type of Body Area Networks (BANs). While scalability and Quality of Service (QoS) may be of major importance for wide area sensor networks (such as those deployed for smart cities [5]) and Vehicular Cloud Services [6], energy-limited wireless backbones [7] or data-centers for Big Data Stream Mobile Computing [8], human factors and ergonomics are essential issues for an adequate design of a BAN
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