Abstract
This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA).
Highlights
According to some forecasts [1], it is expected that between 2015 and 2050 the over-60 years of age world population will grow from 900 to 2000 million
From the comparison of the mean performance metrics achieved for the different combinations of input features, we conclude that the best performance of the Decision Tree algorithm takes place when μSMV, Awdi f f σSMV, and μ Ap are used as input variables to characterize the mobility of the individuals
We evaluate the performance of the algorithms for all the 31 possible combinations of the five sensors of the body area network
Summary
According to some forecasts [1], it is expected that between 2015 and 2050 the over-60 years of age world population will grow from 900 to 2000 million. This dramatic demographic change will undoubtedly give rise to a series of challenges in the health systems that must be faced in order to sustain and increase the quality of life of the citizens. 28–35% of people over 65 suffer a fall each year, whereas this percentage climbs with age, reaching 32–42% for people over 70 [3]. Other population groups are exposed to endure severe falls during their work or leisure time (cyclists, mountaineers, firemen, antenna installers, cable layers, etc.)
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