No abstract

No abstract

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

When three-dimensional (3-D) human or animal movement is recorded using a photogrammetric system, bone-embedded frame positions and orientations are estimated from reconstructed surface marker trajectories using either nonoptimal or optimal algorithms. The effectiveness of these mathematical procedures in accommodating for both photogrammetric errors and skin movement artifacts depends on the number of markers associated with a given bone as well as on the size and shape characteristics of the relevant cluster. One objective of this paper deals with the identification of marker-cluster design criteria aimed at the minimization of error propagation from marker coordinates to bone-embedded frame position and orientation. Findings allow for the quantitative estimation of these errors for any given cluster configuration and suggest the following main design criteria. A cluster made up of four markers represents a good practical compromise. Planar clusters are acceptable, provided in quasi-isotropic distribution. The root mean square distance of the markers from their centroid should be greater than ten times the standard deviation of the marker position error. The second objective of this paper deals with the identification of the optimal cluster position and orientation on the limb aimed at the minimization of error propagation to anatomical landmark laboratory coordinates. Cluster position should be selected to minimize skin movement artifacts. The longest principal axis of the marker distribution should be oriented toward the relevant anatomical landmark position.

No abstract