Fall Detection Based on Wearable Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: 25 December 2025 | Viewed by 1720
Special Issue Editor
2. Institute for Systems and Robotics, LARSyS, Torre Norte Piso 7, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Interests: wearables; fall detection; data augmentation; robot; 3D Vision; tactile sensing
Special Issue Information
Dear Colleagues,
Fall detection in the senior population has the potential of reducing healthcare costs, as well as improving the impact on the lives of every senior person. However, data on the senior population during falls are scarce or not possible to gather, so current models for fall detection are not accurate for their application. This Special Issue aims to gather ideas and methods that generate accurate values for wearable sensors, as well as other aspects of fall detection. These accurate samples should be as close as possible to wearable device data. Thus, this Special Issue calls for works that (i) generate plausible data of senior people falling from simulations, (ii) generate samples that follow the trajectory patterns of the senior population, and (iii) create mappings that generate data of senior falls from actual data of healthy participants (either young or senior). This topic fits into the scope of Sensors due to the sensor modeling and data analysis. The topics of interest are as follows:
- Data generation from simulation;
- Transfer learning;
- Data augmentation from fall videos;
- Motion capture;
- Fall detection.
Dr. Plinio Moreno
Guest Editor
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Keywords
- data generation from simulation
- transfer learning
- data augmentation from fall videos
- motion capture
- fall detection
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