ArticleSensors (Basel, Switzerland)2015
A wavelet-based approach to fall detection.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evaluating the Effect of Activity and Environment on Fall Risk in a Paradigm-Depending Laboratory Setting: Protocol for an Experimental Pilot Study.JMIR research protocols · 2023Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Feasibility of Using Floor Vibration to Detect Human Falls.International journal of environmental research and public health · 2020Article
- Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.Sensors (Basel, Switzerland) · 2020Article
- Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements.Sensors (Basel, Switzerland) · 2020Article
- Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study.Journal of medical Internet research · 2020Article
- An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment.Sensors (Basel, Switzerland) · 2019Article
- Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review.Sensors (Basel, Switzerland) · 2018Article
- The Relationship Between Physical Activity and Frailty Among U.S. Older Adults Based on Hourly Accelerometry Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2018Article
- [Prevention of falls and fall-related injuries : Personal balance and future tasks].Zeitschrift fur Gerontologie und Geriatrie · 2017Review
- Combining novelty detectors to improve accelerometer-based fall detection.Medical & biological engineering & computing · 2017Article
- A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry.Sensors (Basel, Switzerland) · 2017Review
- Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.Sensors (Basel, Switzerland) · 2017Article
- A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features.Computational intelligence and neuroscience · 2017Article
- Assistant Personal Robot (APR): Conception and Application of a Tele-Operated Assisted Living Robot.Sensors (Basel, Switzerland) · 2016Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the "prototype fall".In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.