ArticleSensors (Basel, Switzerland)2020
Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- MadgwickFall-Net: A Lightweight Dual-Frame Feature Fusion Network for Pre-Impact Fall Detection Using Wearable IMUs.Bioengineering (Basel, Switzerland) · 2026Article
- Millimeter-wave technology for multi-person fall detection validated through wearable sensors and real-life scenarios.Scientific reports · 2026Article
- Differentiating Human Falls from Daily Activities Using Machine Learning Methods Based on Accelerometer and Altimeter Sensor Fusion Feature Engineering.Sensors (Basel, Switzerland) · 2025Article
- Article
- An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.Journal of medical Internet research · 2024Article
- A dynamic spatiotemporal model for fall warning and protection.Medical & biological engineering & computing · 2024Article
- Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites.Sensors (Basel, Switzerland) · 2022Article
- Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research.Sensors (Basel, Switzerland) · 2022Article
- The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.Sensors (Basel, Switzerland) · 2021Article
- A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.Frontiers in aging neuroscience · 2021Article
- Detection of Pre-Impact Falls from Heights Using an Inertial Measurement Unit Sensor.Sensors (Basel, Switzerland) · 2020Article
- Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user's body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.
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Registered trials
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