ArticleSensors (Basel, Switzerland)2020
IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm.
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.
- Simulation-to-Real Trip-Fall Detection with Continuous-Wave Doppler Radar via Physics-Informed Kinematic Modeling and Domain Randomization.Sensors (Basel, Switzerland) · 2026Article
- Movement smoothness at term-equivalent age is associated with motor developmental delay in preterm infants.Scientific reports · 2025Article
- Key Fundamentals and Examples of Sensors for Human Health: Wearable, Non-Continuous, and Non-Contact Monitoring Devices.Sensors (Basel, Switzerland) · 2025Article
- An Approach to Fall Detection Using Statistical Distributions of Thermal Signatures Obtained by a Stand-Alone Low-Resolution IR Array Sensor Device.Sensors (Basel, Switzerland) · 2025Article
- Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.Sensors (Basel, Switzerland) · 2024Article
- Noncontact Sensors for Vital Signs Measurement: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2024Review
- Application of Feedforward and Recurrent Neural Networks for Fusion of Data from Radar and Depth Sensors Applied for Healthcare-Oriented Characterisation of Persons' Gait.Sensors (Basel, Switzerland) · 2023Article
- Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring.Sensors (Basel, Switzerland) · 2022Review
- Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements.Sensors (Basel, Switzerland) · 2022Article
- Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- UWB Radio-Based Motion Detection System for Assisted Living.Sensors (Basel, Switzerland) · 2021Article
- Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into "Fall" and "Activities of daily living (ADL)" while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into "Fall" and "ADL." The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.
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