ArticleSensors (Basel, Switzerland)2021
A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.IEEE access : practical innovations, open solutions · 2026Article
- Article
- Effect of a Six-Month Dance Intervention on Postural Control and Fall-Related Outcomes in Older Adults with Mild Cognitive Impairment: A Randomized Controlled Trial.Geriatrics (Basel, Switzerland) · 2025Article
- PainSeeker: a head pose-invariant deep learning method for assessing rat's pain by facial expressions.Frontiers in veterinary science · 2025Article
- A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning.Sensors (Basel, Switzerland) · 2023Article
- Falls caused by balance disorders in the elderly with multiple systems involved: Pathogenic mechanisms and treatment strategies.Frontiers in neurology · 2023Review
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Authors and funding
3 authors.
Funding
Abstract
Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.
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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.