ArticleSensors (Basel, Switzerland)2021
NT-FDS-A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices.
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 11 papers.
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Who cites it
11 citing papers in PubMed, 57 citations in OpenAlex.
- A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.IEEE access : practical innovations, open solutions · 2026Article
- Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons.Smart health (Amsterdam, Netherlands) · 2024Article
- Article
- A robust multimodal detection system: physical exercise monitoring in long-term care environments.Frontiers in bioengineering and biotechnology · 2024Article
- Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method.Sensors (Basel, Switzerland) · 2023Article
- Microwave Devices for Wearable Sensors and IoT.Sensors (Basel, Switzerland) · 2023Review
- IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety.Frontiers in bioengineering and biotechnology · 2023Article
- A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Smart Wearables with Sensor Fusion for Fall Detection in Firefighting.Sensors (Basel, Switzerland) · 2021Article
- A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling.Sensors (Basel, Switzerland) · 2021Article
- A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets-SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.
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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.