Sampling Frequency Evaluation on Recurrent Neural Networks Architectures for IoT Real-time Fall Detection Devices

Luna-Perejón, Francisco; Civit-Masot, Javier; Muñoz-Saavedra, Luis; Durán-López, Lourdes; Amaya-Rodriguez, Isabel; Domínguez-Morales, Juan Pedro; Vicente-Díaz, Saturnino; Linares-Barranco, A.; Balcells, A; Domínguez-Morales, Manuel

doi:10.5220/0008494805360541

Cited by 4 publications

(4 citation statements)

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“…A recent study [34] revealed that this smooths the objective function to improve the performance. A 10-fold cross validation study [35] determined that this was not effective for obtaining non-sequential characteristics. Based on these results, in this work we deepened our study and analyzed the feasibility of integration for four different architectures.…”

Section: Methodsmentioning

confidence: 99%

“…The users for each subset were randomly chosen, but maintaining an equitable distribution between adults and elderly. The training subset were the used in [35] applying 10-fold cross validation and estimate the goodness of the models with a correct reliability. In a first stage, five training processes for each architecture with different sampling frequencies were performed, in order to determine those with the best performance.…”

Section: Methodsmentioning

confidence: 99%

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Wearable Fall Detector Using Recurrent Neural Networks

Luna-Perejón

Domínguez-Morales

Balcells

2019

Sensors

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Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Wearable Fall Detector Using Recurrent Neural Networks

Luna-Perejón

Domínguez-Morales

Balcells

2019

Sensors

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“…On the contrary, authors in [86] experiments carried out on the SmartFall dataset. The experimental results show that the RF algorithm outperforms a single deep LSTM model and other different ensemble techniques.…”

Section: Machine Learning Vs Deep Learningmentioning

confidence: 99%

“…A work by [86] wanted to predict pre-fall situations by proposing stacking-based ensemble learning of DL models namely LSTM with various configurations. They also included conventional ML algorithms to predict the risk of falling in the elderly.…”

Section: B Performance Evaluation and Metricsmentioning

confidence: 99%

Fall Detection System using Wearable Sensor Devices and Machine Learning: A Review

Amir,

Dziyauddin,

Mohamed

et al. 2024

Preprint

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Over the past years, Fall Detection System (FDS) has undergone extensive research to improve living risk, especially for the elderly who are vulnerable to these fall events. Devices employing sensors are crucial components of FDS in achieving high accuracy and sensitivity. This article overviews different sensor modalities, such as ambient-based and vision-based systems, as well as commonly used wearable devices for fall detection, along with the associated data processing algorithms. The critical elements of fall detection, such as architectures and algorithms for processing sensor data, machine learning and deep learning methodologies, and validation of FDS performance, are considered. The article also delves into safety aspects and presents technical challenges and concerns in FDS for researchers in the field to identify areas requiring further improvement. Finally, future research opportunities to improve fall detection for widespread use are outlined.

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