A Systematic Review of Artificial Neural Networks in Medical Science and Applications

Al-Salman, Omar; Mustafina, Jamila; Shahoodh, Gailan

doi:10.1109/dese51703.2020.9450245

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…It uses Gibbs sampling during this process. Differently from the Feed Forward Neural Networks [36], the RBM iterates over the layers, activating and deactivating the units until the visible layer represents a set that satisfies the previous distribution data. This characteristic demands high-quality input data during the training process, otherwise the weights would "learn" bad patterns and generate undesired visible units' activation.…”

Section: Restricted Boltzmann Machinesmentioning

confidence: 99%

An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines

Alencar,

Barreto,

Souto

et al. 2023

JSAN

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Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data.

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Section: Restricted Boltzmann Machinesmentioning

confidence: 99%