Convolutional Neural Network for Elderly Wandering Prediction in Indoor
Scenarios
- URL: http://arxiv.org/abs/2012.12987v1
- Date: Wed, 23 Dec 2020 21:27:37 GMT
- Title: Convolutional Neural Network for Elderly Wandering Prediction in Indoor
Scenarios
- Authors: Rafael F. C. Oliveira, Fabio Barreto, Raphael Abreu
- Abstract summary: This work proposes a way to detect the wandering activity of Alzheimer's patients from path data collected from non-intrusive indoor sensors around the house.
Due to the lack of adequate data, we've manually generated a dataset of 220 paths using our own developed application.
Wandering patterns in the literature are normally identified by visual features (such as loops or random movement)
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This work proposes a way to detect the wandering activity of Alzheimer's
patients from path data collected from non-intrusive indoor sensors around the
house. Due to the lack of adequate data, we've manually generated a dataset of
220 paths using our own developed application. Wandering patterns in the
literature are normally identified by visual features (such as loops or random
movement), thus our dataset was transformed into images and augmented.
Convolutional layers were used on the neural network model since they tend to
have good results finding patterns, especially on images. The Convolutional
Neural Network model was trained with the generated data and achieved an f1
score (relation between precision and recall) of 75%, recall of 60%, and
precision of 100% on our 10 sample validation slice
Related papers
- Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection [0.9895793818721335]
This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier.
arXiv Detail & Related papers (2024-05-27T07:49:30Z) - On Input Formats for Radar Micro-Doppler Signature Processing by Convolutional Neural Networks [1.2499537119440245]
The utility of the phase information, as well as the optimal format of the Doppler-time input for a convolutional neural network is analysed.
It is found that the performance achieved by convolutional neural network classifiers is heavily influenced by the type of input representation.
arXiv Detail & Related papers (2024-04-12T07:30:08Z) - Neural Priming for Sample-Efficient Adaptation [92.14357804106787]
We propose Neural Priming, a technique for adapting large pretrained models to distribution shifts and downstream tasks.
Neural Priming can be performed at test time, even for pretraining as large as LAION-2B.
arXiv Detail & Related papers (2023-06-16T21:53:16Z) - Post-training Model Quantization Using GANs for Synthetic Data
Generation [57.40733249681334]
We investigate the use of synthetic data as a substitute for the calibration with real data for the quantization method.
We compare the performance of models quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN, with quantization using real data and an alternative data generation method based on fractal images.
arXiv Detail & Related papers (2023-05-10T11:10:09Z) - Convolutional Neural Networks for the classification of glitches in
gravitational-wave data streams [52.77024349608834]
We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors.
We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset.
We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
arXiv Detail & Related papers (2023-03-24T11:12:37Z) - Supervised Training of Siamese Spiking Neural Networks with Earth's
Mover Distance [4.047840018793636]
This study adapts the highly-versatile siamese neural network model to the event data domain.
We introduce a supervised training framework for optimizing Earth's Mover Distance between spike trains with spiking neural networks (SNN)
arXiv Detail & Related papers (2022-02-20T00:27:57Z) - Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs.
By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
arXiv Detail & Related papers (2022-02-01T17:11:13Z) - Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
We propose a self-supervised approach for pre-training deep neural networks in remote sensing.
By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner.
We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
arXiv Detail & Related papers (2021-08-02T07:50:50Z) - Application of Facial Recognition using Convolutional Neural Networks
for Entry Access Control [0.0]
The paper focuses on solving the supervised classification problem of taking images of people as input and classifying the person in the image as one of the authors or not.
Two approaches are proposed: (1) building and training a neural network called WoodNet from scratch and (2) leveraging transfer learning by utilizing a network pre-trained on the ImageNet database.
The results are two models classifying the individuals in the dataset with high accuracy, achieving over 99% accuracy on held-out test data.
arXiv Detail & Related papers (2020-11-23T07:55:24Z) - Automatic Recall Machines: Internal Replay, Continual Learning and the
Brain [104.38824285741248]
Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.
We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective.
Instead the implicit memory of learned samples within the assessed model itself is exploited.
arXiv Detail & Related papers (2020-06-22T15:07:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.