Fall detection using multimodal data

• URL: http://arxiv.org/abs/2205.05918v1
• Date: Thu, 12 May 2022 07:13:34 GMT
• Title: Fall detection using multimodal data
• Authors: Thao V. Ha, Hoang Nguyen, Son T. Huynh, Trung T. Nguyen, Binh T. Nguyen
• Abstract summary: This paper studies the fall detection problem based on a large public dataset, namely the UP-Fall Detection dataset. We propose several techniques to obtain valuable features from these sensors and cameras and then construct suitable models for the main problem.
• Score: 1.8149327897427234
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, the occurrence of falls has increased and has had detrimental effects on older adults. Therefore, various machine learning approaches and datasets have been introduced to construct an efficient fall detection algorithm for the social community. This paper studies the fall detection problem based on a large public dataset, namely the UP-Fall Detection Dataset. This dataset was collected from a dozen of volunteers using different sensors and two cameras. We propose several techniques to obtain valuable features from these sensors and cameras and then construct suitable models for the main problem. The experimental results show that our proposed methods can bypass the state-of-the-art methods on this dataset in terms of accuracy, precision, recall, and F1 score.

Related papers

• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Physics Sensor Based Deep Learning Fall Detection System [0.9128828609564524]
  We build a complete system named TSFallDetect including data receiving device based on embedded sensor. We exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors.
  arXiv  Detail & Related papers  (2024-02-29T07:50:06Z)
• Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data [1.9731252964716424]
  We employ a feature selection process to identify the most relevant features from the multisensor UP-FALL dataset. We then evaluate the efficiency of various machine learning models in detecting the impact moment. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks.
  arXiv  Detail & Related papers  (2023-12-21T01:05:44Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Human Fall Detection- Multimodality Approach [2.7215474244966296]
  We use wrist sensor with acclerometer data keeping labels to binary classification, namely fall and no fall from the data set. The experimental results shows that using only wrist data as compared to multi sensor for binary classification did not impact the model prediction performance for fall detection.
  arXiv  Detail & Related papers  (2023-02-01T04:05:14Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Detecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System [0.8021979227281782]
  Missing values, widely called as textitsparsity in literature, is a common characteristic of many real-world datasets. We study different patterns of missing values, various statistical and ML based data imputation methods for different kinds of sparsity. We then select the best concept drift detector given a dataset with missing values based on the different metrics.
  arXiv  Detail & Related papers  (2022-07-27T04:27:49Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals [10.866594993485226]
  We propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M) We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bigressive LSTM with Attention) to capture temporal dependence from time-series data.
  arXiv  Detail & Related papers  (2021-07-27T06:48:20Z)
• Pretrained equivariant features improve unsupervised landmark discovery [69.02115180674885]
  We formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features. Our method produces state-of-the-art results in several challenging landmark detection datasets.
  arXiv  Detail & Related papers  (2021-04-07T05:42:11Z)
• DecAug: Augmenting HOI Detection via Decomposition [54.65572599920679]
  Current algorithms suffer from insufficient training samples and category imbalance within datasets. We propose an efficient and effective data augmentation method called DecAug for HOI detection. Experiments show that our method brings up to 3.3 mAP and 1.6 mAP improvements on V-COCO and HICODET dataset.
  arXiv  Detail & Related papers  (2020-10-02T13:59:05Z)

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.