Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data

• URL: http://arxiv.org/abs/2401.05407v1
• Date: Thu, 21 Dec 2023 01:05:44 GMT
• Title: Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data
• Authors: Tresor Y. Koffi, Youssef Mourchid, Mohammed Hindawi and Yohan Dupuis
• Abstract summary: 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.
• Score: 1.9731252964716424
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate noise and improve data quality. Furthermore, we employ a feature selection process to identify the most relevant features derived from the multisensor UP-FALL dataset, which in turn will enhance the performance and efficiency of machine learning models. We then evaluate the efficiency of various machine learning models in detecting the impact moment using the resulting data information from multiple sensors. Through extensive experimentation, we assess the accuracy of our approach using various evaluation metrics. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks. This highlights the potential of our approach to enhance fall detection systems and improve the overall safety and well-being of individuals at risk of falls.

Related papers

• Leveraging Mixture of Experts for Improved Speech Deepfake Detection [53.69740463004446]
  Speech deepfakes pose a significant threat to personal security and content authenticity. We introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture.
  arXiv  Detail & Related papers  (2024-09-24T13:24:03Z)
• Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes [70.66864668709677]
  We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures.
  arXiv  Detail & Related papers  (2024-07-13T01:41:12Z)
• AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification [0.0]
  This research focuses on harmonizing information through wireless communication and identifying individuals in NLOS scenarios using ultra-wideband radar signals. Experiments demonstrate categorization accuracies of 88.37% for static data and 87.20% for dynamic data, highlighting the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-02-27T11:42:26Z)
• A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition [63.26015736148707]
  This paper introduces a novel methodology to resolve the issue of optimal sensor placement for Human Activity Recognition. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach.
  arXiv  Detail & Related papers  (2023-07-06T10:38:14Z)
• Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy [6.739132519488627]
  We introduce a physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%.
  arXiv  Detail & Related papers  (2023-03-02T03:16:21Z)
• 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)
• Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets [6.114996271792091]
  In many cases, the goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to reduce operational cost and eliminate downtime. This often boils down to detecting anomalies within the sensor date acquired from the system. The smart manufacturing application domain poses certain salient technical challenges. We show that predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2022-06-13T17:51:24Z)
• Fall detection using multimodal data [1.8149327897427234]
  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.
  arXiv  Detail & Related papers  (2022-05-12T07:13:34Z)
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets [0.7388859384645262]
  We study the problem of predicting the presence of transient noise artifacts in a gravitational wave detector. We introduce models that reduce the error rate by over 60% compared to the previous state of the art.
  arXiv  Detail & Related papers  (2022-02-27T23:41:23Z)
• DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series [88.12892448747291]
  We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.
  arXiv  Detail & Related papers  (2021-09-21T10:38:26Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)

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.