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An explainable artificial intelligence driven fall system for sensor data analysis enhanced by butterworth filtering.

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Title: An explainable artificial intelligence driven fall system for sensor data analysis enhanced by butterworth filtering.
Authors: J., Shalini1 (AUTHOR), L., Ashok Kumar1 (AUTHOR)
Source: Engineering Applications of Artificial Intelligence. Oct2025:Part B, Vol. 158, pN.PAG-N.PAG. 1p.
Subjects: Artificial intelligence, Butterworth filters (Signal processing), Recurrent neural networks, Intelligent sensors, Long short-term memory, Patient monitoring
Abstract: The detection of falls is an essential component of healthcare monitoring systems, especially for older people at a greater risk of falling than younger people. To address the shortcomings of previously established methodologies, this research proposes a unique Artificial Intelligence driven sensor-based methodology that utilizes the SisFall dataset in conjunction with a Recurrent Neural Network - Long Short-Term Memory model. Two methods were considered: one using a Butterworth filter and the other without filtering. The results emphasize the significance of noise reduction in enhancing model performance. Additionally, the integration of Explainable Artificial Intelligence techniques brings transparency and interpretability to the model's predictions, enhancing its dependability and trustworthiness in healthcare applications. Using Artificial Intelligence driven fall detection with Explainable Artificial Intelligence for transparent decision-making, this methodology presents a robust approach to improving accuracy and reducing false alarms in real-world healthcare settings. The study demonstrates that combining advanced filtering techniques with Explainable Artificial Intelligence algorithms successfully overcomes the challenges associated with traditional fall detection systems. The findings further confirm that the application of an Artificial Intelligence based Butterworth filter significantly enhances model accuracy, achieving 98.96% compared to 79.77% without filtering. These findings highlight the potential of Artificial Intelligence driven fall detection systems in healthcare, paving the way for more accurate, interpretable, and reliable monitoring solutions that can enhance elderly safety and improve real-time clinical decision-making. [Display omitted] • Developed a sensor-based fall detection system using RNN and LSTM for real-time alerts. • Used Butterworth filtering to improve sensor data accuracy with minimal training. • Applied SHAP and LIME for explainable AI to enhance model transparency. • Compared with existing methods, showing improved accuracy and efficiency. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 187817880
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: An explainable artificial intelligence driven fall system for sensor data analysis enhanced by butterworth filtering.
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  Data: <searchLink fieldCode="AR" term="%22J%2E%2C+Shalini%22">J., Shalini</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22L%2E%2C+Ashok+Kumar%22">L., Ashok Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Oct2025:Part B, Vol. 158, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Butterworth+filters+%28Signal+processing%29%22">Butterworth filters (Signal processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+sensors%22">Intelligent sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+monitoring%22">Patient monitoring</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The detection of falls is an essential component of healthcare monitoring systems, especially for older people at a greater risk of falling than younger people. To address the shortcomings of previously established methodologies, this research proposes a unique Artificial Intelligence driven sensor-based methodology that utilizes the SisFall dataset in conjunction with a Recurrent Neural Network - Long Short-Term Memory model. Two methods were considered: one using a Butterworth filter and the other without filtering. The results emphasize the significance of noise reduction in enhancing model performance. Additionally, the integration of Explainable Artificial Intelligence techniques brings transparency and interpretability to the model's predictions, enhancing its dependability and trustworthiness in healthcare applications. Using Artificial Intelligence driven fall detection with Explainable Artificial Intelligence for transparent decision-making, this methodology presents a robust approach to improving accuracy and reducing false alarms in real-world healthcare settings. The study demonstrates that combining advanced filtering techniques with Explainable Artificial Intelligence algorithms successfully overcomes the challenges associated with traditional fall detection systems. The findings further confirm that the application of an Artificial Intelligence based Butterworth filter significantly enhances model accuracy, achieving 98.96% compared to 79.77% without filtering. These findings highlight the potential of Artificial Intelligence driven fall detection systems in healthcare, paving the way for more accurate, interpretable, and reliable monitoring solutions that can enhance elderly safety and improve real-time clinical decision-making. [Display omitted] • Developed a sensor-based fall detection system using RNN and LSTM for real-time alerts. • Used Butterworth filtering to improve sensor data accuracy with minimal training. • Applied SHAP and LIME for explainable AI to enhance model transparency. • Compared with existing methods, showing improved accuracy and efficiency. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.engappai.2025.111364
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      – Code: eng
        Text: English
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        PageCount: 1
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Butterworth filters (Signal processing)
        Type: general
      – SubjectFull: Recurrent neural networks
        Type: general
      – SubjectFull: Intelligent sensors
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      – SubjectFull: Long short-term memory
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      – SubjectFull: Patient monitoring
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            NameFull: J., Shalini
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            NameFull: L., Ashok Kumar
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              Text: Oct2025:Part B
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              Y: 2025
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              Value: 158
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