TAME Pain: Trustworthy AssessMEnt of Pain from speech and audio for the empowerment of patients
TAME Pain: Trustworthy AssessMEnt of Pain from speech and audio for the empowerment of patients
Precise pain assessment is crucial for medical professionals to provide appropriate treatment. However, not every patient can verbalise the experienced pain for various reasons (e.g., speech disorders or language barriers). In these cases, medical practitioners must provide treatment based on non-verbal signs. The TAME Pain project aims to provide medical professionals with an additional pain assessment tool using audio analysis. We seek to develop an algorithm that functions as an autonomous system and evaluate its trustworthiness. This proof-of-concept study will investigate whether the acoustic signal extracted from healthy individuals subjected to pain can predict pain accurately. We will assess the system’s trustworthiness by surveying medical professionals. By comparing their assessments with and without our algorithm against pain levels reported by the participants, we evaluate the trustworthiness and feasibility of clinical implementation. Our project aims at empowering patients, including non-verbal and second-language speakers. Our work also supports healthcare professionals with an accessible decision support tool, ultimately reducing the risk of potential physical harm and improving patient experience. This paper describes the planned study to collect data for the system that assesses bioacoustic pain markers in the speech signal and outlines our project plan for achieving this.
acoustic biomarkers, non-verbal pain assessment, trustworthy autonomous pain assessment
Association for Computing Machinery
Schneiders, Eike
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Williams, Jennifer
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Farahi, Arya
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Seabrooke, Tina
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Vigneswaran, Ganesh
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Bautista, John Robert
5ebb89f2-99e5-4e39-8f92-a8de6f767ae9
Dowthwaite, Liz
5dc18f65-ef15-4186-8a15-fb3f30a1a498
Piskopani, Anna-Maria
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11 July 2023
Schneiders, Eike
e96da286-6e4a-477b-be12-ae76e249151f
Williams, Jennifer
3a1568b4-8a0b-41d2-8635-14fe69fbb360
Farahi, Arya
295bd2d0-e23f-460c-9eeb-c7825b2dddd2
Seabrooke, Tina
bf0d9ea5-8cf7-494b-9707-891762fce6c3
Vigneswaran, Ganesh
4e3865ad-1a15-4a27-b810-55348e7baceb
Bautista, John Robert
5ebb89f2-99e5-4e39-8f92-a8de6f767ae9
Dowthwaite, Liz
5dc18f65-ef15-4186-8a15-fb3f30a1a498
Piskopani, Anna-Maria
01b1f906-9878-43eb-80a4-bf753ee96cdb
Schneiders, Eike, Williams, Jennifer, Farahi, Arya, Seabrooke, Tina, Vigneswaran, Ganesh, Bautista, John Robert, Dowthwaite, Liz and Piskopani, Anna-Maria
,
et al.
(2023)
TAME Pain: Trustworthy AssessMEnt of Pain from speech and audio for the empowerment of patients.
In TAS 2023 - Proceedings of the 1st International Symposium on Trustworthy Autonomous Systems: Proceedings of the First International Symposium on Trustworthy Autonomous Systems.
Association for Computing Machinery..
(doi:10.1145/3597512.3597513).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Precise pain assessment is crucial for medical professionals to provide appropriate treatment. However, not every patient can verbalise the experienced pain for various reasons (e.g., speech disorders or language barriers). In these cases, medical practitioners must provide treatment based on non-verbal signs. The TAME Pain project aims to provide medical professionals with an additional pain assessment tool using audio analysis. We seek to develop an algorithm that functions as an autonomous system and evaluate its trustworthiness. This proof-of-concept study will investigate whether the acoustic signal extracted from healthy individuals subjected to pain can predict pain accurately. We will assess the system’s trustworthiness by surveying medical professionals. By comparing their assessments with and without our algorithm against pain levels reported by the participants, we evaluate the trustworthiness and feasibility of clinical implementation. Our project aims at empowering patients, including non-verbal and second-language speakers. Our work also supports healthcare professionals with an accessible decision support tool, ultimately reducing the risk of potential physical harm and improving patient experience. This paper describes the planned study to collect data for the system that assesses bioacoustic pain markers in the speech signal and outlines our project plan for achieving this.
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More information
Published date: 11 July 2023
Additional Information:
Funding for this research was provided by:
UK Engineering and Physical Sciences Research Council (EPSRC) (EP/V00784X/1)
University of Texas at Austin (Good Systems)
Funding Information:
This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through the Trustworthy Autonomous Systems Hub (EP/V00784X/1) and by Good Systems, a research grand challenge at the University of Texas at Austin.
Publisher Copyright:
© 2023 Owner/Author.
Venue - Dates:
First International Symposium on Trustworthy Autonomous Systems: ACM, Heriot-Watt University, Edinburgh, United Kingdom, 2023-07-11 - 2023-07-12
Keywords:
acoustic biomarkers, non-verbal pain assessment, trustworthy autonomous pain assessment
Identifiers
Local EPrints ID: 479813
URI: http://eprints.soton.ac.uk/id/eprint/479813
PURE UUID: 6a275d97-1765-48e0-8e5f-897e116eec2d
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Date deposited: 27 Jul 2023 06:48
Last modified: 17 Mar 2024 04:12
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Contributors
Author:
Eike Schneiders
Author:
Jennifer Williams
Author:
Arya Farahi
Author:
John Robert Bautista
Author:
Liz Dowthwaite
Author:
Anna-Maria Piskopani
Corporate Author: et al.
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