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
The TAME Pain Dataset contains data collected during a study with 51 individuals. It encompasses a collection of 7,039 annotated utterances derived from 51 participants, totalling approximately 311 minutes of audio recordings. Each utterance within the dataset is labeled with a self-reported pain level on a 1-10 scale. These pain levels are further categorized into three distinct classifications: binary (No Pain vs. Pain), three-class (Mild, Moderate, Severe), and condition-based (Cold vs. Warm), facilitating diverse analytical approaches.
By making this dataset publicly available, we aim to advance AI-driven pain assessment technologies by enabling the analysis of audio features to objectively identify pain.
Pain assessment, dataset, pain level, audio files
Dao, Tu-Quyen
f18d7658-f219-49c3-abe5-a846a78b67ed
Schneiders, Eike
9da80af0-1e27-4454-90e2-eb1abf7108bd
Williams, Jennifer
3a1568b4-8a0b-41d2-8635-14fe69fbb360
Bautista, John Robert
5736fb17-b694-40ee-879b-97fd7883e1d9
Seabrooke, Tina
bf0d9ea5-8cf7-494b-9707-891762fce6c3
Vigneswaran, Ganesh
4e3865ad-1a15-4a27-b810-55348e7baceb
Kolpekwar, Rishik
bf48e3ae-7389-4fd2-8882-f47b7d036405
Vashistha, Ritwik
49ccd6f6-081f-43c1-bc64-9c59abc45c2b
Farahi, Arya
60072f79-991d-4eaf-b78e-63e2477851b0
Dao, Tu-Quyen
f18d7658-f219-49c3-abe5-a846a78b67ed
Schneiders, Eike
9da80af0-1e27-4454-90e2-eb1abf7108bd
Williams, Jennifer
3a1568b4-8a0b-41d2-8635-14fe69fbb360
Bautista, John Robert
5736fb17-b694-40ee-879b-97fd7883e1d9
Seabrooke, Tina
bf0d9ea5-8cf7-494b-9707-891762fce6c3
Vigneswaran, Ganesh
4e3865ad-1a15-4a27-b810-55348e7baceb
Kolpekwar, Rishik
bf48e3ae-7389-4fd2-8882-f47b7d036405
Vashistha, Ritwik
49ccd6f6-081f-43c1-bc64-9c59abc45c2b
Farahi, Arya
60072f79-991d-4eaf-b78e-63e2477851b0
Dao, Tu-Quyen, Schneiders, Eike, Williams, Jennifer, Bautista, John Robert, Seabrooke, Tina, Vigneswaran, Ganesh, Kolpekwar, Rishik, Vashistha, Ritwik and Farahi, Arya
(2025)
TAME Pain: Trustworthy AssessMEnt of Pain from Speech and Audio for the Empowerment of Patients.
PhysioNet
doi:10.13026/20e2-1g10
[Dataset]
Abstract
The TAME Pain Dataset contains data collected during a study with 51 individuals. It encompasses a collection of 7,039 annotated utterances derived from 51 participants, totalling approximately 311 minutes of audio recordings. Each utterance within the dataset is labeled with a self-reported pain level on a 1-10 scale. These pain levels are further categorized into three distinct classifications: binary (No Pain vs. Pain), three-class (Mild, Moderate, Severe), and condition-based (Cold vs. Warm), facilitating diverse analytical approaches.
By making this dataset publicly available, we aim to advance AI-driven pain assessment technologies by enabling the analysis of audio features to objectively identify pain.
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More information
Published date: 21 January 2025
Keywords:
Pain assessment, dataset, pain level, audio files
Identifiers
Local EPrints ID: 497539
URI: http://eprints.soton.ac.uk/id/eprint/497539
PURE UUID: 1ec67b7c-e5c9-447d-880a-6faf3a165453
Catalogue record
Date deposited: 27 Jan 2025 17:48
Last modified: 28 Jan 2025 03:15
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Contributors
Creator:
Tu-Quyen Dao
Creator:
Eike Schneiders
Creator:
Jennifer Williams
Creator:
John Robert Bautista
Creator:
Rishik Kolpekwar
Creator:
Ritwik Vashistha
Creator:
Arya Farahi
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