An AI-enabled pain classification algorithm using skin conductance data from a smart sock achieved an accuracy of 80-93% and a precision of 0.88 after balancing the dataset with interpolated data.
Does an AI-enabled system using skin conductance sensoring in socks accurately classify pain levels in healthy adults?
An AI-enabled smart sock measuring skin conductance can detect pain with up to 93% accuracy, offering a potential non-invasive monitoring tool for noncommunicative patients.
Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.
Korving et al. (Sat,) conducted a other in Healthy adults (pain monitoring) (n=30). AI-enabled pain monitoring system using skin conductance sensoring in socks vs. Moments without pain was evaluated on Pain classification accuracy and precision. An AI-enabled pain classification algorithm using skin conductance data from a smart sock achieved an accuracy of 80-93% and a precision of 0.88 after balancing the dataset with interpolated data.
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