Relationship Between Efficiency, Effectiveness, and Learnability of Home Connected Medical Device in Ambulatory Surgery

Chaniaud, Noémie; Sagnier, Camille; Mégalakaki, Olga; Loup-Escande, Émilie

doi:10.1089/tmj.2021.0233

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…In the long term, it will be valuable to further develop an explicit agenda challenging designers to make sensors easily usable by a broader range of potential users. Learnability has consistently been highlighted across population groups as an important factor of use of relevant systems [60], [61]. Failing to drive engagement with such usability issues will limit the scope and utility of affect-aware systems in the long term.…”

Section: Discussionmentioning

confidence: 99%

The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes

Olugbade,

Buono,

Potapov

et al. 2024

IEEE Trans. Affective Comput.

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Chronic pain is a prevalent condition where fear of movement and pain interferes with everyday functioning. Yet, there is no open body movement dataset for people with chronic pain in everyday settings. Our EmoPain@Home dataset addresses this with capture from people with and without chronic pain in their homes, while they performed their routine activities. The data includes labels for pain, worry, and movement confidence continuously recorded for activity instances for the people with chronic pain. We explored two-level pain detection based on this dataset and obtained 0.62 mean F1 score. However, extension of the dataset led to deterioration in performance confirming high variability in pain expressions for real world settings. We investigated activity recognition for this setting as a first step in exploring the use of the activity label as contextual information for improving pain level classification performance. We obtained mean F1 score of 0.43 for 9 activity types, highlighting its feasibility. Further exploration, however, showed that data from healthy people cannot be easily leveraged for improving performance because worry and low confidence alter activity strategies for people with chronic pain. Our dataset and findings lay critical groundwork for automatic assessment of pain experience and behaviour in the wild.

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Section: Discussionmentioning

confidence: 99%

The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes

Olugbade,

Buono,

Potapov

et al. 2024

IEEE Trans. Affective Comput.

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Impact of the ambulatory surgery system on the usability of a home medical device for post-operative follow-up: a mixed-method study in simulation

Chaniaud,

Loup-Escande

2024

Theoretical Issues in Ergonomics Science

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The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes

Olugbade¹,

Buono²,

Potapov³

et al. 2023

Preprint

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<p>Chronic pain is a prevalent condition where fear of movement and pain interferes with everyday functioning. Yet, there is no open body movement dataset for people with chronic pain in everyday settings. Our EmoPain@Home dataset addresses this with capture from people with and without chronic pain in their homes, while they performed their routine activities. The data includes labels for pain, worry, and movement confidence continuously recorded for activity instances for the people with chronic pain. We explored two-level pain detection based on this dataset and obtained 0.62 mean F1 score. However, extension of the dataset led to deterioration in performance confirming high variability in pain expressions for real world settings. We investigated activity recognition for this setting as a first step in exploring the use of the activity label as contextual information for improving pain level classification performance. We obtained mean F1 score of 0.43 for 9 activity types, highlighting its feasibility. Further exploration, however, showed that data from healthy people cannot be easily leveraged for improving performance because worry and low confidence alter activity strategies for people with chronic pain. Our dataset and findings lay critical groundwork for automatic assessment of pain experience and behaviour in the wild. </p>

show abstract

Relationship Between Efficiency, Effectiveness, and Learnability of Home Connected Medical Device in Ambulatory Surgery

Cited by 4 publications

References 23 publications

The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes

The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes

Impact of the ambulatory surgery system on the usability of a home medical device for post-operative follow-up: a mixed-method study in simulation

The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes

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