While there is growing interest in developing technology to support pain assessment, pain-related self-management, and healthcare personalisation, there are currently no datasets on nonverbal pain behaviour in the context of functional activities.To address this gap, we introduce the EmoPain(at)Home dataset which consists of motion capture data and self-reported pain, worry, and confidence intensities captured from people with chronic pain. The data were recorded during self-selected functional activities in the home, e.g. vacuuming. We include analysis of the dataset as well as baseline classification of pain levels with average F1 score of 0.61 for two classes. We additionally discuss inclusivity considerations for capture of datasets in naturalistic settings, based on lessons learnt within our study.

Pain is a ubiquitous and multifaceted experience, making the gathering of ground truth for training machine learning system particularly difficult. In this paper, we reflect on the use of voice-based Experience Sampling Method (ESM) approaches for collecting pain self-reports in two different real-life case studies: long-distance runners, and people living with chronic pain performing housework activities. We report on the reflections emerging from these two qualitative studies in which semi-structured interviews were used to exploratively gather initial insights on how voice-based ESM could affect the collection of self-reports as ground truth.While frequent ESM questions may be considered intrusive, most of our participants found them useful, and even welcomed those question prompts. Particularly, they found that such voice-based questions facilitated in-the-moment self-reflection, and stimulated a sense of companionship leading to richer self-reporting, and possibly more reliable ground truth. We will discuss the ways in which participants benefitted from subjective self-reporting leading to an increased awareness and self-understanding. In addition, we make the case for the possibility of building a chatbot with ESM capabilities in order to gather more enhanced, refined but structured ground truth that combines pain ratings and their qualification. Such rich ground truth can provide could be seen as more reliable, as well as contributing to more refined machine learning models able to better capture the complexity of pain experience. CCS CONCEPTS • Human-centered computing • Human computer interaction (HCI) • Empirical studies in HCI