Abstract
Pain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain.
Highlights
In recent years, the understanding of the role of the brain in pain processing has increased due to non-invasive brain imaging methodologies
We evaluated the subjective ratings of the pain level during the study period, in which around 10–24 measurements were taken by every patient at home
This paper presented a novel protocol for low back pain estimation based on the data collected from both hospital and home measurements, casting light on the potential of pain monitoring in natural scenarios
Summary
The understanding of the role of the brain in pain processing has increased due to non-invasive brain imaging methodologies. Pain monitoring of patients in intensive care (ICU), or home care is mainly a manual check by a clinician to make any required adjustments to medication or treatment [10]. This is a huge workload for clinicians and the consistency and reliability are cannot be guaranteed. Determining the mechanism of pain and designing a system that could automatically monitor pain to reduce the heavy workload of clinicians is paramount, to provide them with a point of reference for accurate treatment, and to further improve people’s quality of life. One of the golden standards for assessing or monitoring pain is through self-reporting, when a patient is asked the intensity of pain using a 10-cm visual analog scale (VAS)
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