Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography.

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.

Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement.

BACKGROUND: Assessment of pain is critical to its optimal treatment. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain is a multivalent, dynamic, and ambiguous phenomenon that is difficult to quantify, particularly when the patient's ability to communicate is limited. The criterion standard of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as electrocardiography, electromyography, photoplethysmography, and electrodermal activity. However, pain assessment by using only these signals can be unreliable, as various other factors alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. OBJECTIVE: Our objective is to further the development and research of a pain assessment tool for use with patients who are likely experiencing mild to moderate pain. We will collect observational data through wearable technologies, measuring facial electromyography, electrocardiography, photoplethysmography, and electrodermal activity. METHODS: This protocol focuses on the second phase of a larger study of multimodal signal acquisition through facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain and for building predictive models. We used state-of-the-art standard sensors to measure bioelectrical electromyographic signals and changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, we further developed the pain assessment tool and reconstituted it with modern wearable sensors, devices, and algorithms. In this second phase, we will test the smart pain assessment tool in communicative patients after elective surgery in the recovery room. RESULTS: Our human research protections application for institutional review board review was approved for this part of the study. We expect to have the pain assessment tool developed and available for further research in early 2021. Preliminary results will be ready for publication during fall 2020. CONCLUSIONS: This study will help to further the development of and research on an objective pain assessment tool for monitoring patients likely experiencing mild to moderate pain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17783.

Health and medical device development for fundamental care: Scoping review.

BACKGROUND: The use of technology and health and medical devices as a part of fundamental nursing care is increasing. Although involving users in the device development process is essential, the role of nurses in the process has not yet been discussed. OBJECTIVES: To examine and map what kind of health and medical devices have been developed specifically for fundamental nursing care and to examine the design and development of the devices, particularly focusing on the role of nurses in the process. DESIGN: Scoping review. DATA SOURCES: The Medline, Cinahl, Web of Science, IEEE Explore and ACM DL databases REVIEW METHODS: The databases were searched to identify studies describing health and medical devices developed for fundamental nursing care published between the years 2008-2018 in English language. References of included articles were reviewed for additional eligible studies. Two research team members screened the abstracts and full articles against the predefined inclusion and exclusion criteria. The PRISMA-ScR checklist was used. RESULTS: Of the 7223 reports identified, a total of 19 were chosen for the scoping review. Of these, five were further analysed regarding the development process. Main focus areas of the included reports were patient monitoring, pressure ulcer prevention and patient transfer and mobility. Device development process, divided into three phases, was mainly driven by technological expertise and healthcare personnel were mainly involved in the evaluation phases. CONCLUSIONS: Health and medical devices are a crucial part of the healthcare today and nurses are increasingly involved with their use. Most of the devices have been developed mainly by using technological expertise although they are directly aimed at fundamental aspects of nursing care. The results of our review suggest that the expertise of the nurses as the end-users of the devices could be much more exploited. RELEVANCE TO CLINICAL PRACTICE: A combination of expertise of device development from both nursing professionals and technical experts is necessary to disentangle the requirements of increased quality in nursing care combined with the ever-growing technological requirements.

Acute pain intensity monitoring with the classification of multiple physiological parameters.

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

The Internet of Things for basic nursing care-A scoping review.

BACKGROUND: The novel technology of the Internet of Things (IoT) connects objects to the Internet and its most advanced applications refine obtained data for the user. We propose that Internet of Things technology can be used to promote basic nursing care in the hospital environment by improving the quality of care and patient safety. OBJECTIVES: To introduce the concept of Internet of Things to nursing audience by exploring the state of the art of Internet of Things based technology for basic nursing care in the hospital environment. DATA SOURCES AND REVIEW METHODS: Scoping review methodology following Arksey & O'Malley's stages from one to five were used to explore the extent, range, and nature of current literature. We searched eight databases using predefined search terms. A total of 5030 retrievals were found which were screened for duplications and relevancy to the study topic. 265 papers were chosen for closer screening of the abstracts and 93 for full text evaluation. 62 papers were selected for the review. The constructs of the papers, the Internet of Things based innovations and the themes of basic nursing care in hospital environment were identified. RESULTS: Most of the papers included in the review were peer-reviewed proceedings of technological conferences or articles published in technological journals. The Internet of Things based innovations were presented in methodology papers or tested in case studies and usability assessments. Innovations were identified in several topics in four basic nursing care activities: comprehensive assessment, periodical clinical reassessment, activities of daily living and care management. CONCLUSIONS: Internet of Things technology is providing innovations for the use of basic nursing care although the innovations are emerging and still in early stages. Internet of things is yet vaguely adopted in nursing. The possibilities of the Internet of Things are not yet exploited as well as they could. Nursing science might benefit from deeper involvement in engineering research in the area of health.