Background and Objective. The most frequently used methods for assessing pain are self-reports and observation. However, physiological methods could improve accuracy and reliability for those with communicative difficulties. This review’s objective is to analyze methods used to physiologically assess pain, to rank them by invasiveness per method and vulnerability per subject group, and to assess their technological maturity. Databases and Data Treatment. Six international databases were searched for review papers between 2007 and 2019. Inclusion criteria were as follows: at least one physiological method for acute or chronic pain in humans; languages were as follows: English, French, Dutch, German, and Spanish. Quality of reviews was assessed using the CASP checklist. Results. The methods’ heart rate variability and electroencephalogram show clear and consistent results as acute pain assessment. Magnetic resonance imaging can measure chronic pain. Ordered by invasiveness and vulnerability, a trend shows that the invasive methods are used more with less vulnerable subjects. Only instruments used for skin conductance and automatic facial recognition have a lower-than-average technological maturity. Conclusions. Some pain assessment methods show good and consistent results and have high technological maturity; however, using them as pain assessment for persons with ID is uncommon. Since this addition can ameliorate caregiving, more research of assessment methods should occur.

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An effective assisting tool for caregivers to monitor pain of persons with severe intellectual disabilities (SID) is eagerly needed, since these persons have difficulties with self-report. The Bio response System detecting stress and the techniques with potential to distinguish pain from stress suggest the possibility to detect pain with physiological data. In the current paper, we propose the design of a peripheral display for making caregivers aware of the real-time pain condition of their clients without added attention burden. An iterative user-centered design process resulted in two prototypes and corresponding evaluations of one peripheral display. The potential of the display to help caregivers be promptly aware of the pain condition of clients was tested with targeted users. Guidelines for the design of peripheral display applications in similar semihospitalized contexts are provided. Further steps in this study will be to test the adjusted Bio response System for detecting pain in persons with SID adequately. CCS CONCEPTS• Human-centered computing; • Visulization; • Visualization design and evaluation methods;

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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.