Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing

Sensors · 2025-03-26 · 4 citations

Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard" for pain assessment, tools are needed to objectively monitor and account for inter-individual differences. This study introduced a novel framework to objectively classify pain intensity levels using physiological signals during Quantitative Sensory Testing sessions. Twenty-four participants participated in the study wearing physiological sensors (blood volume pulse (BVP), galvanic skin response (GSR), electromyography (EMG), respiration rate (RR), skin temperature (ST), and pupillometry). This study employed two analysis plans. Plan 1 utilized a grid search methodology with a 10-fold cross-validation framework to optimize time windows (1-5 s) and machine learning hyperparameters for pain classification tasks. The optimal time windows were identified as 3 s for the pressure session, 2 s for the pinprick session, and 1 s for the cuff session. Analysis Plan 2 implemented a leave-one-out design to evaluate the individual contribution of each sensor modality. By systematically excluding one sensor's features at a time, the performance of these sensor sets was compared to the full model using Wilcoxon signed-rank tests. BVP emerged as a critical sensor, significantly influencing performance in both pinprick and cuff sessions. Conversely, GSR, RR, and pupillometry demonstrated stimulus-specific sensitivity, significantly contributing to the cuff session but with limited influence in other sessions. EMG and ST showed minimal impact across all sessions, suggesting they are non-critical and suitable for reducing sensor redundancy. These findings advance the design of sensor configurations for personalized pain management. Future research will focus on refining sensor integration and addressing stimulus-specific physiological responses.

Beyond Pain Intensity Scores: A Systematic Review of Multimodal Digital Neurophysiological Sensors for Objective Pain Assessment in the Clinical Application

Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2025-09-01

People have been looking for an objective measure of chronic pain for decades. Current objective measures can be achieved by correlating patients’ self-report with physiological signal features, but such measures often yield inconsistent results due to individual variability. While existing approaches heavily rely on pain intensity, this study reviewed literatures in differentiating pain types and pain locations using neurophysiological sensors. Results showed that using modalities like EEG and fNIRS, high classification accuracy for specific stimuli can be achieved. However, most existing datasets lack integration of both neurological and peripheral physiological signals and do not label multimodal pain dimensions, such as type, intensity, and location. The review highlights an urgent need for datasets that combine these modalities to support objective pain assessment. Future research should also prioritize multidimensional labeling, population diversity, and validation frameworks to advance personalized pain management and rehabilitation workflows.

Impact of Visual Perception Mismatch Design on Response Time in Mixed Reality

Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2025-09-01

Mixed reality technology has provided more possibilities for users to interact with both the virtual and real worlds. By integrating virtual elements with real scenes, mixed reality offers a novel immersive experience. Mixed reality also introduces complex perceptual and cognitive challenges when processing conflicting information from the virtual and real worlds. This study investigates the impact of color mismatches on response time in a mixed reality scenario. We recruited 15 participants to identify the font color of words while ignoring their meaning. The experiment included three levels of perceptual-conceptual matching. We performed a global comparison across all conditions, a within-group comparison, and a between-group comparison. Results show that in the mixed reality scenario, the processing of visual information relies on low-level physical properties (e.g., brightness and hue) and is modulated by high-level cognitive factors (e.g., conflict processing and expectation). These findings indicate that visual mismatches in mixed reality can significantly affect response time, particularly when perceptual salience is low and different from cognitive expectations. The results have implications for the design of mixed reality interfaces in complex environments where users must process information from the virtual and real worlds, such as head-up displays, warehouse management systems, and industrial instructional overlays.

Font and background color combinations influence recognition efficiency: A novel method via primary color Euclidean distance and response surface analysis

Displays · 2024-11-12 · 1 citations

A Systematic Review and Meta-Analysis of Older Adults’ Cognitive Impairments and Effects on Road Safety

Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2024-09-01 · 2 citations

Driving is challenging for older adults, especially those with cognitive impairments. This paper systematically reviewed 39 peer-reviewed studies to examine the impact of cognitive deficits on older adults’ driving abilities, focusing on the discussion of neuropsychological assessments including Trail Making Tests, Useful Field of View, Maze Test, and Mini-Mental Status Exam. The study proposed the concept of Road Safety Cognitive Health, which encompasses the cognitive processes crucial for safe driving, aiming to inform future driver training, licensing regulations, roadway designs, and vehicle technology innovations. Concussively, this research advances understanding of road safety challenges for cognitively impaired older adults and advocates for an integrative approach to ensure their driving safety.

Using Physiological Signals for Pain Assessment: An Evaluation of Deep Learning Models

2024-10-03 · 2 citations

Pain assessment is of major significance in clinical environments. The current gold standard is self-reporting of pain based on the patient’s subjective willingness. However, pain assessment based on physiological signals is developing rapidly due to the objectivity and real-time nature of physiological signals. This study aims to systematically compare the performance of convolutional neural networks (CNN) combined with long short-term memory networks (LSTM), bidirectional long short-term memory networks (BiLSTM), transformers, and gated recurrent units (GRU) models in pain classification tasks. We assessed these hybrid models’ performance experimentally using a variety of metrics, such as accuracy, precision, recall, F1 score, training time, and inference time. The experimental results show that the CNN+Transformer model best performs in most evaluation metrics with an accuracy of 0.795, while the CNN+GRU model performs the worst with an accuracy of only 0.559. In addition, we also analyze the computational efficiency of each model in terms of training and inference time. Overall, this paper provides important direction for future research and real-world applications by thoroughly evaluating the effectiveness of various deep learning models in pain classification tasks.

Temperature Influences Driving Performance, While Interior Ambient Lighting Does Not

SSRN Electronic Journal · 2024-01-01

Efficacy of Low‐Dose Scopolamine and Palonosetron in Reducing Immediate Post‐Gastrointestinal Endoscopy Nausea and Vomiting: A Prospective, Randomized, Controlled Study

The Journal of Clinical Pharmacology · 2024-09-03

This study aimed to assess the incidence of post-discharge nausea and vomiting (PDNV) following sedation with nalbuphine and etomidate and to evaluate the prophylactic effects of scopolamine in reducing PDNV. A two-stage prospective clinical trial was conducted. The first part involved an observational study of 77 subjects to assess the PDNV incidence post-sedation with nalbuphine, etomidate, and propofol. The second part compared the effectiveness of palonosetron 0.075 mg (P group), scopolamine 0.1 mg (S group), and their combination (PS group) in reducing PDNV. The primary endpoint was the incidence of PDNV within 8 h post-sedation. Secondary outcomes included PDNV frequency and severity at 8-24, 0-24, and 24-48 h and side effects of medications. The incidence of PDNV within 8 h post-sedation was 37.66% (29/77). The PS group showed a significantly lower PDNV rate of 2.56% within 8 h, compared to the P group (35.71%, P < .001), S group (19.64%, P < .001), and control group (38.39%, P < .001), respectively. The S group (19.64%) also had a lower rate than the P group (35.71%, P = .007) and the control group (38.39%, P = .002). Subgroup analysis suggested a potential differential effect of palonosetron in reducing vomiting among male patients undergoing gastrointestinal procedures. The combination therapy was also associated with fewer cases of mild or no nausea and vomiting. In summary, the incidence of PDNV following sedation with nalbuphine and etomidate was notably high. The combination of scopolamine and palonosetron was more effective in preventing PDNV, with implications for improved post-sedation care.

A Novel Labeling Method of Physiological-based Pressure Pain Assessment Among Patients With and Without Chronic Low Back Pain

Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2024-09-01 · 3 citations

Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups.