Pain and Stress Detection Using Wearable Sensors and Devices-A Review

CogWatch: An open-source platform to monitor physiological indicators for cognitive workload and stress

Cognitive workload is a measure of the mental resources a user is dedicating to a given task. Low cognitive workload produces boredom and decreased vigilance, which can lead to an increase in response time. Under high cognitive workload the information processing burden of the user increases significantly, thereby compromising the ability to effectively monitor their environment for unexpected stimuli or respond to emergencies. In cognitive workload and stress monitoring research, sensors are used to measure applicable physiological indicators to infer the state of user. For example, electrocardiography or photoplethysmography are often used to track both the rate at which the heart beats and variability between the individual heart beats. Photoplethysmography and chest straps are also used in studies to track fluctuations in breathing rate. The Galvanic Skin Response is a change in sweat rate (especially on the palms and wrists) and is typically measured by tracking how the resistance of two probes at a fixed distance on the subject's skin changes over time. Finally, fluctuations in Skin Temperature are typically tracked with thermocouples or infrared light (IR) measuring systems in these experiments. While consumer options such a smartwatches for health tracking often have the integrated ability to perform photoplethysmography, they typically perform significant processing on the data which is not transparent to the user and often have a granularity of data that is far too low to be useful for research purposes. It is possible to purchase sensor boards that can be added to Arduino systems, however, these systems generally are very large and obtrusive. Additionally, at the high end of the spectrum there are medical tools used to track these physiological signals, but they are often very expensive and require specific software to be licensed for communication. In this paper, an open-source solution to create a physiological tracker with a wristwatch form factor is presented and validated, using conventional off-the-shelf components. The proposed tool is intended to be applied as a cost-effective solution for research and educational settings.

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life

ABSTRACT

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.

The use of wearable devices on physical activity levels among individuals living with diabetes: 2017 BRFSS

AIM

This study aims to examine the association between wearing wearable devices and physical activity levels among people living with diabetes.

METHODS

1298 wearable device users and nonusers living with diabetes from eight states of the 2017 Behavioral Risk Factors Surveillance System were included in the analysis. Unadjusted and adjusted linear regression was performed to determine the association between self-reported physical activity per week (min) and wearable device usage (users and nonusers) among people living with diabetes using survey analysis.

RESULTS

84.97 % (95 % CI [80.39, 88.89]) of participants were nonusers of wearable devices, while 15.03 % (95 % CI [11.11, 19.61]) were users. Across the sample, the average weekly physical activity was 427.39 mins (95 % Cl [356.43, 498.35]). Nonusers had a higher physical activity per week with 433.83 mins (95 % CI [353.59, 514.07]), while users only had 392.59 mins (95 % CI [253.48, 531.69]) of physical activity per week. However, the differences between the two groups were non-statistically significant (p=.61). In both adjusted and unadjusted linear regressions between physical activity per week and wearable device usage, statistically significant associations were not found (unadjusted: β=-41.24, p=.62; adjusted: β=-56.41, p=.59).

CONCLUSION

Further research is needed to determine the effectiveness of wearable devices in promoting physical activity among people with diabetes. Additionally, there is a need to determine how people with diabetes use wearable devices that could promote physical activity levels.

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals

BACKGROUND

Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored.

OBJECTIVE

To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease.

METHODS

A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions.

RESULTS

The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores.

CONCLUSION

The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.

Transformer encoder with multiscale deep learning for pain classification using physiological signals

Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model's superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.

Effect of stress-based interventions on the quality of life of people with an intellectual disability and their caregivers

PURPOSE

People with intellectual disabilities often show challenging behaviour, which can manifest itself in self-harm or aggression towards others. Real-time monitoring of stress in clients with challenging behaviour can help caregivers to promptly deploy interventions to prevent escalations, ultimately to improve the quality of life of client and caregiver. This study aimed to assess the impact of real-time stress monitoring with HUME, and the subsequent interventions deployed by the care team, on stress levels and quality of life.

MATERIALS AND METHODS

Real-time stress monitoring was used in 41 clients with intellectual disabilities in a long-term care setting over a period of six months. Stress levels were determined at the start and during the deployment of the stress monitoring system. The quality of life of the client and caregiver was measured with the Outcome Rating Scale at the start and at three months of use.

RESULTS

The results showed that the HUME-based interventions resulted in a stress reduction. The perceived quality of life was higher after three months for both the clients and caregivers. Furthermore, interventions to provide proximity were found to be most effective in reducing stress and increasing the client's quality of life.

CONCLUSIONS

The study demonstrates that real-time stress monitoring with the HUME and the following interventions were effective. There was less stress in clients with an intellectual disability and an increase in the perceived quality of life. Future larger and randomized controlled studies are needed to confirm these findings.

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol

INTRODUCTION

Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors.

METHODS AND ANALYSIS

We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods.

ETHICS AND DISSEMINATION

The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation.

TRIAL REGISTRATION NUMBER

NCT05747040.

The Challenges of Pain Assessment in Geriatric Patients With Dementia: A Review

Pain in dementia patients is common, poorly measured, and undertreated. It is important to discuss the challenges in the pain assessment and management to find a possible solution for adequate pain management. The aim of this article is to discuss the challenges in the assessment of pain in geriatric patients with dementia. An extensive online database search was conducted via multiple websites using the following keywords: "dementia," "pain assessments," "pain assessment with dementia," "causes of pain with dementia," "pain assessments using recent technology," "geriatric," and "old age" to identify the relevant articles. Our inclusion criteria were articles that focused on pain in geriatric patients diagnosed with dementia, in English, published between January 2018 and January 2023, and available as free full text and those which were clinical trials, observational studies, review articles, systemic reviews, meta-analysis, or case series. The exclusion criteria were articles that did not have pain in geriatric patients diagnosed with dementia as their primary focus, involving geriatric or non-geriatric patients with major psychological distress, not in the English language, not published between January 2018 and January 2023, and not available as free full-text and those which were case reports and editorial articles. After manually excluding the articles that did not meet our inclusion criteria, we ended up with 38 articles. In conclusion, any instruments have been made for the pain assessment in patients with dementia. The two most common tools used to assess pain in this vulnerable population are the Pain Assessment in Advanced Dementia (PAINAD) and Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC) scales. The utilization of new technology may offer promising solutions for the pain assessment in patients with dementia.

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia

OBJECTIVE

The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only.

METHODS

This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS.

RESULTS

The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN.

CONCLUSION

We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information.

SIGNIFICANCE

Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.

Transcranial Magnetic Stimulation-Induced Heart-Brain Coupling: Implications for Site Selection and Frontal Thresholding-Preliminary Findings

Background

Neurocardiac-guided transcranial magnetic stimulation (TMS) uses repetitive TMS (rTMS)-induced heart rate deceleration to confirm activation of the frontal-vagal pathway. Here, we test a novel neurocardiac-guided TMS method that utilizes heart-brain coupling (HBC) to quantify rTMS-induced entrainment of the interbeat interval as a function of TMS cycle time. Because prior neurocardiac-guided TMS studies indicated no association between motor and frontal excitability threshold, we also introduce the approach of using HBC to establish individualized frontal excitability thresholds for optimally dosing frontal TMS.

Methods

In studies 1A and 1B, we validated intermittent theta burst stimulation (iTBS)-induced HBC (2 seconds iTBS on; 8 seconds off: HBC = 0.1 Hz) in 15 (1A) and 22 (1B) patients with major depressive disorder from 2 double-blind placebo-controlled studies. In study 2, HBC was measured in 10 healthy subjects during the 10-Hz "Dash" protocol (5 seconds 10-Hz on; 11 seconds off: HBC = 0.0625 Hz) applied with 15 increasing intensities to 4 evidence-based TMS locations.

Results

Using blinded electrocardiogram-based HBC analysis, we successfully identified sham from real iTBS sessions (accuracy: study 1A = 83%, study 1B = 89.5%) and found a significantly stronger HBC at 0.1 Hz in active compared with sham iTBS (d = 1.37) (study 1A). In study 2, clear dose-dependent entrainment (p = .002) was observed at 0.0625 Hz in a site-specific manner.

Conclusions

We demonstrated rTMS-induced HBC as a function of TMS cycle time for 2 commonly used clinical protocols (iTBS and 10-Hz Dash). These preliminary results supported individual site specificity and dose-response effects, indicating that this is a potentially valuable method for clinical rTMS site stratification and frontal thresholding. Further research should control for TMS side effects, such as pain of stimulation, to confirm these findings.

Pain Management in Pediatric Burns: A Review of the Science behind It

Pediatric burns are a significant medical issue that can have long-term effects on various aspects of a child's health and well-being. Pain management in pediatric burns is a crucial aspect of treatment to ensure the comfort and well-being of young patients. The causes and risk factors for pediatric burns vary depending on various factors, such as geographical location, socioeconomic status, and cultural practices. Assessing pain in pediatric patients, especially during burn injury treatment, poses several challenges. These challenges stem from various factors, including the age and developmental stage of the child, the nature of burn injuries, and the limitations of pain assessment tools. In pediatric pain management, various pain assessment tools and scales are used to evaluate and measure pain in children. These tools are designed to account for the unique challenges of assessing pain in pediatric patients, including their age, developmental stage, and ability to communicate effectively. Pain can have significant physical, emotional, and psychological consequences for pediatric patients. It can interfere with their ability to engage in daily activities, disrupt sleep patterns, and negatively affect their mood and behavior. Untreated pain can also lead to increased stress, anxiety, and fear, which can further exacerbate the pain experience. Acute pain, which is short-term and typically associated with injury or illness, can disrupt a child's ability to engage in physical activities and impede their overall recovery process. On the other hand, chronic pain, which persists for an extended period, can have long-lasting effects on physical functioning and quality of life in children. The psychological consequences of burns can persist long after the physical wounds have healed, leading to ongoing emotional distress and impaired functioning. Multimodal pain management, which involves the use of multiple interventions or medications targeting different aspects of the pain pathway, has gained recognition as an effective approach for managing pain in both children and adults. However, it is important to consider the specific needs and considerations of pediatric patients when developing evidence-based guidelines for multimodal pain management in this population. Over the years, there have been significant advances in pediatric pain research and technology, leading to a better understanding of pain mechanisms and the development of innovative approaches to assess and treat pain in children. Overall, pain management in pediatric burns requires a multidisciplinary approach that combines pharmacologic and nonpharmacologic interventions.

Analysis of pain research literature through keyword Co-occurrence networks

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.

A Quantitative Measure of Pain with Current Perception Threshold, Pain Equivalent Current, and Quantified Pain Degree: A Retrospective Study

Background: As a subjective sensation, pain is difficult to evaluate objectively. The assessment of pain degree is largely dependent on subjective methods such as the numeric rating scale (NRS). The PainVisionTM system has recently been introduced as an objective pain degree measurement tool. The purpose of this study was to analyze correlations between the NRS and the current perception threshold (CPT), pain equivalent current (PEC), and quantified pain degree (QPD). Methods: Medical records of 398 subjects who visited the pain clinic in a university hospital from March 2017 to February 2019 were retrospectively reviewed. To evaluate the pain degree, NRS, CPT, PEC, and QPD were measured. Subjects were categorized into two groups: the Pain group (n = 355) and the No-pain group (n = 43). Results: The NRS showed a negative correlation with CPT (R = -0.10, p = 0.054) and a positive correlation with QPD (R = 0.13, p = 0.008). Among various diseases, only spinal disease patients showed a negative correlation between CPT and NRS (R = -0.22, p = 0.003). Additionally, there were significant differences in CPT and QPD between the Pain and No-pain groups (p = 0.005 and p = 0.002, respectively). Conclusions: CPT and QPD measured using the PainVisionTM system could be used to estimate pain intensity and the presence of pain. These parameters would be considered useful for predicting pain itself and its intensity.

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.

Pain Management in the Most Vulnerable Intellectual Disability: A Review

This review is made up of two parts; the first part discussing intellectual disability (ID) in general, while the second part covers the pain associated with intellectual disability and the challenges and practical tips for the management of pain associated with (ID). Intellectual disability is characterized by deficits in general mental abilities, such as reasoning, problem solving, planning, abstract thinking, judgment, academic learning, and learning from experience. ID is a disorder with no definite cause but has multiple risk factors, including genetic, medical, and acquired. Vulnerable populations such as individuals with intellectual disability may experience more pain than the general population due to additional comorbidities and secondary conditions, or at least the same frequency of pain as in the general population. Pain in patients with ID remains largely unrecognized and untreated due to barriers to verbal and non-verbal communication. It is important to identify patients at risk to promptly prevent or minimize those risk factors. As pain is multifactorial, thus, a multimodal approach using both pharmacotherapy and non-pharmacological management is often the most beneficial. Parents and caregivers should be oriented to this disorder, given adequate training and education, and be actively involved with the treatment program. Significant work to create new pain assessment tools to improve pain practices for individuals with ID has taken place, including neuroimaging and electrophysiological studies. Recent advances in technology-based interventions such as virtual reality and artificial intelligence are rapidly growing to help give patients with ID promising results to develop pain coping skills with effective reduction of pain and anxiety. Therefore, this narrative review highlights the different aspects regarding the current status of the pain associated with intellectual disability, with more emphasis on the recent pieces of evidence for the assessment and management of pain among populations with intellectual disability.

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease

Background

Fatigue is a subjective, complex and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. It is a well-recognized manifestation in chronic inflammatory rheumatic diseases, such as Sjögren's Syndrome and Systemic Lupus Erythematosus and an important predictor of patient's health-related quality of life (HRQoL). Patient reported outcome questions are the key instruments to assess fatigue. To date, there is no consensus about reliable quantitative assessments of fatigue.

Method

Observational data for a period of one month were collected from 296 participants in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity and sleep features, and app-based daily and weekly questions covering various HRQoL factors including pain, mood, general physical activity and fatigue. Descriptive statistics and hierarchical clustering of digital data were used to describe behavioural phenotypes. Gradient boosting classifiers were trained to classify participant-reported weekly fatigue and daily tiredness from multi-sensor and other participant-reported data, and extract a set of key predictive features.

Results

Cluster analysis of Fitbit parameters highlighted multiple digital phenotypes, including sleep-affected, fatigued and healthy phenotypes. Features from participant-reported data and Fitbit data both contributed as key predictive features of weekly physical and mental fatigue and daily tiredness. Participant answers to pain and depressed mood-related daily questions contributed the most as top features for predicting physical and mental fatigue, respectively. To classify daily tiredness, participant answers to questions on pain, mood and ability to perform daily activities contributed the most. Features related to daily resting heart rate and step counts and bouts were overall the most important Fitbit features for the classification models.

Conclusion

These results demonstrate that multimodal digital data can be used to quantitatively and more frequently augment pathological and non-pathological participant-reported fatigue.

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

BACKGROUND

Neurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject's long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy.

METHOD

Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject's response to the numerical rating scale (NRS) or patient global impression of change (PGIC).

RESULTS

Principal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome.

CONCLUSIONS

The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient's response to therapy and their general well-being.

Zoom-Based Mindfulness-Oriented Recovery Enhancement Plus Just-in-Time Mindfulness Practice Triggered by Wearable Sensors for Opioid Craving and Chronic Pain

Objective

The opioid crisis in the USA remains severe during the COVID-19 pandemic, which has reduced access to evidence-based interventions. This Stage 1 randomized controlled trial (RCT) assessed the preliminary efficacy of Zoom-based Mindfulness-Oriented Recovery Enhancement (MORE) plus Just-in-Time Adaptive Intervention (JITAI) prompts to practice mindfulness triggered by wearable sensors (MORE + JITAI).

Method

Opioid-treated chronic pain patients (n = 63) were randomized to MORE + JITAI or a Zoom-based supportive group (SG) psychotherapy control. Participants completed ecological momentary assessments (EMA) of craving and pain (co-primary outcomes), as well as positive affect, and stress at one random probe per day for 90 days. EMA probes were also triggered when a wearable sensor detected the presence of physiological stress, as indicated by changes in heart rate variability (HRV), at which time participants in MORE + JITAI were prompted by an app to engage in audio-guided mindfulness practice.

Results

EMA showed significantly greater reductions in craving, pain, and stress, and increased positive affect over time for participants in MORE + JITAI than for participants in SG. JITAI-initiated mindfulness practice was associated with significant improvements in these variables, as well as increases in HRV. Machine learning predicted JITAI-initiated mindfulness practice effectiveness with reasonable sensitivity and specificity.

Conclusions

In this pilot trial, MORE + JITAI demonstrated preliminary efficacy for reducing opioid craving and pain, two factors implicated in opioid misuse. MORE + JITAI is a promising intervention that warrants investigation in a fully powered RCT.

Preregistration

This study is registered on ClinicalTrials.gov (NCT04567043).

Differences in Trapezius Muscle H-Reflex between Asymptomatic Subjects and Symptomatic Shoulder Pain Subjects

In chronic shoulder pain, adaptations in the nervous system such as in motoneuron excitability, could contribute to impairments in scapular muscles, perpetuation and recurrence of pain and reduced improvements during rehabilitation. The present cross-sectional study aims to compare trapezius neural excitability between symptomatic and asymptomatic subjects. In 12 participants with chronic shoulder pain (symptomatic group) and 12 without shoulder pain (asymptomatic group), the H reflex was evoked in all trapezius muscle parts, through C3/4 nerve stimulation, and the M-wave through accessory nerve stimulation. The current intensity to evoke the maximum H reflex, the latency and the maximum peak-to-peak amplitude of both the H reflex and M-wave, as well as the ratio between these two variables, were calculated. The percentage of responses was considered. Overall, M-waves were elicited in most participants, while the H reflex was elicited only in 58-75% or in 42-58% of the asymptomatic and symptomatic participants, respectively. A comparison between groups revealed that the symptomatic group presented a smaller maximum H reflex as a percentage of M-wave from upper trapezius and longer maximal H reflex latency from the lower trapezius (p < 0.05). Subjects with chronic shoulder pain present changes in trapezius H reflex parameters, highlighting the need to consider trapezius neuromuscular control in these individuals' rehabilitation.

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings.

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning

With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient's data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson's correlation coefficient on WEKA for features' importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

Wearables and Machine Learning for Improving Runners' Motivation from an Affective Perspective

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes' performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners' emotions during their training. The solution is based on the analysis of runners' electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners' motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.

Diagnosing Pain in Individuals with Intellectual and Developmental Disabilities: Current State and Novel Technological Solutions

Pain assessment poses a challenge in several groups of clients, yet specific barriers arise when it comes to pain assessment of individuals with intellectual and developmental disabilities (IDD), due mostly to communication challenges preventing valid and reliable self-reports. Despite increased interest in pain assessment of those diagnosed with IDD within recent years, little is known about pain behavior in this group. The present article overviews the current state of pain diagnosis for individuals with IDD, focusing on existing pain assessment scales. In addition, it suggests technological developments offering new ways to diagnose existence of pain in this population, such as a Smartphone App for caregivers based on unique acoustic characteristics of pain-related vocal responses, or the use of smart wearable shirts that enable continuous surveillance of vital physiological signs. Such novel technological solutions may improve diagnosis of pain in the IDD population, as well as in other individuals with complex communication needs, to provide better pain treatment and enhance overall quality of life.

Automatic detection of the mental state in responses towards relaxation

Nowadays, considering society's highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of 25.76 ± 3.7 years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to 94.01 ± 1.73 % with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of 90.36 ± 1.62 %.

Comparison of Deep Learning Algorithms in Predicting Expert Assessments of Pain Scores during Surgical Operations Using Analgesia Nociception Index

There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient's HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors' predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.

Wearable Sensors for Healthcare: Fabrication to Application

This paper presents a substantial review of the deployment of wearable sensors for healthcare applications. Wearable sensors hold a pivotal position in the microelectronics industry due to their role in monitoring physiological movements and signals. Sensors designed and developed using a wide range of fabrication techniques have been integrated with communication modules for transceiving signals. This paper highlights the entire chronology of wearable sensors in the biomedical sector, starting from their fabrication in a controlled environment to their integration with signal-conditioning circuits for application purposes. It also highlights sensing products that are currently available on the market for a comparative study of their performances. The conjugation of the sensing prototypes with the Internet of Things (IoT) for forming fully functioning sensorized systems is also shown here. Finally, some of the challenges existing within the current wearable systems are shown, along with possible remedies.

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.

Anterior plating technique for distal radius: comparing performance after learning through naive versus deliberate practice

BACKGROUND

Surgical teaching is most often carried out in the operating theatre through mentorship, and the performance of surgical procedures is rarely measured. The objective of this article is to compare the progression in learning curves of junior surgeons trained in the anterior plating technique for the distal radius on a nonbiological model according to three different methods.

METHODS

The materials comprised 12 junior surgeons of level 1 or 2 (as per Tang and Giddins) divided into three groups: control (G1), naive practice (G2), and deliberate practice (G3). The three groups watched a demonstration video of a level 5 expert. The four G1 surgeons (two level 1 and two level 2) saw the video only once, and each inserted five plates. The four G2 surgeons (two level 1 and two level 2) inserted five plates and watched the video before each time. The four G3 surgeons (two level 1 and two level 2) saw the video before the first plate insertion. Before posing the subsequent four plates, the four G3 surgeons watched their own video, and the expert indicated their errors and how to avoid them next time. A 12-criteria OSATS defined on the basis of the 60 videos, each graded from one (min.) to five (max.), was used to measure the objective surgical performance per plating (min. 12; max. 60) and per series of five plate fixations (min. 60, max. 300).

RESULTS

The total average objective performance of G1 was 44.73, of G2 was 50.57 and of G3 was 54.35. Change in objective performance was better for G3 (13.25) than G2 (5) or G1 (3.75). For all groups, the progression in objective performance was better amongst level 1 surgeons (9) than level 2 surgeons (5.6).

CONCLUSION

Surgical teaching is based on mentorship and experience. However, since "see one, practice many, do one" has started to replace "see one, do one, teach one", learning techniques have increasingly relied on procedure simulators. Against this background, few studies have looked at measuring the performance of surgical procedures and improved learning curves. Our results appear to suggest that deliberate practice, when used in addition to mentorship, is the best option for shortening the growth phase of the learning curve and improving performance. Deliberate practice is a learning technique for surgical procedures that is complementary to mentorship and experience, which allows the growth phase of the learning curve to be shortened and the objective performance of junior surgeons to be improved.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

"Listen to Your Immune System When It's Calling for You": Monitoring Autoimmune Diseases Using the iShU App

The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.

Physiological responses to pain in cancer patients: A systematic review

BACKGROUND AND OBJECTIVE

Pain is one of the most debilitating symptoms in persons with cancer. Still, its assessment is often neglected both by patients and healthcare professionals. There is increasing interest in conducting pain assessment and monitoring via physiological signals that promise to overcome the limitations of state-of-the-art pain assessment tools. This systematic review aims to evaluate existing experimental studies to identify the most promising methods and results for objectively quantifying cancer patients' pain experience.

METHODS

Four electronic databases (Pubmed, Compendex, Scopus, Web of Science) were systematically searched for articles published up to October 2020.

RESULTS

Fourteen studies (528 participants) were included in the review. The selected studies analyzed seven physiological signals. Blood pressure and ECG were the most used signals. Sixteen physiological parameters showed significant changes in association with pain. The studies were fairly consistent in stating that heart rate, the low-frequency to high-frequency component ratio (LF/HF), and systolic blood pressure positively correlate with the pain.

CONCLUSIONS

Current evidence supports the hypothesis that physiological signals can help objectively quantify, at least in part, cancer patients' pain experience. While there is much more to be done to obtain a reliable pain assessment method, this review takes an essential first step by highlighting issues that should be taken into account in future research: use of a wearable device for pervasive recording in a real-world context, implementation of a big-data approach possibly supported by AI, including multiple stratification factors (e.g., cancer site and stage, source of pain, demographic and psychosocial data), and better-defined recording procedures. Improved methods and algorithms could then become valuable add-ons in taking charge of cancer patients.

Combining Electrodermal Activity With the Peak-Pain Time to Quantify Three Temporal Regions of Pain Experience

Background

Self-reported pain levels, while easily measured, are often not reliable for quantifying pain. More objective methods are needed that supplement self-report without adding undue burden or cost to a study. Methods that integrate multiple measures, such as combining self-report with physiology in a structured and specific-to-pain protocol may improve measures.

Method

We propose and study a novel measure that combines the timing of the peak pain measured by an electronic visual-analog-scale (eVAS) with continuously-measured changes in electrodermal activity (EDA), a physiological measure quantifying sympathetic nervous system activity that is easily recorded with a skin-surface sensor. The new pain measure isolates and specifically quantifies three temporal regions of dynamic pain experience: I. Anticipation preceding the onset of a pain stimulus, II. Response rising to the level of peak pain, and III. Recovery from the peak pain level. We evaluate the measure across two pain models (cold pressor, capsaicin), and four types of treatments (none, A=pregabalin, B=oxycodone, C=placebo). Each of 24 patients made four visits within 8 weeks, for 96 visits total: A training visit (TV), followed by three visits double-blind presenting A, B, or C (randomized order). Within each visit, a participant experienced the cold pressor, followed by an hour of rest during which one of the four treatments was provided, followed by a repeat of the cold pressor, followed by capsaicin.

Results

The novel method successfully discriminates the pain reduction effects of the four treatments across both pain models, confirming maximal pain for no-treatment, mild pain reduction for placebo, and the most pain reduction with analgesics. The new measure maintains significant discrimination across the test conditions both within a single-day's visit (for relative pain relief within a visit) and across repeated visits spanning weeks, reducing different-day-physiology affects, and providing better discriminability than using self-reported eVAS.

Conclusion

The new method combines the subjectively-identified time of peak pain with capturing continuous physiological data to quantify the sympathetic nervous system response during a dynamic pain experience. The method accurately discriminates, for both pain models, the reduction of pain with clinically effective analgesics.

A vision for the future of wearable sensors in spine care and its challenges: narrative review

OBJECTIVE

This review aimed to: (I) provide a brief overview of some topical areas of current literature regarding applications of wearable sensors in the management of low back pain (LBP); (II) present a vision for a future comprehensive system that integrates wearable sensors to measure multiple parameters in the real world that contributes data to guide treatment selection (aided by artificial intelligence), uses wearables to aid treatment support, adherence and outcome monitoring, and interrogates the response of the individual patient to the prescribed treatment to guide future decision support for other individuals who present with LBP; and (III) consider the challenges that will need to be overcome to make such a system a reality.

BACKGROUND

Advances in wearable sensor technologies are opening new opportunities for the assessment and management of spinal conditions. Although evidence of improvements in outcomes for individuals with LBP from the use of sensors is limited, there is enormous future potential.

METHODS

Narrative review and literature synthesis.

CONCLUSIONS

Substantial research is underway by groups internationally to develop and test elements of this system, to design innovative new sensors that enable recording of new data in new ways, and to fuse data from multiple sources to provide rich information about an individual's experience of LBP. Together this system, incorporating data from wearable sensors has potential to personalise care in ways that were hitherto thought impossible. The potential is high but will require concerted effort to develop and ultimately will need to be feasible and more effective than existing management.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

Virtual Reality in Home Palliative Care: Brief Report on the Effect on Cancer-Related Symptomatology

Virtual reality (VR) has been used as a complementary therapy for managing psychological and physical symptoms in cancer patients. In palliative care, the evidence about the use of VR is still inadequate. This study aims to assess the effect of an immersive VR-based intervention conducted at home on anxiety, depression, and pain over 4days and to evaluate the short-term effect of VR sessions on cancer-related symptomatology. Participants were advanced cancer patients assisted at home who were provided with a VR headset for 4days. On days one and four, anxiety and depression were measured by the Hospital Anxiety and Depression Scale (HADS) and pain by the Brief Pain Inventory (BPI). Before and after each VR session, symptoms were collected by the Edmonton Symptom Assessment Scale (ESAS). Participants wore a smart wristband measuring physiological signals associated with pain, anxiety, and depression. Fourteen patients (mean age 47.2±14.2years) were recruited. Anxiety, depression (HADS), and pain (BPI) did not change significantly between days one and four. However, the ESAS items related to pain, depression, anxiety, well-being, and shortness of breath collected immediately after the VR sessions showed a significant improvement (p<0.01). A progressive reduction in electrodermal activity has been observed comparing the recordings before, during, and after the VR sessions, although these changes were not statistically significant. This brief research report supports the idea that VR could represent a suitable complementary tool for psychological treatment in advanced cancer patients assisted at home.

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment

Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care. We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks'') for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.