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1. Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

   https://doi.org/10.1371/journal.pone.0254108  

   Pouromran, Fatemeh; Radhakrishnan, Srinivasan; Kamarthi, Sagar (July 2021, PLOS ONE)

   Le, Khanh N.Q. (Ed.)

   In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device. 

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2. Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study

   https://doi.org/10.3390/signals2040051  

   Agarwal, Ankita; Graft, Josephine; Schroeder, Noah; Romine, William (December 2021, Signals)

   Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device. 

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3. Synchronized affect in shared experiences strengthens social connection

   https://doi.org/10.1038/s42003-023-05461-2  

   Cheong, Jin Hyun; Molani, Zainab; Sadhukha, Sushmita; Chang, Luke J. (October 2023, Communications Biology)

   Abstract People structure their days to experience events with others. We gather to eat meals, watch TV, and attend concerts together. What constitutes a shared experience and how does it manifest in dyadic behavior? The present study investigates how shared experiences—measured through emotional, motoric, physiological, and cognitive alignment—promote social bonding. We recorded the facial expressions and electrodermal activity (EDA) of participants as they watched four episodes of a TV show for a total of 4 h with another participant. Participants displayed temporally synchronized and spatially aligned emotional facial expressions and the degree of synchronization predicted the self-reported social connection ratings between viewing partners. We observed a similar pattern of results for dyadic physiological synchrony measured via EDA and their cognitive impressions of the characters. All four of these factors, temporal synchrony of positive facial expressions, spatial alignment of expressions, EDA synchrony, and character impression similarity, contributed to a latent factor of a shared experience that predicted social connection. Our findings suggest that the development of interpersonal affiliations in shared experiences emerges from shared affective experiences comprising synchronous processes and demonstrate that these complex interpersonal processes can be studied in a holistic and multi-modal framework leveraging naturalistic experimental designs. 

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4. Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics

   https://doi.org/10.3389/fnetp.2023.1227228  

   Sides, Krystal; Kilungeja, Grentina; Tapia, Matthew; Kreidl, Patrick; Brinkmann, Benjamin H.; Nasseri, Mona (October 2023, Frontiers in Network Physiology)

   This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p $<$0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p $>$0.05). There was a significant difference between ovulating and non-ovulating cycles (p $<$0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively. 

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5. Close TIES in relationships: A dynamic systems approach for modeling physiological linkage

   https://doi.org/10.1177/02654075221082594  

   Kuelz, Ashley; Boyd, Savannah; Butler, Emily (October 2022, Journal of Social and Personal Relationships)

   We explore complex dynamic patterns of autonomic physiological linkage (i.e., statistical interdependence of partner’s physiology; PL), within the sympathetic and parasympathetic nervous systems (SNS and PNS), as potential correlates of emotional and regulatory dynamics in close relationships. We include electrodermal activity (EDA) as an indicator of SNS activation and respiratory sinus arrhythmia (RSA) as an indicator of regulatory and/or homeostatic processes within the PNS. Measures of EDA and RSA were collected in 10-second increments from 53 heterosexual couples during a mixed-emotion conversation in the laboratory. We used the R statistical package, rties (Butler & Barnard, 2019), to model the dynamics of EDA and RSA with a coupled oscillator model and then categorized couples into qualitatively distinct profiles based on the set of parameters that emerged. We identified two patterns for EDA and three patterns for RSA. We then investigated associations between the PL patterns and self-report measures of relationship and conversation quality and emotional valence using Bayesian multilevel and logistic regression models. Overall, we found robust results indicating that PL profiles were credibly predicted by valence and relationship quality reported prior to the conversations. In contrast, we found very little evidence suggesting that PL patterns predict self-reported conversation quality or valence following the conversation. Together, these results suggest that PL across autonomic subsystems may reflect different processes and therefore have different implications when considering interpersonal dynamics. 

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