More Like this

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. 

   more » « less
2. Distress and self-care among chaplains working in palliative care

   https://doi.org/10.1017/S1478951518001062  

   White, Kelsey B.; Murphy, Patricia E.; Jeuland, Jane; Fitchett, George (October 2019, Palliative and Supportive Care)

   Abstract Background The prevalence of burnout and distress among palliative care professionals has received much attention since research suggests it negatively impacts the quality of care. Although limited, research suggests low levels of burnout or distress among healthcare chaplains; however, there has been no research among chaplains working in specific clinical contexts, including palliative care. Objective This study explored the distress, self-care, and debriefing practices of chaplains working in palliative care. Method Exploratory, cross-sectional survey of professional chaplains. Electronic surveys were sent to members of four professional chaplaincy organizations between February and April 2015. Primary measures of interest included Professional Distress, Distress from Theodicy, Informal Self-care, Formal Self-care, and debriefing practices. Result More than 60% of chaplains working in palliative care reported feeling worn out in the past 3 months because of their work as a helper; at least 33% practice Informal Self-care weekly. Bivariate analysis suggested significant associations between Informal Self-care and both Professional Distress and Distress from Theodicy. Multivariate analysis also identified that distress decreased as Informal and Formal Self-care increased. Significance of results Chaplains working in palliative care appear moderately distressed, possibly more so than chaplains working in other clinical areas. These chaplains also use debriefing, with non-chaplain palliative colleagues, to process clinical experiences. Further research is needed about the role of religious or spiritual beliefs and practices in protecting against stress associated with care for people at the end of life. 

   more » « less
3. Pilot study of a real-time early agitation capture technology (REACT) for children with intellectual and developmental disabilities

   Khan, Nibraas; Plunk, Abigale; Zheng, Zhaobo; Adiani, Deeksha; Staubitz, John; Weitlauf, Amy; Sarkar, Nilanjan Sarkar (October 2024, Digital health)

   Objective: Children and adolescents with intellectual and developmental disabilities (IDD), particularly those with autism spectrum disorder, are at increased risk of challenging behaviors such as self-injury, aggression, elopement, and property destruction. To mitigate these challenges, it is crucial to focus on early signs of distress that may lead to these behaviors. These early signs might not be visible to the human eye but could be detected by predictive machine learning (ML) models that utilizes real-time sensing. Current behavioral assessment practices lack such proactive predictive models. This study developed and pilot-tested real-time early agitation capture technology (REACT), a real-time multimodal ML model to detect early signs of distress, termed “agitations.” Integrating multimodal sensing, ML, and human expertise could make behavioral assessments for people with IDD safer and more efficient. Methods: We leveraged wearable technology to collect behavioral and physiological data from three children with IDD aged 6 to 9 years. The effectiveness of the REACT system was measured using F1 score, assessing its usefulness at the time of agitation to 20s prior. Results: The REACT system was able to detect agitations with an average F1 score of 78.69% at the time of agitation and 68.20% 20s prior. Conclusion: The findings support the use of the REACT model for real-time, proactive detection of agitations in children with IDD. This approach not only improves the accuracy of detecting distress signals that are imperceptible to the human eye but also increases the window for timely intervention before behavioral escalation, thereby enhancing safety, well-being, and inclusion for this vulnerable population. We believe that such technological support system will enhance user autonomy, self-advocacy, and self-determination. 

   more » « less
4. Evaluating Autonomous Vehicle External Communication Using a Multi-Pedestrian VR Simulator

   https://doi.org/10.1145/3678506  

   Tran, Tram_Thi Minh; Parker, Callum; Yu, Xinyan; Dey, Debargha; Martens, Marieke; Bazilinskyy, Pavlo; Tomitsch, Martin (August 2024, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   With the rise of autonomous vehicles (AVs) in transportation, a pressing concern is their seamless integration into daily life. In multi-pedestrian settings, two challenges emerge: ensuring unambiguous communication to individual pedestrians via external Human-Machine Interfaces (eHMIs), and the influence of one pedestrian over another. We conducted an experiment (N=25) using a multi-pedestrian virtual reality simulator. Participants were paired and exposed to three distinct eHMI concepts: on the vehicle, within the surrounding infrastructure, and on the pedestrian themselves, against a baseline without any eHMI. Results indicate that all eHMI concepts improved clarity of communication over the baseline, but differences in their effectiveness were observed. While pedestrian and infrastructure communications often provided more direct clarity, vehicle-based cues at times introduced uncertainty elements. Furthermore, the study identified the role of co-located pedestrians: in the absence of clear AV communication, individuals frequently sought cues from their peers. 

   more » « less
5. Assessing predictability of environmental time series with statistical and machine learning models

   https://doi.org/10.1002/env.2864  

   Bonas, Matthew; Datta, Abhirup; Wikle, Christopher_K; Boone, Edward_L; Alamri, Faten_S; Hari, Bhava_Vyasa; Kavila, Indulekha; Simmons, Susan_J; Jarvis, Shannon_M; Burr, Wesley_S; et al (July 2024, Environmetrics)

   Abstract The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of physical processes ranging from air pollution to meteorology. This presents both challenges and opportunities to the statistical community, which could contribute to the machine learning literature with a model‐based approach with formal uncertainty quantification. Should, however, classical statistical methodologies be discarded altogether in environmental statistics, and should our contribution be focused on formalizing machine learning constructs? This work aims at providing some answers to this thought‐provoking question with two time series case studies where selected models from both the statistical and machine learning literature are compared in terms of forecasting skills, uncertainty quantification and computational time. Relative merits of both class of approaches are discussed, and broad open questions are formulated as a baseline for a discussion on the topic. 

   more » « less