The Experts below are selected from a list of 141 Experts worldwide ranked by ideXlab platform
Elizabeth B Klerman - One of the best experts on this subject based on the ideXlab platform.
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identifying objective Physiological markers and modifiable behaviors for self reported stress and mental health status using wearable Sensors and mobile phones observational study
Journal of Medical Internet Research, 2018Co-Authors: Akane Sano, Sara Taylor, Andrew W Mchill, Andrew J K Phillips, Laura K Barger, Elizabeth B KlermanAbstract:Background: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. Objective: We developed new tools that provide objective Physiological and behavioral measures using wearable Sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. Methods: We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based Sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for Sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. Results: We identified the Physiological Sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable Sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable Sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. Conclusions: New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping. [J Med Internet Res 2018;20(6):e210]
Garun S Hamilton - One of the best experts on this subject based on the ideXlab platform.
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energy expenditure in obstructive sleep apnea validation of a multiple Physiological Sensor for determination of sleep and wake
Sleep and Breathing, 2013Co-Authors: Denise M Odriscoll, Anthony Turton, Janet M Copland, Boyd Josef Gimnicher Strauss, Garun S HamiltonAbstract:Obstructive sleep apnea (OSA) may be associated with increased energy expenditure (EE) during sleep. As actigraphy is inaccurate at estimating EE from body movement counts alone, we aimed to compare a multiple Physiological Sensor with polysomnography for determination of sleep and wake, and to test the hypothesis that OSA is associated with increased EE during sleep. We studied 50 adults referred for routine overnight polysomnography. In addition to polysomnography, the SenseWear Pro3 ArmbandTM (Bodymedia Inc.) was placed on the upper right arm. Epoch-by-epoch agreement rate between the measures of sleep versus wake was calculated. Linear regression analyses were performed for EE against apnea–hypopnea index (AHI), 3% oxygen desaturation index (ODI), body mass index (BMI), waist–hip ratio (WHR), gender, age, and average heart rate during sleep. The epoch-by-epoch agreement rate was high (79.9 ± 1.6%) and the ability of the SenseWear to estimate sleep was very good (sensitivity, 88.7 ± 1.5%). However, it was less accurate in determining wake (specificity 49.9 ± 3.6%). Sleep EE was associated with AHI, 3% ODI, BMI, WHR, and male gender (p < 0.001 for all). Stepwise multiple linear regression however revealed that BMI, male gender, age, and average heart rate during sleep were independent predictors of EE (Model R 2 = 0.78). The SenseWear armband provides a reasonable estimation of sleep but a poor estimation of wake. Furthermore, in a selected population of OSA patients, increasing OSA severity is associated with increased EE during sleep, although primarily through an association with increased BMI. However, as our data are not adjusted for fat-free mass and the SenseWear has yet to be validated for EE in OSA patients, these data should be interpreted with caution.
Mohammad S Obaidat - One of the best experts on this subject based on the ideXlab platform.
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Prioritized payload tuning mechanism for wireless body area network-based healthcare systems
2014 IEEE Global Communications Conference GLOBECOM 2014, 2014Co-Authors: Soumen Moulik, Sudip Misra, Chandan Chakraborty, Mohammad S ObaidatAbstract:This paper presents a priority-based MAC-frame payload tuning mechanism with reduced energy consumption for healthcare systems that use Wireless Body Area Networks (WBANs). A fundamental problem in WBANs is to prioritize the Physiological Sensors depending on several health and external criteria. The challenge is to design a dynamic decision making model that can optimize the energy consumption of each Physiological Sensor. To address this problem we employ the concept of Fuzzy Inference System (FIS) in order to calculate Criticality Index (CI), which signifies the severity or the priority of the Physiological data sensed by each Sensor. Considering the obtained CI value we proceed with designing a Markov Decision Process (MDP) based dynamic decision making model in order to tune MAC-frame payload by optimizing the energy consumption of each Sensor node. We achieve around 25% decrease in the overall energy consumption using our proposed mechanism.
Thomas S. Huang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction
Proceedings of the 6th International Workshop on Audio Visual Emotion Challenge - AVEC '16, 2016Co-Authors: Kevin Brady, Pooya Khorrami, Elizabeth Godoy, Charlie Dagli, Youngjune Gwon, William Campbell, Thomas S. HuangAbstract:The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. The Audio Video Emotion Challenge (AVEC) 2016 provides a well-defined framework for developing and rigorously evaluating innovative approaches for estimating the arousal and valence states of emotion as a function of time. It presents the opportunity for investigating multimodal solutions that include audio, video, and Physiological Sensor signals. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities. It includes a number of technical contributions, including the development of novel high- and low-level features for modeling emotion in the audio, video, and Physiological channels. Low-level features include modeling arousal in audio with minimal prosodic-based descriptors. High-level features are derived from supervised and unsupervised machine learning approaches based on sparse coding and deep learning. Finally, a state space estimation approach is applied for score fusion that demonstrates the importance of exploiting the time-series nature of the arousal and valence states. The resulting system outperforms the baseline systems [10] on the test evaluation set with an achieved Concordant Correlation Coefficient (CCC) for arousal of 0.770 vs 0.702 (baseline) and for valence of 0.687 vs 0.638. Future work will focus on exploiting the time-varying nature of individual channels in the multi-modal framework.
Denise M Odriscoll - One of the best experts on this subject based on the ideXlab platform.
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energy expenditure in obstructive sleep apnea validation of a multiple Physiological Sensor for determination of sleep and wake
Sleep and Breathing, 2013Co-Authors: Denise M Odriscoll, Anthony Turton, Janet M Copland, Boyd Josef Gimnicher Strauss, Garun S HamiltonAbstract:Obstructive sleep apnea (OSA) may be associated with increased energy expenditure (EE) during sleep. As actigraphy is inaccurate at estimating EE from body movement counts alone, we aimed to compare a multiple Physiological Sensor with polysomnography for determination of sleep and wake, and to test the hypothesis that OSA is associated with increased EE during sleep. We studied 50 adults referred for routine overnight polysomnography. In addition to polysomnography, the SenseWear Pro3 ArmbandTM (Bodymedia Inc.) was placed on the upper right arm. Epoch-by-epoch agreement rate between the measures of sleep versus wake was calculated. Linear regression analyses were performed for EE against apnea–hypopnea index (AHI), 3% oxygen desaturation index (ODI), body mass index (BMI), waist–hip ratio (WHR), gender, age, and average heart rate during sleep. The epoch-by-epoch agreement rate was high (79.9 ± 1.6%) and the ability of the SenseWear to estimate sleep was very good (sensitivity, 88.7 ± 1.5%). However, it was less accurate in determining wake (specificity 49.9 ± 3.6%). Sleep EE was associated with AHI, 3% ODI, BMI, WHR, and male gender (p < 0.001 for all). Stepwise multiple linear regression however revealed that BMI, male gender, age, and average heart rate during sleep were independent predictors of EE (Model R 2 = 0.78). The SenseWear armband provides a reasonable estimation of sleep but a poor estimation of wake. Furthermore, in a selected population of OSA patients, increasing OSA severity is associated with increased EE during sleep, although primarily through an association with increased BMI. However, as our data are not adjusted for fat-free mass and the SenseWear has yet to be validated for EE in OSA patients, these data should be interpreted with caution.