PhysioTreadmill: An Auto-Controlled Treadmill Featuring Physiological-Data-Driven Visual/Audio Feedback

2020 ◽  
Author(s):  
Shaolong Liu ◽  
Xingce Wang ◽  
Zhongke Wu ◽  
Ying He
Keyword(s):  
Data Driven ◽  
Physiological Data ◽  
Audio Feedback

scholarly journals Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jari Lipsanen ◽  
Liisa Kuula ◽  
Marko Elovainio ◽  
Timo Partonen ◽  
Anu-Katriina Pesonen
Keyword(s):  
Skin Surface ◽  
Living Conditions ◽  
Data Driven ◽  
Physiological Data ◽  
Free Living ◽  
Physiological Level ◽  
Temperature Rhythm ◽  
Time Series Modelling ◽  
Circadian Preference ◽  
Free Living Conditions

AbstractThe individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series modelling to characterize distinct profiles of the circadian rhythm measured from skin surface temperature in free-living conditions. We demonstrate the existence of three distinct clusters of individuals which differed in their circadian temperature profiles. The cluster with the highest temperature amplitude and the lowest midline estimating statistic of rhythm, or rhythm-adjusted mean, had the most regular and early-timed sleep–wake rhythm, and was the least probable for those with a concurrent delayed sleep phase, or eveningness chronotype. While the clusters associated with the observed sleep and circadian preference patterns, the entirely unsupervised modelling of physiological data provides a novel basis for modelling and understanding the human circadian functions in free-living conditions.

scholarly journals A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

2018 ◽  
Vol 3 (2) ◽  
pp. 245-263
Author(s):  
Franco van Wyk ◽  
Anahita Khojandi ◽  
Brian Williams ◽  
Don MacMillan ◽  
Robert L. Davis ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Data Driven ◽  
Physiological Data ◽  
Benefit Analysis ◽  
Data Acquisition Systems ◽  
Using Data ◽  
Data Driven Modeling

A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification

2019 ◽  
Vol 50 (11) ◽  
pp. 2046-2064
Author(s):  
Junqi Guo ◽  
Yazhu Dai ◽  
Chixiang Wang ◽  
Hao Wu ◽  
Tianyou Xu ◽  
...  
Keyword(s):  
Cognitive Load ◽  
Feature Fusion ◽  
Data Driven ◽  
Physiological Data ◽  
Load Detection

scholarly journals Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3843
Author(s):  
Kar Fye Alvin Lee ◽  
Woon-Seng Gan ◽  
Georgios Christopoulos
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Nervous Activity ◽  
Self Report ◽  
Data Driven ◽  
Psychological State ◽  
Research Progress ◽  
Physiological Data ◽  
Learning Approaches ◽  
Cognitive Fatigue

Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

A Tale of Two Colons and Two Cancers

Swiss Surgery ◽  
2003 ◽  
Vol 9 (1) ◽  
pp. 3-7 ◽  
Author(s):  
Gervaz ◽  
Bühler ◽  
Scheiwiller ◽  
Morel
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Adjuvant Chemotherapy ◽  
Splenic Flexure ◽  
Chromosomal Instability ◽  
Proximal Colon ◽  
Colorectal Carcinogenesis ◽  
Physiological Data ◽  
Central Hypothesis ◽  
Group Stratification

The central hypothesis explored in this paper is that colorectal cancer (CRC) is a heterogeneous disease. The initial clue to this heterogeneity was provided by genetic findings; however, embryological and physiological data had previously been gathered, showing that proximal (in relation to the splenic flexure) and distal parts of the colon represent distinct entities. Molecular biologists have identified two distinct pathways, microsatellite instability (MSI) and chromosomal instability (CIN), which are involved in CRC progression. In summary, there may be not one, but two colons and two types of colorectal carcinogenesis, with distinct clinical outcome. The implications for the clinicians are two-folds; 1) tumors originating from the proximal colon have a better prognosis due to a high percentage of MSI-positive lesions; and 2) location of the neoplasm in reference to the splenic flexure should be documented before group stratification in future trials of adjuvant chemotherapy in patients with stage II and III colon cancer.

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