Physical Sciences and Mathematics Commons^™

Evaluating The Reproducibility Of Physiological Stress Detection Models, Varun Mishra, Sougata Sen, Grace Chen, Tian Hao, Jeffrey Rogers, Ching-Hua Chen, David Kotz

Dartmouth Scholarship

Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics.

This paper takes …

Automatic Recognition, Segmentation, And Sex Assignment Of Nocturnal Asthmatic Coughs And Cough Epochs In Smartphone Audio Recordings: Observational Field Study, Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, Tobias Kowatsch

Dartmouth Scholarship

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio …

Utilizing Neural Networks And Wearables To Quantify Hip Joint Angles And Moments During Walking And Stair Ascent, Megan V. Mccabe

ENGS 88 Honors Thesis (AB Students)

Wearable sensors were leveraged to develop two methods for computing hip joint angles and moments during walking and stair ascent that are more portable than the gold standard. The Insole-Standard (I-S) approach replaced force plates with force-measuring insoles and achieved results that match the curvature of results from similar studies. Peaks in I-S kinetic results are high due to error induced by applying the ground reaction force to the talus. The Wearable-ANN (W-A) approach combines wearables with artificial neural networks to compute the same results. Compared against the I-S, the W-A approach performs well (average rRMSE = 18%, R^{2 …}