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234026
20260402T141856Z
DTSTART;TZID=America/New_York:20260420T130000
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 0260420T140000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/compu
 ter-science-department-ms-thesis-presentation-dinesh-j-kodwai-covidrhythmt
 ab-covid-19-infection
Computer Science Department , MS Thesis Presentation , DInesh J Kodwai, " CovidRhythmTab:
  COVID-19 Infection Detection Framework from Wearable Sensor Data using Ta
 bular Neural Networks to Analyze BioBehavioral Rhythms"
\nDinesh J Kodwani\nMS student\nWPI – Computer Science Department\nMonday, April 20, 2026\nTime:
  1:00 p.m. – 2:00 p.m.\nLocation: Fuller Labs 141\nAdvisor: Prof. Emmanu
 el Agu\nReader : Prof. Dmitry Korkin\nAbstract:\nThe SARS-Cov-2 virus caus
 ed the COVID-19 pandemic between 2020 and 2023. While the pandemic has end
 ed, new infections continue to manifest. Early infection detection can inf
 orm timely interventions to curb pandemics. While PCR tests are accurate, 
 their results are delayed and they require the patient to be present in-pe
 rson, which presents a burden. Passive, convenient, early infection detect
 ion methods are desirable to limit disease spread. Human bodies follow nat
 ural, periodic clocks or biorhythms that govern various functions includin
 g activity, body temperature, heart rate and sleep-wake cycles.\nPrior wor
 k has found that COVID-19 disrupted the natural body rhythms of infected p
 atients, even before biological symptoms fully manifest. Many smartwatches
 , which are substantially cheaper than PCR machines, can continuously meas
 ure physiological signals such as the heart rate and activity of users. Su
 ch smartwatch data and extracted features are often recorded in a tabular 
 data format that can be analyzed to detect diseases such as COVID-19.\nThe
  goal of this thesis is to investigate whether TabR, a Neural Network desi
 gned for tabular data can enhance the performance in pre-symptomatic predi
 ction of COVID-19 from disruptions in biobehavioral rhythms of the users.\
 nThis thesis proposes CovidRhythmTab, a neural network framework that util
 izes TabR to classify presymptomatic COVID-19 disease from smartwatch data
 . Rigorous evaluation was done including a comparison to state-of-the-art 
 baselines. To the best of our knowledge this is the first work to explore 
 the use of tabular neural networks to classify COVID-19 infection from sma
 rtwatch-based biobehavioral rhythmic features.\n
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