Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data

Ryan Holder, Ramesh Kumar Sah, Michael Cleveland, Hassan Ghasemzadeh

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Detecting stress from wearable sensor data enables those struggling with unhealthy stress coping mechanisms to better manage their stress. Previous studies have investigated how mechanisms for detecting stress from sensor data can be optimized, comparing alternative algorithms and approaches to find the best possible outcome. One strategy to make these mechanisms more accessible is to reduce the number of sensors that wearable devices must support. Reducing the number of sensors will enable wearable devices to be a smaller size, require less battery, and last longer, making use of these wearable devices more accessible. To progress towards this more convenient stress detection mechanism, we investigate how learning algorithms perform on singular modalities and compare the outcome with results from multiple modalities. We found that singular modalities performed comparably or better than combined modalities on two stress-detection datasets, suggesting that there is promise for detecting stress with fewer sensor requirements. From the four modalities we tested, acceleration, blood volume pulse, and electrodermal activity, we saw acceleration and electrodermal activity to stand out in a few cases, but all modalities showed potential. Our results are acquired from testing with random holdout and leave-one-subject-out validation, using several machine learning techniques. Our results can inspire work on optimizing stress detection with singular modalities to make the benefits of these detection mechanisms more convenient.

Original languageEnglish (US)
Pages (from-to)557-562
Number of pages6
JournalProceedings - IEEE Consumer Communications and Networking Conference, CCNC
StatePublished - 2022
Externally publishedYes
Event19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States
Duration: Jan 8 2022Jan 11 2022


  • machine learning
  • stress detection
  • wearable sensors

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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