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Abstract:
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Occupational stress is a relevant problem in contemporary work environments, affecting
employee well-being and organizational performance. Wearable sensing and
artificial intelligence can support continuous stress monitoring by analyzing physiological
signals, but the centralized collection of these sensitive data raises privacy and
security concerns. In this context, this study proposed and evaluated federated learning
as a privacy-preserving approach for detecting and monitoring occupational stress.
The work combines a rapid literature review with a practical prototype that integrates
wearable sensing, a mobile privacy gateway, anonymized communication, authenticated
data submission, and local model training. The review discusses the relevance
of occupational stress, the role of wearable technologies and artificial intelligence in
stress monitoring, and the privacy risks associated with centralized physiological data
collection.
For the practical implementation, a layered Internet of Medical Things (IoMT) architecture
was developed in which ESP32-based wearable devices transmit physiological
signals to an owner-authorized mobile application through authenticated Bluetooth
Low Energy bonding. The mobile application stores provisioning credentials
securely, signs outgoing JSON payloads with HMAC-SHA256, and forwards authenticated
requests through the Tor anonymity network to a local training node hosted at
a healthcare institution. The stress classification component detects general physiological
stress states (amusement, baseline, stress) from the WESAD laboratory protocol,
not specific occupational stressors; therefore, the occupational stress framing is
the motivating application context, not the validated experimental scope. Within this
scope, local model training was validated on the WESAD dataset using a regularized
tabular Multilayer Perceptron (MLP), with the physiological data used for classifier
training and validation originating exclusively from the WESAD benchmark dataset,
not from the prototype sensors; this model achieved a validation macro F1-score of
0.9565 for three-class stress classification. The ESP32-based sensing hardware and
mobile privacy gateway were functionally validated for data transmission and authentication.
The proposed model was compared with an original MLP baseline and alternative
training strategies, using accuracy, precision, recall, macro F1-score, validation
loss, and overfitting gap as evaluation criteria. |
