Exploration of Edge Machine Learning-based Stress Detection Using Wearable Devices

Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT technology, smart wearables can handle diverse operations, including networking and recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying smartphones now have data processing capability. Edge computing on such devices enables real-time feedback and in turn preemptive identification of reactions to stress. This can provide an opportunity to prevent more severe consequences that might result if stress is unaddressed. From a system perspective, leveraging edge computing allows saving energy such as network bandwidth and latency since it processes data in proximity to the data source. It can also strengthen privacy by implementing stress prediction at local devices without transferring personal information to the public cloud. This thesis presents a framework for real-time stress prediction using Fitbit and machine learning with the support from cloud computing. Fitbit is a wearable tracker that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate fluctuations and corresponding stress levels entered by users. I also established the dataset collected from police cadets during their academy training program. Machine learning classifiers for stress prediction are built using classic models and TensorFlow in the cloud. Lastly, the classifiers are optimized using model compression techniques for deploying them on the smartphones and analyzed how efficiently stress prediction can be performed on the edge.