| Abstract: | Stress has become one of the most prominent problems of modern societies and a key contributor to major health issues. Dealing with stress effectively requires detecting it in real-time, informing the user, and giving instructions on how to manage it. Over the past few years, wearable devices equipped with biosensors that can be utilized for stress detection have become increasingly popular. Since they come with various designs and technologies and acquire biosignals from different body locations, choosing a suitable device for a particular application has become a challenge for researchers and end-users. This study compares seven common wearable biosensors for stress detection applications. This was accomplished by collecting physiological sensor data during Baseline, Stress, Recovery, and Cycling sessions from 32 participants. Machine learning algorithms were used to classify four stress classes, and the results obtained from all wearables were compared. Following this, a state-of-the-art explainable artificial intelligence method was employed to clarify our models’ predictions and investigate the influence different features have on the models’ outputs. Despite the results showing that ECG wearables perform slightly better than the rest of the devices, adding a second biosignal (EDA) improved the results significantly, tipping the balance toward multisensor wearables. Finally, we concluded that although the output results of each model can be affected by various factors, in most cases, there is no significant difference in the accuracy of stress detection by different wearables. However, the decision to select a particular wearable for stress detection applications must be made carefully considering the trade-off between the users’ expectations and preferences and the pros and cons of each device. |
