Background
Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices.
Methods
Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed.
Results
The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively.
Conclusion
The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.
In modern societies new, lifestyle related chronic diseases are appearing, affecting more and more people. Besides decreasing the quality of life for these patients, their treatments require increasing financial and social support from governments (and in many cases, even from the patients themselves). Apart from socioeconomic concerns, another serious problem is the increasing shortage of experts (e.g. doctors, dietitians, ergonomists) that could help people, as the need for them is growing faster than their numbers. In this paper, a framework for creating expert systems capable of containing and properly using the knowledge of such experts, for providing help to users in acquiring and maintaining a healthy lifestyle, is presented. By selecting two different areas, diet and physical oriented lifestyle management and workplace related ergonomics, the effectiveness of such systems is tested.
Modern societies are dominated by computer-based work. As a result, people tend to be seated for most of their working life. Prolonged sedentariness is known to significantly increase the risk of developing unwanted conditions. This paper presents the development of a rule-based expert system module using CLIPS that provides ergonomic assessment. The system was validated by evaluating pre-recorded user logs from real-life office environments. The tests showed that the system is able to perform the required basic assessment functionality, thus, the implementation of more complex features to advance its development is viable.
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