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This paper proposes a reliable stress and relaxation level estimation algorithm that is implemented in a portable, low-cost hardware device and executed in real time. The main objective of this work is to offer an affordable and ''ready-to-go'' solution for medical and personal environments, in which the detection of the arousal level of a person is crucial. Methods: To achieve meaningful identification of stress and relaxation, a fuzzy algorithm based on expert knowledge is built according to parameters extracted from physiological records. In addition to the heart rate, parameters extracted from the galvanic skin response and breath are employed to extend the results. Moreover, this algorithm achieves accurate results with a restricted computational load and can be implemented in a miniaturized low-cost prototype. The developed solution includes standard and actively shielded electrodes that are connected to an Arduino device for acquisition, while parameter extraction and fuzzy processing are conducted with a more powerful Raspberry Pi board. The proposed solution is validated using real physiological registers from 42 subjects collected using BIOPAC MP36 hardware. Additionally, a real-time acquisition, processing and remote cloud storage service is integrated via IoT wireless technology. Results: Robust identification of stress and relaxation is achieved, with F1 scores of 91.15% and 96.61%, respectively. Moreover, processing is performed using a 20-second sliding window; thus, only a small frame of context is required. Significance: This work presents a reliable solution for identifying stress and relaxation levels in real time, which can lead to the production of low-cost commercial devices for use in medical and personal environments.

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