The market of wearable devices has been growing over the past decades. Smart wearables are usually part of IoT (Internet of things) systems and include many functionalities such as physiological sensors, processing units and wireless communications, that are useful in fields like healthcare, activity tracking and sports, among others. The number of functions that wearables have are increasing all the time. This result in an increase in power consumption and more frequent recharges of the battery. A good option to solve this problem is using energy harvesting so that the energy available in the environment is used as a backup power source. In this paper, an energy harvesting system for solar energy with a flexible battery, a semi-flexible solar harvester module and a BLE (Bluetooth^® Low Energy) microprocessor module is presented as a proof-of-concept for the future integration of solar energy harvesting in a real wearable smart device. The designed device was tested under different circumstances to estimate the increase in battery lifetime during common daily routines. For this purpose, a procedure for testing energy harvesting solutions, based on solar energy, in wearable devices has been proposed. The main result obtained is that the device could permanently work if the solar cells received a significant amount of direct sunlight for 6 h every day. Moreover, in real-life scenarios, the device was able to generate a minimum and a maximum power of 27.8 mW and 159.1 mW, respectively. For the wearable system selected, Bindi, the dynamic tests emulating daily routines has provided increases in the state of charge from 19% (winter cloudy days, 4 solar cells) to 53% (spring sunny days, 2 solar cells).

Continuous physiological monitoring integrated within current wearable devices is a hot topic nowadays. Despite that, measuring physiological variables is still challenging due to intrinsic and extrinsic personal factors. This results in the need for smart, adjustable, and personalised sensing devices. Among the different physiological signals that can be measured, changes in skin conductance are extensively used in Affective Computing research. This measurement presents an unequivocal relationship with the sympathetic branch of the autonomous nervous system, which relates to emotional reactions. However, there is a lack of self-adjustable skin conductance sensors. This paper presents a novel skin conductance analog front-end to deal with individual physiological dynamics. We compared the performance of our system with other skin conductance sensor circuits presented in the literature. Moreover, the experimental results in this work come from the data collected in 47 women volunteers and are compared with the measurements with a reference physiological sensing system commonly used for validations. Finally, our system presents a competitive averaged sensitivity for a typical human EDA range compared to the current state-of-the-art solutions. Experimental results show that the system achieves an averaged sensitivity of up to 0.33 nS for a range 0 μS to 40 μS, with a relative error below 1% for fixed-resistance measurements, and a 0.94 median correlation coefficient when directly comparing with the reference sensor. Moreover, we extracted and analysed different physiological features in data from both sensors and obtained comparable results. Finally, this research is intended to boost the design and development of subject-independent and self-adjustable wearable sensors.