This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user’s face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.

This paper introduces a novel wearable device, the Multi-sensor Wearable Wristband (MsW²), designed to measure electrodermal activity (EDA) and skin temperature using constant AC current instead of the usual constant AC voltage measurement method. Adapted from a previous headband model, MsW² has been optimized for wrist-based applications. Its efficacy was validated through experiments with 25 participants, comparing its EDA measurements with the Empatica E4 device. The results demonstrated high coherence between the two systems. Additionally, a simplified skin impedance model was proposed and validated, providing a reliable means of analyzing EDA data. The wearable demonstrates significant potential for applications in emotion detection, health monitoring, and more.

In this study, a new method of blind audio source separation (BASS) of monaural musical harmonic notes is presented. The input (mixed notes) signal is processed using a flexible analysis and synthesis algorithm (complex wavelet additive synthesis, CWAS), which is based on the complex continuous wavelet transform. When the harmonics from two or more sources overlap in a certain frequency band (or group of bands), a new technique based on amplitude similarity criteria is used to obtain an approximation to the original partial information. The aim is to show that the CWAS algorithm can be a powerful tool in BASS. Compared with other existing techniques, the main advantages of the proposed algorithm are its accuracy in the instantaneous phase estimation, its synthesis capability and that the only input information needed is the mixed signal itself. A set of synthetically mixed monaural isolated notes have been analyzed using this method, in eight different experiments: the same instrument playing two notes within the same octave and two harmonically related notes (5th and 12th intervals), two different musical instruments playing 5th and 12th intervals, two different instruments playing non-harmonic notes, major and minor chords played by the same musical instrument, three different instruments playing non-harmonically related notes and finally the mixture of a inharmonic instrument (piano) and one harmonic instrument. The results obtained show the strength of the technique.

Continuous non-invasive health monitoring requires wearable devices capable of capturing synchronized, multi-parametric physiological data without compromising user comfort or signal integrity. However, existing platforms often suffer from insufficient accuracy in complex modalities such bioimpedance. To address these limitations, this paper introduces the Multi-sensor Wearable Wristband Version 2.0 (${\mathrm{MsW}}^2$-v2), an optimized, highly integrated platform designed for high-precision measurements. The methodology encompasses the hardware optimization of a previous prototype, integrating a low-leakage switching matrix and dual-bandwidth analog front ends to capture, in a quasi-simultaneous (time-multiplexed) manner, electrodermal activity (EDA) and bioimpedance (BiZ), as well as other physiological signals such as skin temperature, oxygen saturation, heart rate, and inertial motion. System validation was performed through rigorous laboratory calibration against reference impedances and subsequent in vivo testing on a human volunteer in an isolated room with no external stimuli whatsoever. The laboratory results indicated high-precision performance, with the mean absolute errors of the measurements being below 3 $\mathsf{\Omega }$ in modulus and 0.2° in phase across a wide range of frequencies, from 1 kHz to 200 kHz. Preliminary in vivo benchmarks confirm the functionality of the device. These findings demonstrate that the ${\mathrm{MsW}}^2$-v2 successfully bridges the gap between high accuracy and wearable form factors, bringing us to the conclusion that this device could serve as a viable, scalable solution for real-time physiological measurements and biomedical signal monitoring.