David Asiain

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.

This work presents an analysis of performance and multiple parameters of MEMS capacitive accelerometers in applications with large thermal variations and the effects of the soldering process on them. The proposed test consists of a thermal characterization phase performed between two mechanical calibrations. The test is performed on multiple units before and after the soldering process. Mechanical, thermal, and performance parameters are analyzed and compared among all tests. The ranges and relative variations of these characteristics, both during the soldering process and the tests, have been identified and characterized individually. Mechanical bias shows greater variability than other parameters in both the soldering process and thermal tests. On the contrary, the thermal characteristic parameters show great stability in all cases. The thermal drifts, which are the main source of error in environments with large thermal variations, are successfully compensated for using a model with only two characteristic parameters. According to the observed behaviors, negative thermal variations (towards cooler temperatures) might be more suitable for thermal calibration due to other effects, such as creep, taking place primarily at hotter temperatures. The creep effect at constant temperature is analyzed according to the Kelvin-Voigt model with promising results, and a possible link between thermal drift and creep effects is presented. Performance results are calculated in multiple compensation scenarios. Using the proposed compensation techniques, the average maximum error is reduced from over 70 mg to 7 mg and the uncertainty is also reduced to a third of the initial value.

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.

Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight algorithm by knowing the characteristic thermal parameters of the accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary. In this work, a simple and fast calibration method that allows the characteristic parameters of the three axes to be obtained simultaneously through a single test is proposed. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the compensation parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This allows it to be run in real time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six tests in orthogonal directions and a thermal chamber with a relative error difference of 0.3%.

Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight algorithm by knowing the characteristic thermal parameters of the accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary. In this work, a simple and fast calibration method that allows the characteristic parameters of the three axes to be obtained simultaneously through a single test is proposed. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the compensation parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This allows it to be run in real time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six-sided orthogonal die and a thermal camera. The average difference between the compensations according to both techniques is 0.32 mg/°C, calculated on an acceleration of 1 G; the maximum deviation being 0.6 mg/°C.

This paper presents a wireless traffic flow detection system, mainly focused on conditions in which the traffic flow is slow or stopped, which increases the risk of highway accidents. To achieve this goal, a Low Power Wide Area Network (LPWAN) based on LoRa called Short LoRa has been developed. This LoRa sub-network complies with the European Telecommunications Standards Institute (ETSI) harmonized standard for its compatibility in Europe countries. In addition, the development of the devices has allowed them to also work on a LoRaWAN network. The introduced development has been compared to a reference system mounted with laser barriers that provided a high accurate comparison. Field tests of the system have been carried out and the data obtained in the measurement has been analyzed with two different methods, and both of them were valid for the application. The results can determine vehicle speed with adequate precision at low speeds. The attenuating behavior of the communication signal is also analyzed through the Radio Signal Strength Indicator (RSSI). The relationship between vehicle speed, gate distances and RSSI attenuation has been studied. The system is proven to have efficient results in detecting traffic flow under the conditions for which it has been developed.

This work presents a portable non-invasive external fixator to assess and monitor fracture healing in real time. To evaluate the potential of this fixator, a transverse osteotomy was performed in the tibia of six adult sheep (mean age 3 ± 0.5 years and weight 63 ± 5 kg). The fractures were stabilized by a specially designed unilateral external fixator, which was instrumented by means of a set of strain gauges. Strains in the external surface of the fixator were monitored during all the healing process. A wireless, remote monitoring of the implant was developed through a specially designed external telemetric device. The strain gauges were arranged in two different half-bridge Wheatstone configurations, allowing easy post-processing of the signal. Thus, bending loads were measured in two planes of the external fixator acting as a load cell. The load through the fixator was evaluated for the gait cycle during all the healing process. Full weight bearing of the injured leg was observed from the beginning. The load transmission mechanism in the fixator was quite similar in all operated tibias and radiographic images showed a successful healing in all animals. Although the fixator has only been tested in an animal model, after further testing this system may have clinical potential.