In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat’s score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.

Nonlinearity of a silicon resonator can lead to improved phase-noise performance in an oscillator when the phase shift of the sustaining amplifier forces the operating point to a steeper phase-frequency slope. As a result, phase modulation on the oscillator frequency is minimized because the resonator behaves as a high-order phase filter. The effect of the increased filtering translates into phase-noise shaping that reflects superior overall performance. Nonlinear effects in MEMS oscillators can be induced via sufficient driving power, generating low-frequency nonwhite noise processes that need to be considered in a phase-noise description. Since the phase-frequency response is not symmetric for a nonlinear detuned resonator, an empirical model based on power series is proposed to describe its effect in the noise sources and to account for the observed higher effective quality factor of the oscillator, the reduction in the corner frequency, and elevated levels of flicker noise very close-to-carrier. The applicability of the presented phase-noise model is shown for three piezoelectric MEMS oscillators, producing a relative fitting error below 1% in all cases.

This paper reports on the design, implementation and characterization of a low phase-noise 27 MHz MEMS oscillator with sub-ppm temperature instability based on a high-Q composite bulk acoustic wave (BAW) resonator. An array of silicon dioxide (SiO ₂ ) pillars has been uniformly embedded in the body of a piezoelectrically transduced silicon resonator to compensate its negative temperature coefficient of frequency (TCF). Using this technique, an overall frequency drift of 83 ppm is achieved for the resonator over the temperature range of -20°C to 100°C while resonator Q remains greater than 7,500 in atmospheric pressure. An electronically compensated oscillator using this resonator exhibits sub-ppm temperature instability with a consistent phase noise (PN) behavior over the entire temperature range and a value of -101 dBc/Hz at 1 kHz offset-frequency. Long-term stability measurement has been carried out for both temperature-compensated resonator and oscillator in an environmental chamber to study their stability over time.

This paper presents an energetically autonomous IoT sensor powered via thermoelectric harvesting. The operation of thermal harvesting is based on maintaining a temperature gradient of at least 26.31 K between the thermoelectric-generator sides. While the hot side employs a metal plate, the cold side is attached with a phase-change material acting as an effective passive dissipative material. The desired temperature gradient allows claiming power conversion efficiencies of about 26.43%, without efficiency reductions associated with heating and soiling. This work presents the characterization of a low-cost off-the-shelf thermoelectric generator that allows estimating the production of at least 407.3 mW corresponding to 2.44 Wh of available energy considering specific operation hours—determined statistically for a given geographic location. Then, the energy production is experimentally verified with the construction of an outdoor IoT sensor powered by a passively-cooled thermoelectric generator. The prototype contains a low-power microcontroller, environmental sensors, and a low-power radio to report selected environmental variables to a central node. This work shows that the proposed supply mechanism provides sufficient energy for continuous operation even during times with no solar resource through an on-board Li-Po battery. Such a battery can be recharged once the solar radiation is available without compromising sensor operation.

This paper presents the Hybrid Renewable Energy System (HYRES), a powerful tool to contribute to the viability analysis of energy systems involving renewable generators. HYRES considers various input parameters related to climatic conditions, statistical reliability, and economic views; in addition to offering multi-objective optimizations using Genetic Algorithms (GAs) that have a better cost-benefit ratio than mono-objective optimization, which is the technique used in several commercial systems like HOMER, a worldwide leader in microgrid modeling. The use of intelligent techniques in HYRES allows optimal sizing of hybrid renewable systems with wind and solar energy generators adapted to different conditions and case studies. The elements that affect the system design like buying and selling energy from/to the grid and the use of storage units can be included in system configuration according to the need. Optimization approaches are selectable and include Initial Cost, Life Cycle Cost, Loss of Power Probability, and Loss of Power Supply Probability.

Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented.