Sensors

Researchers have widely used commercial and alternative measurement systems in impedance-based structural health monitoring (SHM), where a piezoelectric transducer attached to the monitored structure is excited while its electrical impedance is measured. However, the literature lacks sufficient attention to the required number of cycles of the excitation signal, an issue frequently overlooked by researchers.

As the field of human-computer interaction continues to evolve, there is a growing need for new methods of gesture recognition that can be used in a variety of applications, from gaming and entertainment to healthcare and robotics. While traditional methods of gesture recognition rely on cameras or other optical sensors, these systems can be limited by factors such as lighting conditions and occlusions.

This is a new dataset, including behavioral, biometric, and environmental data, obtained from 39 subjects each spending 1 week to 2 months in smart rooms in Tokyo, Japan. The approximate duration of the experiment is 3 years. This dataset includes personal data, such as the use of home appliances, heartbeat rate, sleep status, temperature, illumination, and meal data. Although there are many datasets that publish these data individually, datasets that publish them all at once, tied to individual IDs, are valuable.

In situations when the precise position of a machine is unknown, localization becomes crucial. It is crucial to identify and ascertain the machine's position. This research focuses on improving the position prediction accuracy over long-range networks using a unique machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting approach using LoRa technology, this study suggested an ML-based algorithm.