The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

Voice recognition has been widely used in various applications, especially in the field of security. In this paper, we propose the development of a low-cost security system based on voice recognition using artificial intelligence. The system utilizes a Raspberry Pi 4B as a microcontroller and Python as a programming language. The system works with a pre-recorded database of voices from 20 people, and the new user’s voice is matched against the pre-recorded voices using Gaussian Mixture Model (GMM). We extracted Mel-Frequency Cepstral Coefficients (MFCC) from the recorded voices, which were used to train the GMM. The system achieved an accuracy rate of 95.42%, with an equal error rate of 4.57%. The proposed system is low-cost and easy to use, making it accessible to a wider audience. However, it has some limitations, such as only being able to work with a pre-recorded database of voices.