Cardio-respiratory and daily activity monitor based on FMCW Doppler radar embedded in a wheelchair.

Annu Int Conf IEEE Eng Med Biol Soc

Instituto de Telecomunicações AvRovisco Pais 1, 1049-001 Lisbon, Portugal.

Published: June 2012

Unobtrusive monitoring of the cardio-respiratory and daily activity for wheelchair users became nowadays an important challenge, considering population aging phenomena and the increasing of the elderly with chronic diseases that affect their motion capabilities. This work reports the utilization of FMCW (frequency modulated continuous wave) Doppler radar sensors embedded in a manual wheelchair to measure the cardiac and respiratory activities and the physical activity of the wheelchair user. Another radar sensor is included in the system in order to quantify the motor activity through the wheelchair traveled distance, when the user performs the manual operation of the wheelchair. A conditioning circuit including active filters and a microcontroller based primary processing module was designed and implemented to deliver the information through Bluetooth communication protocol to an Android OS tablet computer. The main capabilities of the software developed using Android SDK and Java were the signal processing of Doppler radar measurement channel signals, graphical user interface, data storage and Wi-Fi data synchronization with remote physiological and physical activity database.

Download full-text PDF

Source
http://dx.doi.org/10.1109/IEMBS.2011.6090542DOI Listing

Publication Analysis

Top Keywords

doppler radar
12
activity wheelchair
12
cardio-respiratory daily
8
daily activity
8
physical activity
8
wheelchair
6
activity
5
activity monitor
4
monitor based
4
based fmcw
4

Similar Publications

Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on esNet ong Short-Term Memory with an ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range-Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal.

View Article and Find Full Text PDF

Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals.

Sensors (Basel)

January 2025

Institute of Telecommunications, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland.

In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST).

View Article and Find Full Text PDF

Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks.

View Article and Find Full Text PDF

In shooting incident reconstructions, forensic examiners usually deal with scenes involving short-range trajectories, typically ≤30 m. In situations such as this, a linear trajectory reconstruction model is appropriate. However, a forensic expert can also be asked to estimate a shooter's position by reconstructing a long-range trajectory where the bullet's path becomes arced as a result of gravity and the greater time in flight.

View Article and Find Full Text PDF

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range-velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!