1,720,992 research outputs found
Wearable Sensor for Boxer Performance Improvement
No full textMeasuring punch force is crucial for assessing the performance and progress of boxers during training and matches. In this paper, we present a novel wearable sensor designed specifically to measure punch force in boxers. The sensor is a unique example of a measuring wearable device that can be easily integrated into commercial boxing gloves, making it suitable for both training and matches. The module is lightweight, compact, and fits into commercial gloves without compromising comfort or mobility. Moreover, the sensor incorporates wireless communication capabilities, enabling real-time monitoring of punch force data on a companion mobile application or a dedicated display unit, facilitating immediate feedback and analysis. We conducted tests with four amateur boxers, and we chose the boxers trying to cover a wide range of standard categories. The results demonstrate that the sensor reliably measures punch force across different boxing techniques such as straights and hooks, with accuracy in the order of 6 % of full scale. The presented wearable sensor represents a significant advancement in wearable sensor technology for boxing; its integration into commercial gloves allows for seamless adoption by boxers of all skill levels, enhancing training efficiency and promoting better performance during matches
Advanced Necklace for Real-Time PPG Monitoring in Drivers
Full text linkMonitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects’ movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver’s well-being by providing information about the driver’s physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace’s design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor’s performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer’s algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads
Low-complexity classification algorithm to identify drivers’ stress using electrodermal activity (EDA) measurements
No full textExploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving †
Full text linkIn this paper, we propose a relatively noninvasive system that can automatically assess the impact of traffic conditions on drivers. We analyze the physiological signals recorded from a set of individuals while driving in a simulated urban scenario in two different traffic scenarios, i.e., with traffic and without traffic. The experiments were carried out in a laboratory located at the University of Udine, employing a driving simulator equipped with a moving platform. We acquired two Skin Potential Response (SPR) signals from the hands of the drivers, and an electrocardiogram (ECG) signal from their chest. In the proposed scheme, the SPR signals are then processed through a Motion Artifact (MA) removal algorithm such that possible motion artifacts arising during the drive are reduced. An analysis considering the scalogram of the single cleaned SPR signal is presented. This signal, along with the ECG, is then fed to various Machine Learning (ML) algorithms. More specifically, some statistical features are extracted from each signal segment which, after being analyzed through a binary ML model, are labeled as corresponding to a stressful situation or not. Our results confirm the applicability of the proposed approach to identify stress in the two scenarios. This is also in accordance with our findings considering the SPR signal scalograms
Cross-Layer Joint Optimization of FEC channel codes and Multiple Description Coding for video delivery over IEEE 802.11e links
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