scholarly journals On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2782 ◽  
Author(s):  
Jorge Blasco ◽  
Pedro Peris-Lopez
Keyword(s):  
Low Cost ◽  
Wearable Sensors ◽  
Area Under The Curve ◽  
Training Data ◽  
Biometric Verification ◽  
Biometric Systems ◽  
Skin Response ◽  
Device User ◽  
Verification Process ◽  
Electrocardiogram Ecg

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.

scholarly journals Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4448 ◽  
Author(s):  
Günther Sagl ◽  
Bernd Resch ◽  
Andreas Petutschnig ◽  
Kalliopi Kyriakou ◽  
Michael Liedlgruber ◽  
...  
Keyword(s):  
Heart Rate ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Quantified Self ◽  
High Quality ◽  
Laboratory Equipment ◽  
Skin Response ◽  
Physiological Sensors

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

scholarly journals Wearable Sensors for Assessing the Role of Olfactory Training on the Autonomic Response to Olfactory Stimulation

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 770
Author(s):  
Alessandro Tonacci ◽  
Lucia Billeci ◽  
Irene Di Mambro ◽  
Roberto Marangoni ◽  
Chiara Sanmartin ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Physiological Signals ◽  
Autonomic Response ◽  
Sensory Stimuli ◽  
Skin Response ◽  
Potential Applications ◽  
Electrocardiogram Ecg ◽  
The Relationship ◽  
Olfactory Training

Wearable sensors are nowadays largely employed to assess physiological signals derived from the human body without representing a burden in terms of obtrusiveness. One of the most intriguing fields of application for such systems include the assessment of physiological responses to sensory stimuli. In this specific regard, it is not yet known which are the main psychophysiological drivers of olfactory-related pleasantness, as the current literature has demonstrated the relationship between odor familiarity and odor valence, but has not clarified the consequentiality between the two domains. Here, we enrolled a group of university students to whom olfactory training lasting 3 months was administered. Thanks to the analysis of electrocardiogram (ECG) and galvanic skin response (GSR) signals at the beginning and at the end of the training period, we observed different autonomic responses, with higher parasympathetically-mediated response at the end of the period with respect to the first evaluation. This possibly suggests that an increased familiarity to the proposed stimuli would lead to a higher tendency towards relaxation. Such results could suggest potential applications to other domains, including personalized treatments based on odors and foods in neuropsychiatric and eating disorders.

scholarly journals Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3258
Author(s):  
Catherine Park ◽  
Ramkinker Mishra ◽  
Amir Sharafkhaneh ◽  
Mon S. Bryant ◽  
Christina Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Wearable Sensors ◽  
Area Under The Curve ◽  
Frailty Model ◽  
Screening Tools ◽  
Physical Frailty ◽  
Stand Test ◽  
Sit To Stand ◽  
Range Indicator

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

scholarly journals An Innovative, Unobtrusive Approach to Investigate Smartphone Interaction in Nonaddicted Subjects Based on Wearable Sensors: A Pilot Study

Medicina ◽  
2019 ◽  
Vol 55 (2) ◽  
pp. 37 ◽  
Author(s):  
Alessandro Tonacci ◽  
Lucia Billeci ◽  
Francesco Sansone ◽  
Antonella Masci ◽  
Anna Paola Pala ◽  
...  
Keyword(s):  
Resting State ◽  
Wearable Sensors ◽  
Smartphone Addiction ◽  
Entertainment Education ◽  
State Recovery ◽  
Skin Response ◽  
Study Population ◽  
Electrocardiogram Ecg ◽  
First Time

Background and objectives: Smartphones are playing a pivotal role in everyday life, due to the opportunity they grant in terms of simplifying communication, entertainment, education and many other daily activities. Against such positive characteristics, smartphone interaction can result, in particular cases, in dangerous smartphone addiction patterns, possibly leading to several long-term detrimental psychophysiological conditions. Therefore, this pilot aims at assessing the feasibility of using an innovative approach, based on unobtrusive wearable sensors, used for the first time in this specific topic, and psychological questionnaires, to investigate the links between stress and emotions in a group of young, nonaddicted individuals performing smartphone interaction. Materials and methods: 17 volunteers were enrolled for the present study. The study protocol was divided into three phases, with an initial resting state (baseline) of three minutes, a smartphone interaction session (task) of the same length, and a final resting state (recovery), lasting three minutes. In the overall procedure, electrocardiogram (ECG) and galvanic skin response (GSR) measurements, both monitored by wearable sensors, were acquired in order to assess the functioning of the autonomic nervous system (ANS). Results: A significant decrease was seen in pNN50 during the smartphone interaction with respect to the baseline (Z = −2.675, p = 0.007), whereas the Low-to-High Frequency (LF/HF) ratio at task was somewhat correlated with phubbing behaviors (r = 0.655, p = 0.029), assessed through dedicated questionnaires. Conclusions: Taken together with the slight changes in GSR data, such results suggest the feasibility of this approach to characterize the ANS activation during smartphone interaction among young individuals. Further studies should enlarge the study population and involve smartphone-addicted subjects in order to increase the scientific and clinical relevance of such findings.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

scholarly journals Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1393
Author(s):  
Uduak Z. George ◽  
Kee S. Moon ◽  
Sung Q. Lee
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Spline Interpolation ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Lung Sound ◽  
Health Monitoring System ◽  
Electrocardiogram Ecg

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

scholarly journals Transit pollution exposure monitoring using low-cost wearable sensors

2021 ◽  
Vol 98 ◽  
pp. 102981
Author(s):  
Naser Hossein Motlagh ◽  
Martha A. Zaidan ◽  
Pak L. Fung ◽  
Eemil Lagerspetz ◽  
Kasimir Aula ◽  
...  
Keyword(s):  
Low Cost ◽  
Wearable Sensors ◽  
Exposure Monitoring ◽  
Pollution Exposure

Pedometer-Determined Step-Count Guidelines for Afterschool Programs

2012 ◽  
Vol 9 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Michael W. Beets ◽  
Aaron Beighle ◽  
Matteo Bottai ◽  
Laura Rooney ◽  
Fallon Tilley
Keyword(s):  
Operating Characteristic ◽  
Low Cost ◽  
Vigorous Physical Activity ◽  
Area Under The Curve ◽  
High Specificity ◽  
Step Count ◽  
Afterschool Programs ◽  
Policy Goal ◽  
Community Based ◽  
Random Intercept

Background:Policies to require afterschool programs (ASPs, 3 PM to 6 PM) to provide children a minimum of 30 minutes of moderate-to-vigorous physical activity (MVPA) exist. With few low-cost, easy-to-use measures of MVPA available to the general public, ASP providers are limited in their ability to track progress toward achieving this policy-goal. Pedometers may fill this gap, yet there are no step-count guidelines for ASPs linked to 30 minutes of MVPA.Methods:Steps and accelerometer estimates of MVPA were collected concurrently over multiple days on 245 children (8.2 years, 48% boys, BMI-percentile 68.2) attending 3 community-based ASPs. Random intercept logit models and receiver operating characteristic (ROC) analyses were used to identify a threshold of steps that corresponded with attaining 30 minutes of MVPA.Results:Children accumulated an average of 2876 steps (standard error [SE] 79) and 16.1 minutes (SE0.5) of MVPA over 111 minutes (SE1.3) during the ASP. A threshold of 4600 steps provided high specificity (0.967) and adequate sensitivity (0.646) for discriminating children who achieved the 30 minutes of MVPA; 93% of the children were correctly classified. The total area under the curve was 0.919. Children accumulating 4600 steps were 25times more likely to accumulate 30 minutes of MVPA.Conclusions:This step threshold will provide ASP leaders with an objective, low-cost, easy-to-use tool to monitor progress toward policy-related goals.

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