A Low Cost IoT Enabled Device for the Monitoring, Recording and Communication of Physiological Signals

Despite that graphene has been extensively used in flexible wearable sensors, it remains an unmet need to fabricate a graphene-based sensor by a simple and low-cost method. Here, graphene nanoplatelets (GNPs) are prepared by thermal expansion method, and a sensor is fabricated by sealing of a graphene sheet with polyurethane (PU) medical film. Compared with other graphene-based sensors, it greatly simplifies the fabrication process and enables the effective measurement of signals. The resistance of graphene sheet changes linearly with the deformation of the graphene sensor, which lays a solid foundation for the detection of physiological signals. A signal processing circuit is developed to output the physiological signals in the form of electrical signals. The sensor was used to measure finger bending motion signals, respiration signals and pulse wave signals. All the results demonstrate that the graphene sensor fabricated by the simple and low-cost method is a promising platform for physiological signal measurement.

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Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Electronics ◽

10.3390/electronics11010155 ◽

2022 ◽

Vol 11 (1) ◽

pp. 155

Author(s):

Juan Antonio Castro-García ◽

Alberto Jesús Molina-Cantero ◽

Isabel María Gómez-González ◽

Sergio Lafuente-Arroyo ◽

Manuel Merino-Monge

Keyword(s):

Electrodermal Activity ◽

Low Cost ◽

Physiological Signals ◽

Body Area Network ◽

Area Network ◽

Body Area ◽

Battery Lifetime ◽

Open Hardware ◽

Custom Made ◽

Human Stress

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

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Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

Advances in Cartography and GIScience of the ICA ◽

10.5194/ica-adv-2-9-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-8

Author(s):

Kalliopi Kyriakou ◽

Bernd Resch

Keyword(s):

Spatial Analysis ◽

Urban Areas ◽

Hot Spot ◽

Low Cost ◽

Field Studies ◽

Physiological Signals ◽

Sensor Data ◽

Skin Response ◽

Density Analysis ◽

Physiological Sensors

Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

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An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Sensors ◽

10.3390/s21051777 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1777

Author(s):

Ana Serrano-Mamolar ◽

Miguel Arevalillo-Herráez ◽

Guillermo Chicote-Huete ◽

Jesus G. G. Boticario

Keyword(s):

Real World ◽

Markov Models ◽

Low Cost ◽

High Accuracy ◽

Physiological Signals ◽

Detection Accuracy ◽

Related Information ◽

Additional Control ◽

Affect Detection ◽

Potential Use

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.

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A Low-Cost Multichannel Preamplifier for Physiological Signals

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.1987.326092 ◽

1987 ◽

Vol BME-34 (4) ◽

pp. 307-310 ◽

Cited By ~ 22

Author(s):

H. W. Smit ◽

K. Verton ◽

C. A. Grimbergen

Keyword(s):

Low Cost ◽

Physiological Signals

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Rapid Fabrication of Epidermal Paper-Based Electronic Devices Using Razor Printing

Micromachines ◽

10.3390/mi9090420 ◽

2018 ◽

Vol 9 (9) ◽

pp. 420 ◽

Cited By ~ 7

Author(s):

Behnam Sadri ◽

Debkalpa Goswami ◽

Ramses Martinez

Keyword(s):

Low Cost ◽

Electronic Devices ◽

Cost Effective ◽

Physiological Signals ◽

Manufacturing Processes ◽

Wireless Power ◽

Fast Mapping ◽

High Moisture ◽

Mechanical Reinforcement ◽

Rapid Fabrication

This work describes the use of a benchtop razor printer to fabricate epidermal paper-based electronic devices (EPEDs). This fabrication technique is simple, low-cost, and compatible with scalable manufacturing processes. EPEDs are fabricated using paper substrates rendered omniphobic by their cost-effective silanization with fluoroalkyl trichlorosilanes, making them inexpensive, water-resistant, and mechanically compliant with human skin. The highly conductive inks or thin films attached to one of the sides of the omniphobic paper makes EPEDs compatible with wearable applications involving wireless power transfer. The omniphobic cellulose fibers of the EPED provide a moisture-independent mechanical reinforcement to the conductive layer. EPEDs accurately monitor physiological signals such as ECG (electrocardiogram), EMG (electromyogram), and EOG (electro-oculogram) even in high moisture environments. Additionally, EPEDs can be used for the fast mapping of temperature over the skin and to apply localized thermotherapy. Our results demonstrate the merits of EPEDs as a low-cost platform for personalized medicine applications.

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Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals

Micromachines ◽

10.3390/mi11070672 ◽

2020 ◽

Vol 11 (7) ◽

pp. 672

Author(s):

Lun-De Liao ◽

Yuhling Wang ◽

Yung-Chung Tsao ◽

I-Jan Wang ◽

De-Fu Jhang ◽

...

Keyword(s):

Low Cost ◽

Object Oriented ◽

Adverse Outcomes ◽

Object Oriented Programming ◽

Wearable Device ◽

Physiological Signals ◽

System Stability ◽

Open Architecture ◽

Sleep Monitoring ◽

Plug And Play

We present a wearable device built on an Adafruit Circuit Playground Express (CPE) board and integrated with a photoplethysmographic (PPG) optical sensor for heart rate monitoring and multiple embedded sensors for medical applications—in particular, sleep physiological signal monitoring. Our device is portable and lightweight. Due to the microcontroller unit (MCU)-based architecture of the proposed device, it is scalable and flexible. Thus, with the addition of different plug-and-play sensors, it can be used in many applications in different fields. The innovation introduced in this study is that with additional sensors, we can determine whether there are intermediary variables that can be modified to improve our sleep monitoring algorithm. Additionally, although the proposed device has a relatively low cost, it achieves substantially improved performance compared to the commercially available Philips ActiWatch2 wearable device, which has been approved by the Food and Drug Administration (FDA). To assess the reliability of our device, we compared physiological sleep signals recorded simultaneously from volunteers using both our device and ActiWatch2. Motion and light detection data from our device were shown to be correlated to data simultaneously collected using the ActiWatch2, with correlation coefficients of 0.78 and 0.89, respectively. For 7 days of continuous data collection, there was only one instance of a false positive, in which our device detected a sleep interval, while the ActiWatch2 did not. The most important aspect of our research is the use of an open architecture. At the hardware level, general purpose input/output (GPIO), serial peripheral interface (SPI), integrated circuit (I2C), and universal asynchronous receiver-transmitter (UART) standards were used. At the software level, an object-oriented programming methodology was used to develop the system. Because the use of plug-and-play sensors is associated with the risk of adverse outcomes, such as system instability, this study heavily relied on object-oriented programming. Object-oriented programming improves system stability when hardware components are replaced or upgraded, allowing us to change the original system components at a low cost. Therefore, our device is easily scalable and has low commercialization costs. The proposed wearable device can facilitate the long-term tracking of physiological signals in sleep monitoring and related research. The open architecture of our device facilitates collaboration and allows other researchers to adapt our device for use in their own research, which is the main characteristic and contribution of this study.

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Low cost system for monitoring physiological signals using FPGA and Android tablet

2017 International Caribbean Conference on Devices, Circuits and Systems (ICCDCS) ◽

10.1109/iccdcs.2017.7959711 ◽

2017 ◽

Author(s):

J. Bucheli ◽

D. Rivas ◽

J. Gavilema ◽

D. Mullo ◽

J. L. Carrillo ◽

...

Keyword(s):

Low Cost ◽

Physiological Signals ◽

Cost System

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Wireless Low Cost Patient Monitoring System

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-757 ◽

2021 ◽

pp. 21-28

Author(s):

M. Gnanathilaka Shofana M ◽

S. Kavitha ◽

A. Pavithra ◽

Dr. V. Vijayarangan

Keyword(s):

Real Time ◽

Monitoring System ◽

Patient Monitoring ◽

Low Cost ◽

Medical Personnel ◽

Well Being ◽

Physiological Signals ◽

Old People ◽

Medical Field ◽

Patient Monitoring System

In this ameliorating vogue of medical field, the impending danger of pandemic had driven us into gloom. The doctors can’t monitor each and every patient by coming near to them at all time. This leads to inconvenience for patient and made hospital management to feel helplessness about the problem. Though Conventional PMS monitors the physiological signals constantly but failed in providing it to the medical personnel during emergencies in real time. Other issues with traditional PMS is that most are heavy and bulky machines. Hence we propose to develop a low cost, portable PMS which measures the physiological signals of the patient with minimal intervention and contactless, providing correct and reliable data at the same time. Thus, we proposed a portable real time device that obtains various biological signals from the patients using electronic sensor network.. The acquired signals are processed and displayed in OLED display. Apart from this the emergency signals are transmitted and received wirelessly to the medical professionals for speedy necessary action without the intervention of them self. The proposed PMS system could be connected to a internet in a wireless mode , so that the patients will be free to move while their physiological signals are monitored. Thus, the critical condition of the patient can be informed to doctors and they could be treated promptly if any emergency occurs. This is suitable for the critical care of patients suffering from COVID -19 and monitoring the well being of old people who are at home and need constant monitoring. This is why we call our device low cost Wireless Patient Monitoring System (WPMS).

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