Acquisition and Fuzzy Processing of Physiological Signals to Obtain Human Stress Level Using Low Cost Portable Hardware

2017 ◽  
pp. 68-78
Author(s):  
Unai Zalabarria ◽  
Eloy Irigoyen ◽  
Raquel Martínez ◽  
Javier Arechalde
Keyword(s):  
Stress Level ◽  
Low Cost ◽  
Physiological Signals ◽  
Human Stress

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

Electronics ◽  
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.

Relationship between rework of engineering drawing tasks and stress level measured from physiological signals

2021 ◽  
Vol 124 ◽  
pp. 103560
Author(s):  
Jeonghyeun Chae ◽  
Sungjoo Hwang ◽  
Wonkyoung Seo ◽  
Youngcheol Kang
Keyword(s):  
Stress Level ◽  
Physiological Signals ◽  
Engineering Drawing ◽  
Drawing Tasks

scholarly journals Detection of Physiological Signals Based on Graphene Using a Simple and Low-Cost Method

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1656 ◽  
Author(s):  
Liping Xie ◽  
Xingyu Zi ◽  
Qingshi Meng ◽  
Zhiwen Liu ◽  
Lisheng Xu
Keyword(s):  
Graphene Sheet ◽  
Pulse Wave ◽  
Low Cost ◽  
Wearable Sensors ◽  
Unmet Need ◽  
Expansion Method ◽  
Physiological Signals ◽  
Physiological Signal ◽  
Solid Foundation ◽  
Processing Circuit

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.

Stress Emotion Recognition Based on RSP and EMG Signals

2013 ◽  
Vol 709 ◽  
pp. 827-831 ◽  
Author(s):  
Chang Zhi Wei
Keyword(s):  
Emotion Recognition ◽  
Stress Level ◽  
Detection Rate ◽  
Physiological Signals ◽  
Correct Detection ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Emg Signal ◽  
Detection Of Stress ◽  
Linear Discriminant Classifier

To recognize the stress emotion, a subject was put alternately in periods of high and low stress by configuring the speed and difficulty of a game named Tetris. The respiration (RSP) signal and the electromyogram (EMG) signal with different stress level were then acquired. After preprocessing, the mathematical features were calculated and automatic detection of stress level based on Fisher linear discriminant classifier was realized. The results show that the average correct detection rate of stress level based on the EMG signal can reach 97.8%. That of the RSP signal is only 86.7%. The EMG signal is more effective than the RSP signal in detection of stress level. Union of multiple physiological signals can effectively improve the correct detection rate.

scholarly journals Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

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.

scholarly journals Performance evaluation of a pull type two rows granular urea applicator

2014 ◽  
Vol 12 (1) ◽  
pp. 211-220
Author(s):  
M Alam ◽  
TR Sarker ◽  
TA Orin
Keyword(s):  
Average Distance ◽  
Low Cost ◽  
Field Capacity ◽  
Application Rate ◽  
Effective Field ◽  
Manufacturing Cost ◽  
Deep Placement ◽  
Human Stress ◽  
Field Efficiency ◽  
Pulling Force

Deep placement of super granular urea is very laborious and time consuming work for manually operation. To overcome the human stress and drudgery to place the super granular urea, a low cost manually operated pull type 2- rows granular urea applicator (GUA) was designed and developed in the department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh. The performance of machine was done in agronomy field of Bangladesh Agricultural University. The effective field capacity was 0.11 ha/hr at a forward speed of 1.78 km/hr and 78.89 % field efficiency of developed granular urea applicator. The average distance between two dropped granular urea (GU) from left hopper & right hopper were 40.64 cm and 40.89 cm respectively. The average missing rate of GU dropped during field operation was 1.65% for granular urea size of 2.83 gm. The depth of granular urea placement was 7-10 cm in puddle field. The pulling force and draft of the developed applicator were varied between 5-11 kg and 3-6.62 kg respectively. The draft power was 0.027 kW for 55.38 N pulling force at 1.78 km/hr speed. The application rate of the GUA was 170 kg/ha. The results of field and laboratory test of the developed applicator were better in comparison to other models of GUA. The operational cost of the applicator is 390 Tk/ha which is lower than 22.16% of BARI made push type GUA and 53% lower than the manually placement of GU. Average grain yield was highest (5234 kg/ha) when granular urea were applied by the developed applicator and followed by GU applied by BRRI made push type GUA (5213 kg/ha) and GU applied by manually (5209 kg/ha). The highest straw yield (6787 kg/ha) was obtained when GU applied by manually. However the differences of grain yields and straw yields were not significantly difference for applying GU by the applicators and manually operation. The manufacturing cost of the applicator is only Tk.6000. The weight and the drawbar power of the applicator were 15 kg and 0.03 kW respectively. Therefore one person (man or women) is enough to pull the machine. Overall performance of the applicator was found quite satisfactory. For this reason, the applicator may be introduced in Bangladesh to apply super granular urea. DOI: http://dx.doi.org/10.3329/jbau.v12i1.21414 J. Bangladesh Agril. Univ. 12(1): 211-220, June 2014

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