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iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022 According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is
projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors
who live alone must have their health state closely monitored to avoid unexpected events (such as
a fall). This study explains the underlying principles, methodology, and research that went into
developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure
system, that we call as iRestroom to assess the frailty of elderly people for them to live a
comfortable, independent, and secure life at home. The proposed restroom idea is based on the
required situations, which are determined by user study, socio-cultural and technological trends,
and user requirements. The iRestroom is designed as a multi-sensory place with interconnected
devices where carriers of older persons can access interactive material and services throughout
their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave
Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a
constantly updated reference for the data originating from numerous sensors and devices installed
in different locations throughout the restroom. A small sample of pilot data was obtained, as well
as pertinent web data. The Institutional Review Board (IRB) has approved all the methods. keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning |
مقاله انگلیسی |
2 |
MagLoc : A magnetic induction based localization scheme for fresh food logistics
MagLoc: یک طرح محلی سازی مبتنی بر القای مغناطیسی برای تدارکات مواد غذایی تازه-2022 An IoT infrastructure to continuously monitor the fresh food supply chain can quickly detect
food quality and contamination issues and thereby reduce costs and food wastage. This, in turn,
involves several challenges including the development of inexpensive quality/contamination
sensors to be deployed in a fine grain manner in the food boxes, technologies for sensor
level communications, online data management and analytics, and logistics driven by such
analytics. In this paper, we study the issues related to the communication among sensing
modules deployed in the fresh food boxes and thereby an automated localization of the boxes
that may have quality/contamination issues. In this context we study the near-field magnetic
induction (NFMI) based communication and localization, as the ubiquitous RF communications
suffer high attenuation through the water/mineral rich tissue media. An accurate localization
of the sensors inside boxes within the food pallets is very challenging in this environment. In
this paper we propose a novel magnetic induction based localization scheme, and show that
with a small number of anchor nodes, the localization can be done without any errors for boxes
as small as 0.5 meter on the side, and with small errors even for boxes half as big.
Keywords: Smart sensing | Industrial sensors | Food supply chain | Physical Internet | Magnetic communication | Localization |
مقاله انگلیسی |
3 |
A new road state information platform based on crowed sensing on challenged network environments
یک پلت فرم جدید اطلاعات وضعیت جاده مبتنی بر حسگر در محیط های شبکه چالش برانگیز-2022 In this paper, a new generation road state information platform based on IoT crowed sens-
ing technology is proposed. Various sensors are attached on vehicle to sample sensor data
and to determine the road state in reatime. Those sensor data and road state information
are transmitted to the neighbor vehicles and road side server using V2X communication
network. Driver can receive the sensor data and road surface state information from the
vehicle in opposite direction or road side server and eventually pay attentions to his/her
driving before encountering the dangerous location. A prototype system of this proposed
system is constructed and evaluated the functions and performance as a preliminary sys-
tem. keywords: اینترنت اشیا | سنسور | شبکه بی سیم | سنجش سطح جاده | رانندگی خودمختار | IoT | Sensor | V2V: V2R | Wireless network | Road surface sensing | Autonomous driving |
مقاله انگلیسی |
4 |
A proactive role of IoT devices in building smart cities
نقش فعال دستگاه های اینترنت اشیا در ساخت شهرهای هوشمند-2022 Due to rapid advancement in technology the world is rapidly changed to face the upcoming challenges and going
towards automation. The use of various IoT devices is making a vast approach and every happening becomes part
of the network and due to that towns are converting into smart cities. The IoT devices collect the data of every
happening smartly and send it for further processing. An imperative part of these devices is containing the
wireless sensors used for building smart cities. A giant set of data is collected in the sensors and is stored in the
data center. Subsequently, the huge data becomes an exorbitant mountain that must be managed smartly if a
smooth operation is required. In this study, how such big data can be managed shrewdly is going to explore and
the proactive role of IoT sensors are investigated which helps in building the future smart cities more independently. The impact of services such as Smart transport, smart energy, smart infrastructure, smart health, smart
agriculture, and smart recreation in respect of smart cities and the old traditional city has been analyzed through
an Analytic Hierarchy Process (AHP). The obtained results showed a satisfactory level of local communities about
98% of people living in smart cities are satisfied in contrast to people living in old traditional cities and others
having a neutral opinion.
keywords: اتوماسیون | دستگاه های اینترنت اشیا | شهر هوشمند | سنسورهای بی سیم | داده های بزرگ | مجموعه غول پیکر | گزاف | Automation | IoT devices | Smart city | Wireless sensors | Big data | Giant set | Exorbitant |
مقاله انگلیسی |
5 |
A real-time tennis level evaluation and strokes classification system based on the Internet of Things
یک سیستم ارزیابی سطح تنیس در زمان واقعی و طبقه بندی ضربه ها بر اساس اینترنت اشیا-2022 In this study a single wearable inertial measurement unit (IMU) and machine learning method-
ologies were used to conduct player level evaluation and classification five prototype tennis
strokes in real-time. The International Tennis Number (ITN) test was used to verify the accuracy
of this IoT system in evaluating participant level. We conducted the ITN test on thirty-six par-
ticipants and conducted one-way ANOVA on the ITN test results using IBM SPSS 26. The IMU in
this study contained a tri-axis accelerometer (± 16 g) and tri-axis gyroscope (± 2000◦ /s) worn on
the participants’ wrist connected to a wireless low-energy Bluetooth smart-phone with data sent
to the computer terminal by cloud storage. Data processing including preprocessing, segmenta-
tion, feature extraction, dimensionality reduction and classification using Support Vector Ma-
chines (SVM), K-nearest neighbor (K-NN) and Naive Bayes (NB) algorithms. One-way ANOVA
analysis predicting participants’ ITN level and ITN field test scores yielded p < 0.001 at the three
different skill levels tested. SVM (MinMax), SVM (Standardiser) and SVM (MaxAbsScaler) clas-
sified unique tennis strokes precision and recall factors at the three different skill levels reliably
yielded in f1-scores above 0.90 for serve, forehand and backhand, with f1-scores for forehand and
backhand volley scores falling below that. The results of this study suggest using a single six-axial
50 Hz IMU in combination with SVM and SVM + PCA represents a significant step towards a more
reliable wearable tennis stroke performance and skill level real-time evaluation and feedback
technology. keywords: اینترنت اشیا | جمع آوری داده ها | پردازش داده ها | یادگیری ماشین | اپلیکیشن موبایل | تنیس | سنسورهای پوشیدنی | ارتباطات بی سیم | Internet of Things | Data collection | Data processing | Machine learning | Mobile application | Tennis | Wearable sensors | Wireless communication |
مقاله انگلیسی |
6 |
Design and architecture of smart belt for real time posture monitoring
طراحی و معماری کمربند هوشمند برای نظارت بر وضعیت بدن در زمان واقعی-2022 The bad back flexions are the main cause of the back disorders and pains. Many working
conditions require that the worker remain sitting and slouching for long time. Having a correct
sitting posture over time is the greatest way to protect workers from the back pains according
to the latest medical researchers. In this paper, we present the architecture and design details of
the proposed posture monitoring system. The aim of this study is to propose a tracking posture
system include complete information about the back posture. The existing posture monitoring
systems in literature were limited to trunk flexion monitoring. In this proposal we introduce
the shoulder bent monitoring in addition to the trunk flexion monitoring in order to provide
complete information about the back posture. The proposed posture monitoring system is a
smart belt equipped by inertial sensors to detect the trunk flexion and a shoulder bent to monitor
the posture over time. A smartphone application was developed to notify the person in case of
bad posture detection. The proposed system demonstrates encouraging results to monitor the
posture over time of seating persons and improves their seating behavior by receiving a real
time notification in case of bad posture detection.
keywords: وضعیت نشستن | سنسورهای اینرسی | پشتی | نظارت بر وضعیت بدن | سیستم بلادرنگ | Seatingposture | Inertialsensors | Backpain | Posturemonitoring | Realtimesystem |
مقاله انگلیسی |
7 |
Detection of moving objects using thermal imaging sensors for occupancy estimation
تشخیص اجسام متحرک با استفاده از سنسورهای تصویربرداری حرارتی برای تخمین اشغال-2022 Thermal imaging sensors have been increasingly integrated in a wide range of smart building
and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable
for applications that require non-intrusive monitoring with proper privacy protection. In this
paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e.,
Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving
objects. This type of sensors are designed to operate with a two-subpage chessboard reading
pattern, which gives rise to blob displacements across two subpages when target objects are
in motion. We have conducted systematic characterization of the sensor and demonstrated
issues through experimental measurements and analysis. We have also proposed a subpage
bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy
estimation with moving objects. The performance of the proposed method is analyzed by
training and testing classification algorithms using two datasets collected with objects of
different moving speeds. Our performance results indicate that the proposed method could be
used for occupancy estimation in various smart building and Internet of Things applications.
keywords: طبقه بندی | حسگر مادون قرمز | اینترنت اشیا | یادگیری ماشین | برآورد اشغال | ساختمان های هوشمند | Classification | Infrared array sensor | Internet of Things | Machine learning | Occupancy estimation | Smart buildings |
مقاله انگلیسی |
8 |
Genetic Algorithm based Internet of Precision Agricultural Things (IopaT) for Agriculture 4:0
اینترنت اشیاء دقیق کشاورزی مبتنی بر الگوریتم ژنتیک (IopaT) برای کشاورزی 4:0-2022 The development of IoT is increasing in our daily life. Its applications are now becoming
famous in rural areas also, such as Agriculture 4.0. Cheap sensors, climate data, soil in-
formation, and drones are now used to solve many real-time problems. One of the most
emerging topics in the IoT in the Agriculture field is IoT based precision agriculture. The
range of IoT applications can range between water spraying from drone, soil recommenda-
tion for different crops, weather prediction and recommendation for water supply, etc. In
this paper we propose a system that will recommend whether water is needed or not by
predicting the rain fall using Genetic Algorithm. In this article, we proposed a unique de-
cision making method to predict Rainfall using Genetic Algorithm (GA) to identify the ne-
cessity of manual water supply is needed or not. The sensor based system will be activated
to check wheather the GA based system completes its prediction correctly or not by sens-
ing moisture level from the soil. If the moisture level of the soil crosses the pre-defined
threshold value then plant watering is performed by quadrotor UAV. A terrace gardening
system is also implemented in this article, which uses a pump for water spraying. Various
atmospheric parameters help to develop a rainfall prediction system to enhance efficiancy
more than 80% in the proposed IopaT system to make the system more interoperable. keywords: اینترنت اشیا | تصمیم گیری | کشاورزی دقیق | الگوریتم ژنتیک | کشاورزی 4.0 | کوادکوپتر پهپاد | سنسور رطوبت خاک | Internet of Things | Decision Making | Precision Agriculture | Genetic Algorithm | Agriculture 4.0 | Quadrotor UAV | Soil Moisture Sensor |
مقاله انگلیسی |
9 |
Implementation and Calibration of an IoT Light Attenuation Turbidity Sensor
پیاده سازی و کالیبراسیون سنسور مه آلود تضعیف نور اینترنت اشیا-2022 Turbidity is an important characteristic of water quality that can indicate the presence of un-
desirable suspended particulate matter. Having access to an inexpensive and effective turbidity
sensor unlocks numerous Internet of Things (IoT) possibilities for remote environmental moni-
toring. Optical light attenuation turbidity sensors operate on the premise of detecting signal
degradation from a light source due to scattering from particles in a solution. This approach is
technologically unpretentious and only requires a handful of inexpensive electronic components
to construct. However, while this method is touted as “simple”, a significant challenge lies in
sensor calibration. That is, converting an analogue signal into a meaningful and accurate digital
reading in a known turbidity measurement standard (e.g., Nephelometric Turbidity Units (NTU)).
This paper presents an IoT light attenuation turbidity sensor design and explores the calibration
process to determine the sensor’s range and accuracy. Sensor calibration is undertaken using
Formazin turbidity standards and is cross-checked against a commercial turbidimeter. We provide
a step-by-step procedure for determining the correct signal strength to use and a functional form
for the sensor response to the Formazin standard. Finally, we specify an estimate of the accuracy
of the sensor and suggest the next steps in the proposed turbidity sensor’s development. Results
indicate that the sensor achieves within 2%-10% of accuracy at higher ranges (100–4000 NTU),
but its performance becomes significantly less reliable in low NTU ranges (< 100 NTU) where the
error rate increases to 26%. The turbidity sensor is used as part of an IoT remote aquatic envi-
ronmental monitoring platform. keywords: Internet of underwater things | Turbidity | Affordable sensors | Calibration | attenuation turbidimeter |
مقاله انگلیسی |
10 |
GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors
GaitCode: احراز هویت پیوسته مبتنی بر راه رفتن با استفاده از یادگیری چند حالته و حسگرهای پوشیدنی-2021 The ever-growing threats of security and privacy loss from unauthorized access to mobile devices have led to the development of various biometric authentication methods for easier and safer data access. Gait-based authentication is a popular biometric authentication as it utilizes the unique patterns of human locomotion and it requires little cooperation from the user. Existing gait-based biometric authentication methods however suffer from degraded performance when using mobile devices such as smart phones as the sensing device, due to multiple reasons, such as increased accelerometer noise, sensor orientation and positioning, and noise from body movements not related to gait. To address these drawbacks, some researchers have adopted methods that fuse information from multiple accelerometer sensors mounted on the human body at different lo- cations. In this work we present a novel gait-based continuous authentication method by applying multimodal learning on jointly recorded accelerometer and ground contact force data from smart wearable devices. Gait cycles are extracted as a basic authentication element, that can continuously authenticate a user. We use a network of auto-encoders with early or late sensor fusion for feature extraction and SVM and soft max for classification. The effectiveness of the proposed approach has been demonstrated through extensive experiments on datasets collected from two case studies, one with commercial off-the-shelf smart socks and the other with a medical-grade research prototype of smart shoes. The evaluation shows that the proposed approach can achieve a very low Equal Error Rate of 0.01% and 0.16% for identification with smart socks and smart shoes respectively, and a False Acceptance Rate of 0.54%–1.96% for leave-one-out authentication. Keywords: Biometric authentication | Gait authentication | Autoencoders | Sensor fusion | Multimodal learning | Wearable sensors |
مقاله انگلیسی |