Bluetooth Connection Research Articles

Embedded IoT Design for Bioreactor Sensor Integration

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.

A Survey on Women Safety and Health Monitoring System

Women safety is a very big issue even in this modern world running with advanced technologies. After a history of times, women got a freedom of equality at the workplace, asset rights, family law, and education. Even Feminist Movement, claimed during 20th Century, Women are not safe anywhere and violence against women and girl child’s happening everywhere during the lonely travelling on roads, picnic spots, work and deserted places. Though we have many armies and super forces to protect everybody, the day to day crime rate against women has not been reduced. There are several safety devices available to protect the women during the violence, but need more human intervention such as entering the data, or shaking the device roughly. We propose a solution for a secure and peaceful environment for women with handbag safety hand-held devices with the aim to provide false proof women safety devices by overcoming the disadvantages in existing system. When we are talking more about Women empowerment, Women achievements it is also very important to think about women safety since a huge numbers (848) of women’s are Indian Women Are Harassed, Raped, Killed Every Day. The proposed work aims at IoT based women safety device by hardware controller attached to the handbag, android application and Bluetooth connectivity in Smartphone. By pressing the controller button, the device alerts the first holder, relatives stored in the database and police when a woman is not safe. The main advantage of the work is, the device works without internet connectivity. Additional features such as protecting the valuable things to be stolen by thieves in crowded places or buses by a separate alarming system, heartbeat sensor setup to monitor heart rate, fingerprint scanner for effecting accessing of the devices and also the mobile android application provide the victim’s location to reach the women and safeguard the women from any harassment at the right time. The main thought of this paper is that it will not only protect the women from physical harassment during odd hours but also safe the women health when it is found abnormal, as a single device comparatively.

Arduino-Powered Remote Monitoring System for Real-Time Heart Rate Detection via Bluetooth

With the increasing need for continuous health monitoring, this study presents the development of an Arduinopowered remote monitoring system for real-time heart rate detection via Bluetooth. The system uses an Arduino microcontroller connected to a pulse sensor to measure heart rate, with data wirelessly transmitted via Bluetooth to a remote device, enabling continuous monitoring without direct patient interaction. Initial self-monitoring trials showed consistent heart rate detection within the normal range of 62-70 beats per minute (BPM), suggesting proper functionality for preliminary use. The system also maintained a stable Bluetooth connection with no noticeable data loss. This cost-effective and efficient system is a valuable tool for telemedicine and remote patient care. Future enhancements may include integrating advanced microcontrollers and developing a more sophisticated mobile app with expanded health monitoring features.

Enhancing physical fitness testing: integrating Bluetooth connectivity for improved hand grip dynamometry

Enhancing physical fitness testing: integrating Bluetooth connectivity for improved hand grip dynamometry

Efficient Departmental Coordination: Real-Time Schedule Display using Arduino

This project tackles the time-consuming task of schedule management for Heads of Departments (HODs) by developing a dynamic, automatic display system. Utilizing an Arduino microcontroller, an RTC module, and a P10 LED display (16x32), the system displays their one-week schedule in real-time, updating automatically each day. Bluetooth connectivity empowers adjustments to the schedule through an external device, offering flexibility and control. Utilizing a P10 LED display for clear visibility, it automatically updates with the real-time clock. Schedule information is embedded within the code for reliability, and the display format is divided for time and detailed schedule. Prioritizing eco- friendliness, it uses low-power components. This project aims to improve departmental communication, transparency, and efficiency by providing a clear and accessible platform for HOD schedule information.

Bluetooth security analysis of general and intimate health IoT devices and apps: the case of FemTech

The number of digital health products is increasing faster than ever. These technologies (e.g. mobile apps and connected devices) collect massive amounts of data about their users, including health, medical, sex life, and other intimate data. In this paper, we study a set of 21 Internet of Things (IoT) devices advertised for general and intimate health purposes of female bodies (aka female-oriented technologies or FemTech). We focus on the security of the Bluetooth connection and communications between the IoT device and the mobile app. Our results highlight serious security issues in the current off-the-shelf FemTech devices. These include unencrypted Bluetooth traffic, unknown Bluetooth services and insecure Bluetooth authentication when connecting to the app. We implement Bluetooth attacks on the communication between these devices and apps, resulting in malfunctioning of the device and app. We discuss our results and provide recommendations for different stakeholders to improve the security practices of Bluetooth-enabled IoT devices in such a sensitive and intimate domain.

Alpha Neurofeedback Training in Elite Soccer Players Trained in Groups.

Neurofeedback training is applied in the world of sports as a means to improve athletes' performance. Training sessions are usually organized on an individual basis, one at a time. Here we investigated if the training could also be organized in groups. Forty-one national-level football (soccer) players (26 females, 15 males) carried out training sessions simultaneously in groups of up to 13, using a wearable device with Bluetooth connection, during their regular training hours at the club. It was possible to obtain good EEG measurements using this setup, albeit with a somewhat higher data loss than usual in standard laboratory sessions. The brain's alpha activity was trained using music-based neurofeedback in a crossover design. A training session consisted of alternating periods of neurofeedback and execution of cognitive tasks. EEG alpha (8-12Hz) activity was higher in the neurofeedback periods compared to the cognitive task periods, and the reverse was true for beta (13-30Hz) activity. The training program resulted in an increase of 34% in alpha activity associated with the training, and improved the athletes' performance on task switching and mental rotation tasks. In addition, self-reported sleep duration, as well as scores on the Being in Shape questionnaire (Feeling of Control and Flow) also improved. This study shows that neurofeedback training is feasible in groups of athletes, which can stimulate its application in team sports.

Remote self-testing for adult cochlear implant users: the effect of wireless streaming on speech recognition in noise

Objectives Wireless sound transfer methods for cochlear implant sound processors have become popular for remote self-assessed hearing tests. The aim of this study was to determine (1) spectral differences in stimuli between different wireless sound transfer options and (2) the effect on outcomes of speech recognition tests in noise. Design In study 1, the frequency response of different streaming options (Phonak Roger Select, Cochlear Mini Mic 2+, telecoil and Bluetooth connection) was measured by connecting headphones to CI sound processors. Study 2 followed a repeated measures design in which digits-in-noise (DIN) tests were performed using wireless streaming to sound processors from Cochlear, Advanced Bionics, and MED-EL. Study Sample 20 normal hearing participants. Results Differences in frequency response between loudspeaker and wireless streaming conditions were minimal. We did not find significant difference in DIN outcome (F4,194 = 1.062, p = 0.376) between the wireless transfer options with the Cochlear Nucleus 7 processor. No significant difference in DIN outcomes was found between Bluetooth streaming and the loudspeaker condition for all of the three tested brands. The mean standard error of measurement was 0.72 dB. Conclusions No significant differences in DIN test outcomes between wireless sound transfer and the reference method were found.

Modelling and Simulation of a Budget-Friendly, Secured and Intelligent Home Automation System Using Internet of Things for the Aged and Disabled

Intelligent Home Automation System is one of the subjects that is gaining traction and popularity due to its numerous benefits across the globe today. Home automation is the process of remotely monitoring and controlling household appliances and devices. With the Internet's and its applications' never-ending growth, there is a lot of promise and scope for remote access, control, and monitoring of network-enabled appliances and devices. This paper developed an easy-to-use, secured and intelligent home automation system; capable of remotely monitoring and controlling appliances in the home, by integrating it with an affordable, flexible, versatile, open-sourced, single-board microprocessor and yet scalable Arduino Uno. The work was carried out in the following stages; First, the Arduino Uno was set up with Bluetooth and Wi-Fi connections, and Android device interfacing. Secondly, upon successful connection and configurations, the system successfully worked with the home model developed. Lastly, a unique yet simple user interface was created using the Android MIT App inventor, thereby completing the system design. The intelligent home automation system was fully simulated given the above steps, ensuring that everyday use of home appliances is monitored and controlled for a more secure, seamless and intuitive user experience. The paper also examined a number of intelligent home automation systems and technologies from a variety of defined perspectives. It focused on the home automation idea, in which smart devices are used to integrate home automation systems and technologies with a central single-board – microprocessor (Arduino Uno); Life is becoming simpler and easier in all aspects due to advancements in automation technologies, particularly in homes and working environments.

Developing Bluetooth phonocardiogram for detecting heart murmurs using hybrid MFCC and LSTM

Cardiovascular disease is a leading global cause of mortality. Most stethoscopes still necessitate the use of tubing, which entails direct physical contact between the healthcare provider and patient. The stethoscope can serve as a means of transmission if it is utilized on individuals who have been diagnosed with airborne and droplet-borne infectious illnesses. A prototype was created to capture heart sounds using a Phonocardiography (PCG) device over website-based Bluetooth connectivity. This approach offers the benefits of being cost-effective, facilitating computer-aided diagnostics, and being wearable. In addition, the primary significance of this study resides in the identification of heart sound irregularities caused by cardio dynamic abnormalities of the heart valves, known as murmurs. The heart sound categorization process utilizes a machine learning model that involves extracting 25 Mel frequency cepstral coefficients (MFCC) as features. The model employs a hybrid approach combining convolutional neural network and long short-term memory (CNN-LSTM) techniques. The research findings indicate that the suggested model achieves an average accuracy rate of 95.9% over five distinct categories, i.e., normal, atrial stenosis, mitral regurgitation, mitral stenosis, and mitral valves prolapse. Further study can be conducted on hardware development by incorporating an infrared sensor at the fingertip of the stethoscope.

Digital Notice Board using Bluetooth and Arduino

In this project, Bluetooth and Arduino microcontrollers are used to build and operate a wireless noticeboard system. To disseminate real-time information across many contexts, a cutting-edge, interactive platform is now being developed. On conventional noticeboards, there are just physical notes and human modifications, both of which can be tedious and time-consuming. This solution makes use of Bluetooth connectivity and the adaptability of Arduino to let users send messages wirelessly from their cell phones or laptops. The Bluetooth-enabled devices used in the wireless noticeboard system allow users to enter messages. An Arduino microcontroller with a Bluetooth module collects the messages, processes them, and displays the results on a display device. Users can rapidly post messages to the noticeboard from their mobile devices using Bluetooth technology without having to engage with it physically. The system's core processing unit, the Arduino microcontroller, which also activates the display unit, receives and processes incoming messages before displaying them.

REMOTE VEHICLE DIAGNOSTIC SYSTEM DEVELOPMENT BASED ON THE INTERNET OF THINGS TECHNOLOGY

Background. Advanced artificial intelligence and IoT gateways are working together in the automotive industry to predict potential vehicle problems by analysing sensor data and optimizing quality control processes. Manufacturers can detect anomalies, improve product reliability, and eliminate manufacturing defects or malfunctions in advance. Predictive analytics also lead to improved fuel efficiency, performance and overall vehicle reliability. Objective. The purpose of this work is to develop a model for remote diagnosis of vehicle faults using a Raspberry Pi model B microcomputer and a SIM7600G-H GSM module. Configure data modules, install the necessary software and configure it, demonstrate step-by-step actions, and perform diagnostics and testing of this module for data transmission. Methods. A prototype was created on the basis of Raspberry Pi 4. and provides monitoring of machine operation in remote mode using the SIM7600E-H LTE Cat-4 4G/3G module. The design has small dimensions, easy installation, requires only initial adjustment and has a wide range of improvements. Results. This prototype uses a diagnostic OBD-II car scanner ELM327 with Bluetooth connection support, a Raspberry PI 4 model B microcomputer with 8 GB of RAM, 4 USB connectors (2 ports type USB3 and 2 ports type USB2), a Gigabit Ethernet port, a USB-C power supply port, and two micro HDMI 4K display connectors. On top of the module there are 48 pins (contacts) with which you can connect modules of different types and directions. The SIM7600G-H communication module is connected to these pins. The last element of the prototype is the SIM card of one of the telephone service providers and the micro SD card, which will act as the main memory element on which the operating system will be written and data will be stored. Conclusions. The article proposes the development of a device model using Internet of Things technologies, which is capable of providing remote diagnosis of car malfunctions. This model is based on the use of the SIM7600G-H module, which provides data transmission through the mobile network. The developed model allows you to read data from various car sensors, as well as transfer this data to a remote device for further analysis. This makes it possible to quickly detect malfunctions and make timely decisions on their correction.

SMART CONNECT: SIMPLIFYING BLUETOOTH HOME AUTOMATION CONTROL

The emergence of smart home technologies has fundamentally transformed the way individuals interact with their living spaces, promising unprecedented levels of convenience, comfort, and energy efficiency. In response to this paradigm shift, the SmartConnect project introduces a pioneering solution that harnesses the power of Bluetooth connectivity to establish a centralized home automation control center. This innovative system aims to streamline the management of diverse smart home devices, encompassing lights, thermostats, and sensors, by providing users with a seamless interface accessible via smartphones or tablets. With a primary objective of empowering users to remotely monitor and control their home environment, SmartConnect facilitates personalized automation routines and responsive adjustments to changing environmental conditions. Leveraging Arduino microcontroller technology and Bluetooth communication protocols, SmartConnect offers a robust and scalable platform for home automation, designed to accommodate future expansions and updates while ensuring compatibility and functionality over the long term. In essence, SmartConnect represents a significant stride towards realizing the smart home vision, embodying a fusion of innovation, efficiency, and user-centric design. Keywords- HC-05 Bluetooth Module, Arduino UNO Micro-controller.

Bark frequency cepstral coefficient based sadness emotion level recognition system

Based on the adverse physiological and social consequences associated with sadness, recognizing this emotion is crucial to mitigate its negative impacts. To address this issue, an efficient system was proposed for recognizing different level of sadness using embedded technology. The system captured speech input through a microphone and then processed it on Raspberry Pi microcomputer. The key feature extraction technique used was Bark-Frequency Cepstral Coefficient (BFCC) method. After selecting a set of features, a thorough analysis of the computational algorithms was conducted, and dimensionality reduction method was applied to reduce computational costs, including noise. These features served as a pre-processing stage for Machine Learning (ML) model based on K-Nearest Neighbors (KNN) architecture. The results were then shown on a smartphone application through Bluetooth connectivity. Based on experimental results, the system showed an impressive improvement of 11.97 dB in the signal-to-noise ratio (SNR) compared to feature extraction using Mel-frequency Cepstral Coefficient (MFCC) method. The results also showed a favorable accuracy rate, implying the effectiveness of KNN with a k-value of 5 in accurately identifying different level of sadness. The system particularly excelled in correctly recognizing high and low level of sadness. Therefore, the effective categorization of sadness emotion level could be applied to the early diagnosis of mental disorders such as depression and anxiety, contributing to the reduction of the adverse impact of sadness.

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol

BackgroundEating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse.MethodsSTORY follows 720 young people aged 16–25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings (‘Ōura ring’) unobtrusively measures individuals’ daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months.DiscussionBy leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.

Wireless Remote Controlled Multi-Purpose Robot using Raspberry PI

Over the past five decades, robotics and associated technologies have evolved into indispensable components across numerous industries, particularly in advanced manufacturing. Moreover, their application has extended beyond production-centric domains to encompass pivotal roles in military and law enforcement sectors, owing to advancements in sophistication, reliability, and compactness. This paper proposes the implementation of a real-time object detection system capable of precisely identifying and locating recognized objects, presenting a significant and practical advancement. Integrating various modules such as GSM and GPS with a Raspberry Pi, along with an array of sensors including metal and flame sensors, an L293 Motor driver, HC-SR04 Ultrasonic sensor, and HC-05 Bluetooth, this system facilitates seamless recognition and localization of objects in real-time. The Bluetooth connectivity feature enables remote control of the device, while a built-in camera captures images of unidentified individuals, thereby enhancing its surveillance capabilities. The incorporation of a motor driver ensures secure and controlled movement of the robot, while the ultrasonic sensor detects obstacles within its proximity. Additionally, the metal sensor swiftly identifies metallic objects, relaying their locations to the administrator, whereas the flame sensor promptly detects fires, enabling the system to dispatch location data for immediate response. This holistic approach renders the proposed system a versatile tool applicable across a spectrum of scenarios, ranging from security and surveillance operations to emergency response initiatives.

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable.

Developing objective and quantitative methods of early gross motor assessment is essential to better understand neurodevelopment and to support early therapeutic interventions. Here, wepresenta method to quantify gross motor performance using a multisensor wearable, MAIJU (Motility Assessment of Infants with a JUmpsuit), which offers an automated, scalable,quantitative, and objective assessment usinga fully automated cloud-based pipeline. This wearable suit is equipped with four movement sensors that record synchronized data to a mobile phone utilizing a low-energy Bluetooth connection. An offline analysis in the cloud server generates fully analyzed results within minutes for each recording. These results include a graphical report of the recording session and a detailed result matrix that gives second-by-second classifications for posture, movement, infant carrying, and free playtime. Our recent results show the virtue of suchquantified motor assessmentprovidinga potentiallyeffective method for distinguishing variations in the infant's gross motor development.

A STUDY ON MARKETING STRATEGIES USED BY boAt

boAt is an Indian startup which was established in the year 2016 by its founders Aman Gupta and Sameer Mehta with the objective to provide high quality products at an affordable and suitable price for the consumers. The company offers to its consumers an affordable range of headphones, earphones, speakers and other accessories. The reason for the companys success can solely be credited to its affordable and cheap prices than compared to other competitors in the market and also because it caters to needs and preferences of the younger generations or the Gen-Z s in India. It also lays strong emphasis on design, boAt products often feature trendy aesthetics, vibrant colors, and unique designs, appealing to fashion-conscious consumer preferences. The companys ongoing product development demonstrates its dedication to innovation and technology , adding features like Bluetooth connectivity, waterproofing, and noise cancellation to enhance the user experience. KEYWORDS: Marketing Strategies, boAt, Consumer Preferences

Monitoring through AI Based Remote Access Vehicle in Hydro Power Plant

The development of hydroelectric power is essential to the production of sustainable energy. Continuous monitoring and maintenance are necessary to guarantee the safe and effective functioning of hydroelectric plants. A revolutionary method for improving the security, effectiveness, and monitoring capacities of hydroelectric power plants is presented in the project "Monitoring through AI-Based Remote Access Vehicle in Hydro Power Plant."This creative project uses cutting-edge technologies, such as Bluetooth connectivity, artificial intelligence (AI), Raspberry Pi, and sensor systems, to develop a remotely controlled car that is specifically designed to meet the demands of hydroelectric power plant conditions. Real-time object detection and obstacle avoidance are made possible by this configuration, guaranteeing safe passage across the complex industrial infrastructure.Incorporating a temperature sensor also makes it easier to identify fire situations early on, while a water-detecting sensor protects against water leaks. By utilizing AI algorithms for object recognition, vehicles can detect and react to impediments in a proactive manner, hence decreasing the likelihood of accidents. As sentinels, the temperature and water leakage sensors keep an eye on things constantly and send out alarms right once if anything seems out of the ordinary. Reduced reliance on in-person inspections due to remote monitoring capabilities results in lower costs and more dependability. To sum up, this initiative tackles important issues pertaining to hydro power plant maintenance, efficiency, and safety

Sign Recognition and Voice Conversion Device for Dumb

This project proposes a comprehensive hardware setup for home automation and environmental monitoring. The system includes components such as a Bluetooth module for remote control, a voice module (APR9600) for audio feedback, an LCD for display purposes, relay modules for controlling electrical devices, bulbs with holders for lighting, a CPU fan for temperature regulation, speakers for audio output, and a 5V power supply for operation. The core functionality of the system revolves around the integration of flux sensors for environmental monitoring. When flux sensors detect specific conditions, predefined audio signals are played through the voice module. These conditions correspond to six distinct signs, facilitating real-time alerts and notifications. Moreover, the system offers remote control capabilities through Bluetooth connectivity, enabling users to manage lighting and fan operations conveniently. Additionally, temperature monitoring is implemented using a DHT11 sensor, triggering the activation of the CPU fan when temperature thresholds are exceeded. Furthermore, an LCD display enhances user interaction by providing visual feedback and status updates. The project utilizes Arduino UNO as the central processing unit and incorporates 12V 1A adapters for powering specific components. Connectors and power cables are utilized for seamless integration and electrical connectivity. Overall, this project presents an integrated solution for home automation and environmental monitoring, offering flexibility, convenience, and enhanced user experience.