Bluetooth Low Energy Research Articles

Accuracy and Functionality of Select Continuous Glucose Monitoring Systems Are Not Impacted by Implantable Cardioverter Defibrillator Devices.

Increasing numbers of individuals with diabetes are adopting use of continuous glucose monitoring (CGM) in their daily self-management. Many of these individuals have advanced heart disease. Implantable cardioverter defibrillator (ICD) devices can effectively reduce arrhythmic death and all-cause mortality in individuals with advanced heart disease. However, the potential impact of ICD devices on CGM system accuracy and functionality has not been well studied. This evaluation assessed whether FreeStyle Libre (FL) CGM systems can coexist and function within the same patient in the presence of wireless interference devices, including current ICD devices. Interferer sources included Wi-Fi devices, Bluetooth devices, cellular mobile devices, implantable medical devices, Bluetooth Low-Energy (BLE) devices, BLE accessory devices and BLE mobile devices, and ICD-programmer interferers. Five testing methodologies were used to evaluate the accuracy and functionality of the CGM systems when exposed to ICD functions: high-energy emergency shocking, pacing modes, anti-tachycardia pacing mode (ATP), and DC Fibber mode. All acceptance criteria and testing requirements were met for the CGM and ICD system for wireless coexistence evaluation. Our findings demonstrated that coexisting ICD devices and FL CGM systems provide safe and effective wireless communications with functional and accurate transfer of data during scenarios expected in clinical use.

P-EcStat: A Versatile Design of Photoelectrochemical and Electrochemical Sensing System with Smartphone Interface via Bluetooth Low Energy

Electrochemical and photoelectrochemical sensors are a rapidly developing field in analytical chemistry. However, commercial systems often lack versatility and affordability, hindering wider adoption. Additionally, the absence of integrated excitation light sources limits their application in photoelectrochemical sensing. Here, we present a highly precise, versatile, affordable measurement system for both electrochemical and photoelectrochemical sensing applications. The system incorporates a three-electrode potentiostat with a synchronized excitation light source. This design enables the system to perform conventional electrochemical measurements like cyclic voltammetry, chronoamperometry, and photoelectrochemical amperometric measurements with controlled light excitation. The developed measurement system operates within a voltage range suitable for a measurable current range of 1 nA to 18 mA, with a high precision of 99%. The excitation source is a monochromatic LED system offering seven distinct wavelengths with digitally controlled intensity via a digital-to-analog converter. Furthermore, an Android-based user interface allows wireless system control via Bluetooth Low Energy. The report also details the construction of a photoelectrochemical experiment using copper (II) oxide nanorods synthesized by the hydrothermal process as the photoactive material employed to test the experiment on a potassium ferricyanide/potassium ferrocyanide solution. This user-friendly system allows broader exploration of electrochemical and photoelectrochemical sensing applications.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

Learner’s attention detection in connected smart classroom using internet of things and convolutional neural networks

Detecting learner attention is an essential part of learning assessment. Consequently, it becomes an essential requirement for adaptive intelligent teaching systems, to identify specific needs and anticipate orientations. In this article, we propose a new model of a connected smart classroom, based on the internet of things, artificial intelligence and machine learning to detect in real time learners' attention and marking their presence during the execution of a teacher-assisted pedagogical activity, as well as to adapt the most suitable learning objects to these learners. The proposed model is based on head position, gaze direction, yawning and eye-state analysis as facial landmarks detected by cameras connected via the Bluetooth low energy network and transmitted to a developed convolutional neural network. In addition, a series of experiments have been conducted to evaluate the performance and efficiency of the model developed. The findings demonstrate that the model developed can be used to precisely capture the status of learners in the classroom in terms of attention and identification. In this way, these interesting findings can be used to adapt teaching activities to the individual needs of learners, and to identify areas where they have difficulties and needs extra help.

Unvealing the Super App for Tourism with QR and Machine Learning

Abstract: In today’s dynamic world of travel and tourism, the demand for innovative solutions that improve user experience and simplify booking processes is constantly increasing. In response to this need, our research introduces an innovative travel super- app poised to revolutionize the way travelers explore and interactwith travel destinations. This comprehensive application uses cutting-edge technologies such as QR code and Bluetooth Low Energy (BLE) technology, as well as advanced machine learning algorithms, to provide users with a seamless and personalized booking experience. By combining various tourist attractions such as cafes, water parks, monuments and museums into one platform, Super App eliminates the need for multiple apps, offering unparalleled convenience and efficiency. With person- alized recommendations, dynamic price predictions and preview tools, users can receive personalized recommendations and makeinformed decisions about their travel plans. Integrating BLE technology into check-in and check-out processes ensures a frictionless and secure authentication experience that increases operational efficiency and visitor satisfaction in tourism desti- nations. Our research aims not only to answer the immediate challenges that tourists face, but also to pave the way for the further development of the tourism industry. With its innovative features and user-centric design, Super App is a significant step towards passenger navigation and communication in today’s digital age.

Touch‐Driven Self‐Powered Sensing System with High‐Performance Thermoelectric Generator

This letter presents a touch‐driven self‐powered sensing system utilizing a high‐performance thermoelectric generator (TEG). The TEG, employing assembly technology with a high‐performance electrodeposited thermoelectric material, is fabricated. This device is accompanied by an investigation into the associated electronic circuits, facilitating a realistic demonstration. Upon tactile interaction with one side of the TEG, the system efficiently harvests energy, accumulating 15.36 mJ in a storage capacitor. Energy consumption, inclusive sensors and Bluetooth Low Energy (BLE) communication, is evaluated at 0.75 mJ/cycle, with a 2 s interval time. Notably, the sensing system demonstrates sustained operation as long as touch interactions persist, showcasing its potential for continuous, self‐powered functionality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Stratum Corneum Hydration Measurements with a Bluetooth Wireless Probe: A Real-Life Study at Home Compared to Measurements under Laboratory Conditions

Introduction: Non-invasive measurement of the stratum corneum hydration (SCH) with capacitance-based instrumentation is established in dermatological and cosmetic studies. We wanted to test the reliability of non-invasive self-measurements for SCH performed under real-life conditions by volunteers with a Bluetooth-based (wireless) probe Corneometer® (CM 825i) transmitting the data via a smartphone application to a central server. Probes and smartphones communicated using Bluetooth Low Energy. Data from the smartphone were securely transferred to a remote server in a different country with TLS encryption using HTTPS protocols. CM 825i values were correlated with the established CM 825 under laboratory conditions. The primary endpoint was the correlation of the two probes. Secondary endpoints were the coefficient of variation (CV) and delta values (before and after treatment). Methods: Eighteen healthy volunteers (f: 8; m: 10) participated in the prospective observational study. The real-world home use of the wireless CM 825i was performed before and after treatments with base cream DAC for 7 days. Results: Both instruments showed a significant and relevant correlation (p < 0.0001; Spearman coefficient of r = 0.8647). CM 825i and CM 825 differentiate significantly between normal and high SCH. Both devices showed comparable robustness in repeated measurements with a CV between 5.6% and 9.2%. Conclusion: We could show a significant correlation between both devices and a comparable differentiation between low and high SCH and comparable CVs. The real-life use demonstrated adequate acquiring and transmitting of in vivo data to a smartphone and subsequently transmitting to the secure server with low numbers of missed transmissions (<0.2%) and missed measurements (<5%).

Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.

Risk Evaluation and Attack Detection in Heterogeneous IoMT Devices Using Hybrid Fuzzy Logic Analytical Approach.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.

BmmW: A DNN-based joint BLE and mmWave radar system for accurate 3D localization with goal-oriented communication

Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE5.1’s constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW’s performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW-Lite and BmmW-Lite+, both tailored for single-antenna BLE devices, which eliminates the necessity for bulky and expensive multi-antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle-of-arrival info), BmmW-Lite uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 36 cm, thus facilitating precise object tracking in indoor environments even when using budget-friendly single-antenna BLE devices. BmmW-Lite+ extends BmmW-Lite by allowing the localization task to be transferred from the edge to the cloud due to device memory and power constraints. To this end, BmmW-Lite+ employs a goal-oriented communication paradigm that compresses initial BLE features into a more compact semantic format at the edge device, which allows to minimize the amount of data that needs to be sent to the cloud. Our experimental results show that BmmW-Lite+ can compress raw BLE features by up to 12% of their initial size (hence allowing to save network bandwidth and minimize energy consumption), with negligible impact on the localization accuracy.

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.

Evaluation of BLE-based audio broadcasting under probabilistic interference

Wireless Bluetooth audio communication has become an inherent part of everyday life, from listening to a podcast on our headphones to streaming music on multiple speakers. Up until now, broadcasting audio to multiple receivers has always required a proprietary implementation. Therefore, recent advances in the specification allow Broadcast Audio streams to be set up on top of Bluetooth Low Energy (BLE). To cope with the unpredictability of wireless media, audio frame retransmission opportunities are provided. However, determining the applicable number of retransmissions for a broadcast stream that is exposed to volatile environmental conditions, is a complex research challenge. This paper presents a model that is capable of simulating a BLE broadcast stream schedule, exposed to various environmental conditions while using a variable number of audio frame retransmissions. The evaluation employs several existing Packet Loss Concealment (PLC) techniques to cope with audio frame losses. The results provide insights into the impact of various frame loss patterns on the audio quality and intelligibility of broadcasted speech. The more advanced PLC techniques can handle a higher frame loss rate threshold. The analysis also shows that large audio frames requiring fragmentation exhibit a higher amount of frame loss for the same BLE packet loss rate and that overlap with in-use 802.11 channels can lead to a large variability in frame loss behavior. The model provides a baseline for the next research challenge related to continuous management of Broadcast Audio streams operating under volatile environmental conditions.

Indoor Navigation App Using Navigine

Indoor navigation systems are increasingly needed for smart cities, robots, and visually impaired people. Although Global Positioning System (GPS) is widely used for navigation in numerous applications, it cannot be used for indoor navigation systems as the signals cannot be absorbed by the walls of buildings. Therefore, various indoor positioning and navigation techniques have been proposed in the literature, such as radio frequency, computer vision, sensor-based, and semantic-based solutions. However, most of these techniques suffer from high cost, low accuracy, or limited scalability. The 2-dimensional navigation system uses the floor map of the building and it directs the user with the help of a route in addition, the system guides with a path assistance mechanism. Various technologies such as Bluetooth® Low Energy, Wi-Fi, etc., can be used for implementation. These allow for determining the location with an accuracy of 3-7 meters and according to the capability of the device. Key Words: Bluetooth Low Energy(BLE), Global Positioning System(GPS), Indoor Positioning Navigation System(IPNS), Simultaneous Localization and Mapping Technology(SLAM).

A Neuro-Driven Mobile Robot Enhanced by LoRa and EEG Technology

The Cognitive Rover project presents an imaginative way to deal with portable mechanical technology, coordinating high level neuro-innovations with long-range correspondence capacities to make a flexible and versatile mechanical stage. The essential target of the task is to foster a versatile robot fit for independent route and connection with its current circumstance, directed by brain signals got through electroencephalography (EEG) innovation. Utilizing the LoRa (Long Range) correspondence convention, the robot lays out strong network overstretched distances, empowering far off activity and information transmission in different situations, including search and salvage missions, perilous climate investigation, and far off review errands. The proposed framework design comprises of two fundamental parts: a neuro-driven control framework and a correspondence module. The neuro-driven control framework uses EEG signals gathered from a MindWave headset to decipher client orders and natural boosts. Through AI calculations and continuous sign handling strategies, the framework makes an interpretation of EEG signals into noteworthy orders for independent route, obstruction evasion, and undertaking execution. The correspondence module incorporates LoRa innovation to work with consistent information trade between the robot and far off administrators, empowering continuous observing, control, and video real time over significant distances. Key benefits of the Cognitive Rover incorporate its versatility to assorted conditions, instinctive client association through cerebrum PC interface innovation, and vigorous long-range correspondence abilities. Additionally, the task adds to progressions in mechanical technology, human-robot cooperation, and assistive advancements, with expected applications in misfortune reaction, ecological checking, and foundation review. Key Words: Electroencephalography (EEG), LoRa (Long Range), Cognitive Rover (CR), Brain Computer Interface (BCI), Bluetooth Low Energy (BLE), Transmitter (Tx)

A Survey of Internet of Things based Indoor Positioning Systems based on Bluetooth Low Energy Beacon

The global positioning systems (GPS) and cellular systems face inherent limitations when it comes to enclosed environments like shopping malls, hospitals, warehouses, as they have significant shadowing effects that cause the signals to be distorted. There have been many studies on different technologies and techniques aiming to find the optimum approach to be used for indoor positioning systems (IPS). Among all the technologies adapted by researchers all around the world, Bluetooth Low Energy (BLE) beacons have been a prevalent choice for many, due to its deploying flexibility, low cost, low power consumption and ability to provide very advanced services to users. It is arduous to obtain RSSI values with great accuracy because the signals can be easily intervened by many factors like humidity, temperature, physical barriers, and interference from decoherent signals. In this paper, we presented a comparative analysis of different, fairly recent IPS that are based on BLE beacons by classifying the different proposed approaches in a structured and systematic manner and traversing the issues and challenges that exist with each technique. The accuracy of the results can be enhanced by using the most suitable positioning algorithm, the orientation of the beacon setup, and optimization methods used. A preliminary result from a case study using BLE beacons with K-Nearest Neighbours (KNN) based fingerprinting approach for indoor positioning estimation shows 86.67% accuracy.

Design and Implementation of Bluetooth Low Energy Based Access Control System

This paper discusses the design of a Bluetooth Low Energy (BLE) based access control system intended to make access control more practical to implement on areas with high personnel turnover rate by making access rules easy to set and making access keys relatively safe to distribute compared to existing key-based access control systems. The use of BLE technology allows the system to estimate key position within the system by utilizing the curve fitting method for distance estimation and the trilateral method for positioning. The proposed system consists of a server, an admin panel, electronic locks, access keys in the form of a wearable, and access keys in the form of an Android application. The system is found to be capable of implementing access control functionalities and capable of implementing indoor positioning based on sections.

Quantifying Head Position Following Vitrectomy Using a 3-Dimensional Position Sensor Embedded in a Novel Ophthalmic Shield: A Pilot Study.

To assess head position following pars plana vitrectomy (PPV) using a novel low-energy, non-intrusive 3-D position sensor platform. In this prospective non-randomized interventional case series, a low-energy Bluetooth smart sensor housed within a novel eye shield recorded 3-D positional data every five minutes. The device was placed on the patient immediately after PPV and data was retrieved at the postoperative day 1 visit. Readings were processed by vector analysis into 4 groups, stratified by the angle of deviation away from a completely prone head position. The primary outcome was the angle between vectors. Ten patients were enrolled in this pilot study. The mean (SD) age was 57.5 (17.4). A total of 2318 readings with a mean (SD) of 231.8 (26.8) readings per patient were obtained. The mean (SD) number of readings were 132.9 (34.7) while awake and 98.9 (27.9) during sleep. Of total readings, only 11.7% fell into group 1 while a majority of readings fell in group 2 (52.4%), and group 3 (32.4%), and only 3.5% fell into group 4. Positional deviation increased during sleep hours, with about 46.8% of readings at a reclined angle (group 3) and 4.9% supine, which was 21.6% and 2.5% respectively during the wake time (p<0.001, p=0.002, respectively). In this pilot study, a non-intrusive wireless 3-D position sensor-shield platform was tolerated well and capable of capturing positional data. Adherence to face-down positioning was low and positional deviation increased significantly while sleeping.

Design and Testing of an Intramedullary Nail Implant Enhanced with Active Feedback and Wireless Connectivity for Precise Limb Lengthening

This comprehensive study presents a pioneering approach to limb lengthening, leveraging the advancements in wireless technology to enhance orthopedic healthcare. Historically, limb lengthening has been a response to discrepancies caused by fractures, diseases, or congenital defects, utilizing the body’s innate ability to regenerate bone and surrounding tissues. Traditionally, this involved external or internal fixation devices, such as the Ilizarov and Taylor Spatial frames or the Precice nail and Fitbone. The focal point of this research is the development and testing of a wireless intramedullary nail implant prototype, controlled remotely via a mobile application. This implant comprises a microcontroller, Bluetooth Low Energy module, a brushed DC motor controlled through an H-bridge, and a force sensor, all powered by medical-grade batteries. The integration of wireless technology facilitates patient autonomy in managing limb lengthening, reducing the need for frequent clinical visits. The methodology involves a detailed block diagram for our proposed work, outlining the process from treatment planning to the initiation of limb lengthening via the mobile application. Osteogenesis, the formation of new bone tissue, plays a crucial role in this procedure, which includes pre-surgery assessment, osteotomy, latency, distraction, consolidation, and removal phases. Key challenges addressed include custom battery design for efficient operation, size constraints, and overcoming signal interference due to the Faraday cage effect. Attenuation testing, simulating human tissue interaction, validates the implant’s connectivity. In conclusion, this research marks a significant stride in orthopedic care, demonstrating the feasibility of a wireless implant for limb lengthening. It highlights the potential benefits of reduced clinical visits, cost efficiency, and patient convenience. Despite limitations such as battery requirements and signal interference, this study opens avenues for future enhancements in patient-centered orthopedic treatments, signaling a transformative shift in managing limb length discrepancies.

An efficient beaconing of bluetooth low energy by decision making algorithm

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments.

Automated Domesticity: Exploring the Realm of Home Automation Systems

This research paper presents the development and implementation of a comprehensive smart home automation system comprising a smart switchboard, smart smoke, flame, and gas detector, and a smart door lock system. Leveraging innovative technologies such as voice control, sensor networks, and Bluetooth Low Energy connectivity, the system offers users enhanced convenience, safety, and energy efficiency within residential environments. The smart switchboard enables intuitive voice-controlled operation alongside traditional manual control options, while the smart smoke, flame, and gas detector provide real-time monitoring and alerting capabilities. Additionally, the smart door lock system ensures secure access control through voice password authentication and manual lock and key options. Through this integration, our research demonstrates the transformative potential of smart home automation in revolutionizing household management and enhancing quality of life