Ultralow Power, Cleft Size-Adjustable and pH-Sensitive Hyaluronic Acid (HA) Biodevices for Acid-Sensing Ion Channels Emulation.

The burgeoning implantable biodevices have unlocked new frontiers in healthcare, promising personalized monitoring strategies tailored to specific needs. Herein, hyaluronic acid (HA) is harnessed to create fully biocompatible, acidity-sensitivity and cleft-adjustable neuromorphic devices. These HA-biodevices exhibit remarkable sensitivity to pH variations, effectively mimicking biological acid-sensing ion channels (ASICs) through protonation reactions between electronegative atoms and hydrogen ions, even at ultralow driving voltage (5 mV). They can monitor joint cartilage acidity by tracking changes in proton concentration and successfully diagnose the onset of arthritis. Furthermore, by adjusting the synaptic device's cleft distance, which determines responsiveness, power efficiency and plasticity, HA-based neuromorphic devices can be tailored to meet the unique demands of various implantation sites, providing both high-sensitivity and low-heat dissipation, thus broadening their application scopes. Moreover, the HA-biodevices maintain stable performance across various bending degrees, up to a curvature radius of 7.5 mm, with flexibility and deformation resilience enabling installation on joints of varying curvatures. The combination of all-biocompatibility, high sensitivity, low heat dissipation, ultralow low power (2 pW), and extraordinary deformation tolerance paves the way for the development of versatile, multipurpose medical monitoring devices with immense potential in the field of healthcare.

Ultrahigh Sensitivity for Thermographic Human-Machine Interface via Precious Metals Atomic Layer Deposition on V-MXene: Computational and Experimental Exploration.

Global healthcare based on the Internet of Things system is rapidly transforming to measure precise physiological body parameters without visiting hospitals at remote patients and associated symptoms monitoring. 2D materials and the prevailing mood of current ever-expanding MXene-based sensing devices motivate to introduce first the novel iridium (Ir) precious metal incorporated vanadium (V)-MXene via industrially favored emerging atomic layer deposition (ALD) techniques. The current work contributes a precise control and delicate balance of Ir single atomic forms or clusters on the V-MXene to constitute a unique precious metal-MXene embedded heterostructure (Ir-ALD@V-MXene) in practical real-time sensing healthcare applications to thermography with human-machine interface for the first time. Ir-ALD@V-MXene delivers an ultrahigh durability and sensing performance of 2.4% °C-1 than pristine V-MXene (0.42% °C-1), outperforming several conventionally used MXenes, graphene, underscoring the importance of the Ir-ALD innovative process. Aberration-corrected advanced ultra-high-resolution transmission/scanning transmission electron microscopy confirms the presence of Ir atomic clusters on well-aligned 2D-layered V-MXene structure and their advanced heterostructure formation (Ir-ALD@V-MXene), enhanced sensing mechanism is investigated using density functional theory (DFT) computations. A rational design empowering the Ir-ALD process on least explored V-MXene can potentially unfold further precious metals ALD-process developments for next-generation wearable personal healthcare devices.

Doping Engineering of Conductive Polymers and Their Application in Physical Sensors for Healthcare Monitoring.

Physical sensors have emerged as a promising technology for real-time healthcare monitoring, which tracks various physical signals from the human body. Accurate acquisition of these physical signals from biological tissue requires excellent electrical conductivity and long-term durability of the sensors under complex mechanical deformation. Conductive polymers, combining the advantages of conventional polymers and organic conductors, are considered ideal conductive materials for healthcare physical sensors due to their intrinsic conductive network, tunable mechanical properties, and easy processing. Doping engineering has been proposed as an effective approach to enhance the sensitivity, lower the detection limit, and widen the operational range of sensors based on conductive polymers. This approach enables the introduction of dopants into conductive polymers to adjust and control the microstructure and energy levels of conductive polymers, thereby optimizing their mechanical and conductivity properties. This review article provides a comprehensive overview of doping engineering methods to improve the physical properties of conductive polymers and highlights their applications in the field of healthcare physical sensors, including temperature sensors, strain sensors, stress sensors, and electrophysiological sensing. Additionally, the challenges and opportunities associated with conductive polymer-based physical sensors in healthcare monitoring are discussed.

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders.

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.

A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer.

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor's judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.

[Application of electronic skin in healthcare and virtual reality].

Electronic skin has shown great application potential in many fields such as healthcare monitoring and human-machine interaction due to their excellent sensing performance, mechanical properties and biocompatibility. This paper starts from the materials selection and structures design of electronic skin, and summarizes their different applications in the field of healthcare equipment, especially current development status of wearable sensors with different functions, as well as the application of electronic skin in virtual reality. The challenges of electronic skin in the field of wearable devices and healthcare, as well as our corresponding strategies, are discussed to provide a reference for further advancing the research of electronic skin.

Plasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis.

Nanophotonics has been widely utilized in enhanced molecularspectroscopy or mediated chemical reaction, which has major applications in the field of enhancing sensing and enables opportunities in developing healthcare monitoring. This review presents an updated overview of the recent exciting advances of plasmonic biosensors in the healthcare area. Manufacturing, enhancements and applications of plasmonic biosensors are discussed, with particular focus on nanolisted main preparation methods of various nanostructures, such as chemical synthesis, lithography, nanosphere lithography, nanoimprint lithography, etc., and describing their respective advances and challenges from practical applications of plasmon biosensors. Based on these sensing structures, different types of plasmonic biosensors are summarized regarding detecting cancer biomarkers, body fluid, temperature, gas and COVID-19. Last, the existing challenges and prospects of plasmonic biosensors combined with machine learning, mega data analysis and prediction are surveyed.

Edge-Fog-Cloud Computing Hierarchy for Improving Performance and Security of NB-IoT-Based Health Monitoring Systems.

This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.

The SSTeP-KiZ System-Secure Real-Time Communication Based on Open Web Standards for Multimodal Sensor-Assisted Tele-Psychotherapy.

In this manuscript, we describe the soft- and hardware architecture as well as the implementation of a modern Internet of Medical Things (IoMT) system for sensor-assisted telepsychotherapy. It enables telepsychotherapy sessions in which the patient exercises therapy-relevant behaviors in their home environment under the remote supervision of the therapist. Wearable sensor information (electrocardiogram (ECG), movement sensors, and eye tracking) is streamed in real time to the therapist to deliver objective information about specific behavior-triggering situations and the stress level of the patients. We describe the IT infrastructure of the system which uses open standards such as WebRTC and OpenID Connect (OIDC). We also describe the system's security concept, its container-based deployment, and demonstrate performance analyses. The system is used in the ongoing study SSTeP-KiZ (smart sensor technology in telepsychotherapy for children and adolescents with obsessive-compulsive disorder) and shows sufficient technical performance.

Non-Contact Video-Based Assessment of the Respiratory Function Using a RGB-D Camera.

A fully automatic, non-contact method for the assessment of the respiratory function is proposed using an RGB-D camera-based technology. The proposed algorithm relies on the depth channel of the camera to estimate the movements of the body's trunk during breathing. It solves in fixed-time complexity, O(1), as the acquisition relies on the mean depth value of the target regions only using the color channels to automatically locate them. This simplicity allows the extraction of real-time values of the respiration, as well as the synchronous assessment on multiple body parts. Two different experiments have been performed: a first one conducted on 10 users in a single region and with a fixed breathing frequency, and a second one conducted on 20 users considering a simultaneous acquisition in two regions. The breath rate has then been computed and compared with a reference measurement. The results show a non-statistically significant bias of 0.11 breaths/min and 96% limits of agreement of -2.21/2.34 breaths/min regarding the breath-by-breath assessment. The overall real-time assessment shows a RMSE of 0.21 breaths/min. We have shown that this method is suitable for applications where respiration needs to be monitored in non-ambulatory and static environments.

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

A Framework for Malicious Traffic Detection in IoT Healthcare Environment.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices' security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring.

With the increasing prevalence of growing population, aging and chronic diseases continuously rising healthcare costs, the healthcare system is undergoing a vital transformation from the traditional hospital-centered system to an individual-centered system. Since the 20th century, wearable sensors are becoming widespread in healthcare and biomedical monitoring systems, empowering continuous measurement of critical biomarkers for monitoring of the diseased condition and health, medical diagnostics and evaluation in biological fluids like saliva, blood, and sweat. Over the past few decades, the developments have been focused on electrochemical and optical biosensors, along with advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have evolved gradually with a mix of multiplexed biosensing, microfluidic sampling and transport systems integrated with flexible materials and body attachments for improved wearability and simplicity. These wearables hold promise and are capable of a higher understanding of the correlations between analyte concentrations within the blood or non-invasive biofluids and feedback to the patient, which is significantly important in timely diagnosis, treatment, and control of medical conditions. However, cohort validation studies and performance evaluation of wearable biosensors are needed to underpin their clinical acceptance. In the present review, we discuss the importance, features, types of wearables, challenges and applications of wearable devices for biological fluids for the prevention of diseased conditions and real-time monitoring of human health. Herein, we summarize the various wearable devices that are developed for healthcare monitoring and their future potential has been discussed in detail.

A Wearable Transient Pressure Sensor Made with MXene Nanosheets for Sensitive Broad-Range Human-Machine Interfacing.

Flexible and degradable pressure sensors have received tremendous attention for potential use in transient electronic skins, flexible displays, and intelligent robotics due to their portability, real-time sensing performance, flexibility, and decreased electronic waste and environmental impact. However, it remains a critical challenge to simultaneously achieve a high sensitivity, broad sensing range (up to 30 kPa), fast response, long-term durability, and robust environmental degradability to achieve full-scale biomonitoring and decreased electronic waste. MXenes, which are two-dimensional layered structures with a large specific surface area and high conductivity, are widely employed in electrochemical energy devices. Here, we present a highly sensitive, flexible, and degradable pressure sensor fabricated by sandwiching porous MXene-impregnated tissue paper between a biodegradable polylactic acid (PLA) thin sheet and an interdigitated electrode-coated PLA thin sheet. The flexible pressure sensor exhibits high sensitivity with a low detection limit (10.2 Pa), broad range (up to 30 kPa), fast response (11 ms), low power consumption (10-8 W), great reproducibility over 10 000 cycles, and excellent degradability. It can also be used to predict the potential health status of patients and act as an electronic skin (E-skin) for mapping tactile stimuli, suggesting potential in personal healthcare monitoring, clinical diagnosis, and next-generation artificial skins.

A Survey of Healthcare Monitoring Systems for Chronically Ill Patients and Elderly.

The demand of healthcare systems for chronically ill patients and elderly has increased in the last few years. This demand is derived by the necessity to allow patients and elderly to be independent in their homes without the help of their relatives or caregivers. The prosperity of the information technology plays an essential role in healthcare by providing continuous monitoring and alerting mechanisms. In this paper, we survey the most recent applications in healthcare monitoring. We organize the applications into categories and present their common architecture. Moreover, we explain the standards used and challenges faced in this field. Finally, we make a comparison between the presented applications and discuss the possible future research paths.

Sensor-Based mHealth Authentication for Real-Time Remote Healthcare Monitoring System: A Multilayer Systematic Review.

The new and groundbreaking real-time remote healthcare monitoring system on sensor-based mobile health (mHealth) authentication in telemedicine has considerably bounded and dispersed communication components. mHealth, an attractive part in telemedicine architecture, plays an imperative role in patient security and privacy and adapts different sensing technologies through many built-in sensors. This study aims to improve sensor-based defence and attack mechanisms to ensure patient privacy in client side when using mHealth. Thus, a multilayer taxonomy was conducted to attain the goal of this study. Within the first layer, real-time remote monitoring studies based on sensor technology for telemedicine application were reviewed and analysed to examine these technologies and provide researchers with a clear vision of security- and privacy-based sensors in the telemedicine area. An extensive search was conducted to find articles about security and privacy issues, review related applications comprehensively and establish the coherent taxonomy of these articles. ScienceDirect, IEEE Xplore and Web of Science databases were investigated for articles on mHealth in telemedicine-based sensor. A total of 3064 papers were collected from 2007 to 2017. The retrieved articles were filtered according to the security and privacy of sensor-based telemedicine applications. A total of 19 articles were selected and classified into two categories. The first category, 57.89% (n = 11/19), included survey on telemedicine articles and their applications. The second category, 42.1% (n = 8/19), included articles contributed to the three-tiered architecture of telemedicine. The collected studies improved the essential need to add another taxonomy layer and review the sensor-based smartphone authentication studies. This map matching for both taxonomies was developed for this study to investigate sensor field comprehensively and gain access to novel risks and benefits of the mHealth security in telemedicine application. The literature on sensor-based smartphones in the second layer of our taxonomy was analysed and reviewed. A total of 599 papers were collected from 2007 to 2017. In this layer, we obtained a final set of 81 articles classified into three categories. The first category of the articles [86.41% (n = 70/81)], where sensor-based smartphones were examined by utilising orientation sensors for user authentication, was used. The second category [7.40% (n = 6/81)] included attack articles, which were not intensively included in our literature analysis. The third category [8.64% (n = 7/81)] included 'other' articles. Factors were considered to understand fully the various contextual aspects of the field in published studies. The characteristics included the motivation and challenges related to sensor-based authentication of smartphones encountered by researchers and the recommendations to strengthen this critical area of research. Finally, many studies on the sensor-based smartphone in the second layer have focused on enhancing accurate authentication because sensor-based smartphones require sensors that could authentically secure mHealth.

A Flexible Wearable Pressure Sensor with Bioinspired Microcrack and Interlocking for Full-Range Human-Machine Interfacing.

Flexible wearable pressure sensors have drawn tremendous interest for various applications in wearable healthcare monitoring, disease diagnostics, and human-machine interaction. However, the limited sensing range (<10%), low sensing sensitivity at small strains, limited mechanical stability at high strains, and complicated fabrication process restrict the extensive applications of these sensors for ultrasensitive full-range healthcare monitoring. Herein, a flexible wearable pressure sensor is presented with a hierarchically microstructured framework combining microcrack and interlocking, bioinspired by the crack-shaped mechanosensory systems of spiders and the wing-locking sensing systems of beetles. The sensor exhibits wide full-range healthcare monitoring under strain deformations of 0.2-80%, fast response/recovery time (22 ms/20 ms), high sensitivity, the ultrasensitive loading sensing of a feather (25 mg), the potential to predict the health of patients with early-stage Parkinson's disease with the imitated static tremor, and excellent reproducibility over 10 000 cycles. Meanwhile, the sensor can be assembled as smart artificial electronic skins (E-skins) for simultaneously mapping the pressure distribution and shape of touching sensing. Furthermore, it can be attached onto the legs of a smart robot and coupled to a wireless transmitter for wirelessly monitoring human-motion interactivities.

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning.

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments.

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other 'things' ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers' heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

An autonomous wheelchair with health monitoring system based on Internet of Thing.

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users' four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.