A Wearable Sensor (WS) is utilized in the healthcare system to provide technical help as well as remote patient monitoring. The sensor is attached to the user’s body and detects their motions at various time intervals. The collected information is forwarded to a healthcare facility for suggested treatments for the remote user [1]. On the heterogeneous platform, data is shared and compared to a predefined dataset. The dataset is made up of a predefined set of medical data and patient information [2]. It is indicated that the patient has a history of communication with healthcare providers and therapy. As a result, medical data is sensitive and private, and must be secured against unauthorized access [3], which may pose a serious threat to the patient’s health. The WS monitors bodily function-related concerns at predetermined intervals and stores the data [4]. There may be errors and latency when storing the data, problems that must be addressed at the outset. In healthcare, WS data is an important element of safe data transfer [5].

The Microelectromechanical System (MEMS) is used in data analysis to improve the quality of medical data. It is used to help the caregivers acquire relevant data for providing appropriate treatment to the patient [6]. The data prediction is used to assess serial and current medical data. The data is then compared to current data [7]. It improves accuracy and enhances the security of medical data by creating a forecast. Data analysis (DA) is accomplished through the development of sensor-improved health information systems for decision-making. [8] The data is evaluated to see if it is relevant or not. It is carried out on time. The data collection and comparison are performed in a set period, and if there is a delay, an error occurs [5]. The data is received from the WS in order to complete the task within the specified period. It provides safe DA and transmits the outcomes to the HCS for evaluation [9].

Data fusion (DF) is performed for medical data collected through extraction and classification techniques. The retrieved data is categorized in order to decrease mistakes and improve the medical system’s accuracy [10]. Numerous data from the sensors are combined to achieve WS fusion. Sensor fusion is the process of merging integrated data that is less ambiguous. Three forms of DF can be used: low level, feature level, and decision level [11]. Low-level refers to the merging of two sensors’ information, while feature-level is defined as extracting medical data features [12]. Finally, using current medical data, decision-level DF is used to reach an appropriate judgment. Three fusion models are utilized for DA: reactive, proactive, and interactive [13].

In Section II, the relevant research conducted to date is discussed in order to provide an overview of the current scenario. Section III illustrates how the Errorless Data Fusion (EDF) approach is achieved. The collected data is sent for feature extraction, followed by the classification of features. Finally, the random forest algorithm is used to acquire optimum data fusion. In Section IV, a comparative study of the suggested EDF approach’s performance is given, which addresses the metrics identification accuracy, fusion error, and detection time. The objective of this work is to use Random Forest (RF) Machine Learning (ML) to enhance the precision of medical data by 20%. In this work, DF is accomplished by categorizing features in assigned time period, and forecasting is conducted through the use of an ML method.

Past research studies have helped in providing insight into the application of technology in healthcare institutions. These research studies have come up with the different findings that indicate the effectiveness of using the IoT to connect to various health devices and systems [14–19]. Wang et al. [20] proposed a hybrid sensory system for patients to monitor routine activities and identify walking patterns. For DF methods collected from the WS, human activity recognition is employed. The features are categorized using a Feature Selection (FS) approach based on data and Support Vector Machine (SVM). A long Short-Term Memory and Convolutional Neural Network (LSTM-CNN) [15] fusion system is presented to identify unusual posture by Gao et al. [21], utilizing a Wearable Inertial Measurement Unit (IMU). This study involves leg Euler angle data to calculate classification precision.

The rapid development in technology has led to significant changes in the healthcare system. Currently, healthcare organizations depend on advanced technology to deliver healthcare products to patients. The IoT has played a critical role in changing the healthcare system. The emergence of wearable sensors has led to significant improvement in patient care and treatment. It enables medical practitioners to monitor patients in the hospital or remotely. Various wearable devices are designed to meet the specific and unique needs of patients. The IoT connects the various wearable devices to the patients and thus helps in gathering important data and information that can be used to make proper healthcare decisions and facilitate the efficient treatment of the patient.

The concept of smart healthcare was introduced to provide proper solutions in the delivery of efficient healthcare. Smart healthcare is regarded as a smart infrastructure that typically uses WSs to help detect and perceive information and data from the patient. The gathered information is then transmitted through the IoT and processed using cloud computing and supercomputers. Additionally, it can coordinate the integrated social system in order to understand the dynamic management of human services. Smart healthcare normally uses technology that includes wearable devices, IoT, and the mobile internet to help obtain data, and connect to individuals, institutions, and materials that are associated with healthcare. The information gathered is actively managed and responds to the needs of the medical ecosystem in an intelligent manner.

Smart healthcare is made up of patients, physicians, research institutes, and hospitals. In fact, it is viewed as an organic whole including different aspects like illness control and monitoring, diagnosis, treatment, health decision-making, hospital administration, and medical research. IoT, cloud computing, mobile internet, 5G, big data, Artificial Intelligence (AI), and contemporary biotechnology are all examples of current Information Technology (IT) and are key components of SCH [14]. These technologies can be used to monitor a patient’s health via wearable devices. Wearable devices can also be by the patient to obtain medical assistance and services from one's own home. It also allows clinicians to handle medical data and information using an integrated information infrastructure that consists of a Laboratory Information Management System (LIMS), Electronic Medical Records (EMRs), an image archiving system, and other technologies. The adoption of Surgical Robots (SRs) and the mixed reality technique allows for more precise surgery. The usage of medical platforms can assist patients to have a better experience. Big data may also be used to examine a certain issue. The use of IoT and new technologies may decrease the risks and expenses of medical procedures and processes.

The application of technology such as AI, SRs, and mixed reality, has made the treatment and diagnosis of illnesses more efficient and intelligent. In most cases, the accuracy of AI diagnosis findings surpasses that of human doctors. ML-based systems are regarded as more accurate than the expert medical practitioners. In recent years, wearable IoT devices have been increasingly adopted in various application fields. The wearable devices are embedded or worn on the human body. The architecture of the wearable IoT network enables it to store important health information that facilitates the treatment and management of patients. The integration of several types of sensors into wearable IoT devices has led to significant improvement in their functionality. Smartwatches, for example, may be used for not just localization, entertainment, social networking, and payment, but also health and routine task tracking.

The software and hardware devices used in the healthcare system to support IoT can be exposed to certain challenges that can compromise their effectiveness. Unauthorized individuals’ access is one of the most significant challenges. Smart healthcare services, like any other internet-connected devices, are vulnerable to hackers [16]. Many wearable devices in the healthcare industry are subject to security threats and vulnerabilities. The intruders may obtain access to the IoT connected to numerous medical devices. They have access to and can change the data and information recorded on the devices. This may endanger the patients’ ability to get effective treatment. Furthermore, patient data may be compromised as a result of illegal access to the health organization’s computer network. An attacker may have access to the patient’s private and sensitive data. As a result, it is critical that cybersecurity measures are implemented to safeguard wearable devices from third-party attacks.

Zahra et al. [22] proposed multimodal sensor fusion (SF) to detect action in assembly production. Information from the wearable IMU and EMG is used for optimizing the training of CNN. Efficiency depends on the mixing as well as forecasting of SF data. Al-Amin et al. [23] developed a foot-mounted inertial sensor-based fusion method for detecting the posture of older people. The raw data is obtained using a hidden Markov model and a Neural Network (NN) in this study, which detects six categories of positions. Rule-based recognition using an optical motion seizure is the posture that is trained. Wang et al. [24] introduced deterministic learning based on DF to address various walking perspectives to detect human posture. The posture motion on spatio-temporal characteristics is extracted using the Radial Basis Function (RBF). For the identification of human posture, the deep Convolutional and Recurrent Neural Network (CRNN) is created.

The posture examination can provide insight on how to improve patient care and ensure enhanced quality care. The categorization of posture characteristics associated with knee, described by Fendri et al. [25] is based on posture analysis using deterministic learning. The patient’s knee has osteoarthritis (OA). It is common in asymptomatic (AS). The RBFNN analyzes postural patterns and increases accuracy by separating them. Nweke et al. [26] used two branches of CNN to create posture feature extraction and classification. For posture identification, a two-branch CNN (TCNN) is employed. Multi-Frequency Posture Energy Images (MF-GEIs) is being utilized to train the posture inputs. Posture energy image is being used to identify posture.

Abbas et al. [27] offer a method for detecting posture recognition. It utilizes a covariate factor for appropriate behavioral biometric characteristics. The semantic information is utilized to improve posture-based identification accuracy. The dynamic selection for the person is performed in this procedure, and the human components are selected to obtain semantic information.

To evaluate human behavior and decrease the rate of misrecognition, Tran et al. [28] suggested human action recognition for multi-sensor fusion. This approach for multi-sensor DF introduces a Multi-View Ensemble Algorithm (MVEA). The feature vector is generated using Logistic Regression (LR) and the K-Nearest Neighbor (K-NN) technique. By using Synthetic Oversampling Minority Techniques (SOMT), the class imbalance is reduced. Hanif et al. [29] presented a data augmentation method for identifying the posture. It utilizes the deep neural network from the inertial sensor. For efficient training, two types of methods are used: Arbitrary Time Deformation (ATD) and Stochastic Magnitude Perturbation (SMP). General postures are recognized by CNN.

Dawar et al. [30] use deep learning-based fusion to incorporate depth and inertial sensing for action identification. A camera recognizes the movement or gesture and captures depth pictures of postures in various angles. Posture in a favorable influence is identified via a decision-level fusion. Zou et al. [31] describe a system that integrates inertial and RGBD sensors for strong posture identification. Eigen posture’s color and depth from the accelerometer in eigenspace are included in posture data. The supervised classifier in this case uses a 3D dense trajectory to obtain greater identification accuracy.

The IoT, as represented by Fan et al. [32], is used to study attitude detection and data analysis. Through the creation of Fast Fourier Transformation (FFT), the goal of this project is to minimize error and frequency domain by 10%. To improve activity, human motion is evaluated by identifying posture.

Islam et al. [33] present data analytics for detecting bodily posture fatigue with the use of a WS. Fatigue is recognized via ML, and essential traits are selected based on its knowledge. Class dependencies are utilized to enhance accuracy when detecting fatigue.

The literature review will contribute immensely to the healthcare delivery system. It will monitor the patient from remote places and help in the facilitation of the treatment. The medical devices will help track the patients and record their health status at all times during and after the treatment. It is necessary for the healthcare professional to enact appropriate healthcare intervention measures to conform to the changes in technology. The proposed research study will also add the most recent information and data on the errorless data fusion technique, and therefore contribute to a new literature review on the topic.

The patients’ bodies are fitted with a WS that detects their posture and transmits their walking pattern to the smart healthcare system. The goal of this project is to improve precision and eliminate errors in medical data by integrating Feature Extraction (FE) and Feature Classification (FC). For sequential analysis of walking patterns, DF is used to determine the least feature depending on the time interval. The suggested technique’s process flow is depicted in Fig. 1.

The FE is derived from the sensor using Eq. (1), and it incorporates data integrity, chaining, and data patterns.

(1) se(q′)=(n,o−+1go¯)+(∏xTω′,go¯∑do¯d′go¯)∗∏\raisebox{1.5pt}{--}\kern-7ptXTfo¯(io¯+c′+paω′,)

The FE is performed by collecting the sensor data. Using that data, i0 represents integrity that contains input and output data. Chaining c′ is the sensor’s data nonstop stream, a pattern of data pa that refers to the patients’ walking patterns. The sensor data features utilizing sensor acquisition se(q′) are included in all three. n0 represents the data sensing and g0 denotes posture recognition, the data are {d′(1),d′(2),…d0}, where d′ refers to data and d0 is represented as several data.

The walking patterns are denoted by ω. They are detected in a specific time duration. They are tracked by sensing WB at each certain period as dt(ω),…xt(ω). The features are denoted as f0, which contains (i0+c′+paω). The extraction is seen here. Following that, Eq. (2) is utilized to obtain the data features.

(2) fo¯={io¯=∑q′ņo¯(1−[mo¯+u′]w)ç′=ņo¯+q′Xt∗∏wgo¯(dT−do¯)+o′pa=∑dTw[q′+\raisebox{1.5pt}{--}\kern-7ptXT(w)]−[do¯+α]

The integrity of the medical data, (1−[m0+u′]ω), represents the incoming and outgoing m0andu′. This describes the patient's WB. A series of sensor data is represented by chaining, n0+q′xt and it is followed for series of data inputs to the devices. o′ is denoted as series of WS data, and α represents a single step made by the patients during their movement. These are employed in the analysis of data patterns. In Eq. (3), all three are applied.

(3) fo¯(β)=∑wq′ņo¯+(mo¯+u′)∗(ņo¯+q′\raisebox{1.5pt}{--}\kern-7ptXTo′)+[q′+\raisebox{1.5pt}{--}\kern-7ptXT(ω′,)do¯]+α

The three features are extracted in a better way for the classification of data; the above Eq. (3) is used to observe the fixed time DA. The calculation of q′+xt(ω)d0 denotes that the WB is tracked in a specific time interval, and o′(ω)+q′.(m0+u′) are monitored and assess the outcomes of posture in the sensor, with β represented as data analysis. After f0(i0+c′+pa) the FC is completed. The least feature (LF), which describes the remaining error data, is utilized to categorize the error data minimization. For posture identification, FC is achieved, and the error is observed using Eq. (4).

(4) =∏q′fo¯(mo¯−u′)+(1+o′+\raisebox{1.5pt}{--}\kern-7ptXT∅)∗∏d′ņo¯[fo¯+α]+ω′,∗(yo¯)∅

FE of the data is performed by detecting the WB of the patients, from which categorization is obtained by assessing Eq. (4). ∂ is referred to as error data in identifying WB, and it contains network traffic (NT) and delays represented as ∅and. These two concerns are solved utilizing the sensing WB pattern of the patients ω(f0). The calculation of [ω∗(y0)∅] shows the walking detection seen by delay and NT. Fig. 2a depicts the pattern classification procedure.

(1+o′+xt∅) is a series of data that prevents NT at a certain time interval xt(∅+f0). The errors are discovered and then eliminated to improve precision. The classification is separated into two categories, error minimization and Least Feature Extraction (LFE), with the latter representing the remaining error. The LFE for classification is calculated using Eq. (5).

(5) δ=∑įo¯d′(mo¯+u′)o′∗∏β(d′)\raisebox{1.5pt}{--}\kern-7ptXTdowhere, do¯∈(o′+w)∏mo¯o′(1+do¯\raisebox{1.5pt}{--}\kern-7ptXT)∗∑wfo¯(ņo¯+β)+l,∀ error detection}

The LF error is identified by assessing Eq. (2), and in this ∑i0d′(m0+u′)o′, the incoming and outgoing data are analyzed β(ω), where d0∈(o′+ω). The next stage is represented by the error identified in the second phase, which is written as ∏m0o′(1+d0xt)∗∑ωf0(d0) In this situation, data are retrieved from the least error (LE) and are denoted as δ, which represents the ∂(d0). Eq. (6) can be used to illustrate the two kinds of classification.

(6) ξ={(1+o′+\raisebox{1.5pt}{--}\kern-7ptXT∅)+[(fo¯+α)(yo¯+∅)]∗(mo¯−u′),∀ error detection(∑įo¯d′(mo¯+u′)o′)+(1+do¯\raisebox{1.5pt}{--}\kern-7ptXT)∗,∀ least error

The classification γ is accomplished by calculating both the error and the LE of the patients’ posture identification. The goal is to decrease the error. They are derived by calculating Eq. (7), in which the error is first assessed and then decreased to enhance data precision. As a result, the formula o′(ω∗αxt) reflects the WB observed at each time interval. The WB is recurrently evaluated in this way. Eq. (7) is evaluated by combining Eqs. (5) and (6), as follows:

(7) (ξ)=(ω′,´∗(yo¯)∅)∗(mo¯−u′)+(1+o′+\raisebox{1.5pt}{--}\kern-7ptXT∅)+{(∑įo¯d′(mo¯+u′)o′), such that d′∈fo¯[(fo¯+α)(yo¯+∅)]+∑wfo¯(ņo¯+β)+where, delay and network traffic reduced

Calculating Eq. (7), where (1+o′+xt∅) is assessed to obtain the series of incoming sensor data, the classification is accomplished by deriving errorless data. Then, using y0∅, the error-based data are categorized, and the LE is achieved by calculating Eq. (8) as follows, resulting in the classification of the two types of posture data.

(8) ξ(w)=12∗∏βo′(mo¯+do¯)\raisebox{1.5pt}{--}\kern-7ptXT+∫ņo¯fo¯(įo¯+c′+þåα)∗(yo¯+∅)

The characteristic of WB is used to determine integrity. The steps in which leg motions are computed are tracked in this way. The classification of WB is assessed using Eq. (8), and the sequence of incoming data is studied o′(β). Here, d′∈f0, and the features represent (m0−u′)o′, which shows the α(∂)+(dt−dt). DF is done in this way, and the classification module is derived by solving Eq. (9).

(9) ρ=(įo¯+ç′+þåα+l)∗∏yo¯∅[[1+o′fo¯]+∏go¯αβ(l)l+\raisebox{1.5pt}{--}\kern-7ptXT]∗∑ξl∅+(1+(mo¯+o′)fo¯)+{(\raisebox{1.5pt}{--}\kern-7ptXT−dT)∗(ņo¯+β)mindT(\raisebox{1.5pt}{--}\kern-7ptXT+α)∗(yo¯+∅)}(1+do¯\raisebox{1.5pt}{--}\kern-7ptXT)∗(ņo¯+β)(mo¯+do¯)\raisebox{1.5pt}{--}\kern-7ptXT∗l(ξ)}

The identification of DF is achieved by computing error-free data, and the LE is produced by using Eq. (9). The recognition is indicated as ρ. WB error is obtained through the “consequential way,” which accurately depicts the data. The time interval is reduced by mindt(xt+α). In xt−d0, the patients’ walking patterns are determined. Because (1+d0xt)is linked with DF in a certain time interval, it employs the ∂(γ). Fig. 2b depicts the features-based posture categorization.

Features are examined in 1+(m0+o′)f0, which indicates the consequential data series and the analysis. The improved DF is seen by determining Eqs. (8) and (9). The ∏g0αβ(∂)∂+xt is related in this instance, and error is detected in the specified time interval. They are utilized to calculate n0+β.

(10) ρw(ϑ)=∑ξl∅+(1+(mo¯+o′)fo¯)∗∫ņo¯fo¯(įo¯+ç′+þåα)+(\raisebox{1.5pt}{--}\kern-7ptXT−dT)∗(ņo¯+β)+(mo¯+do¯)\raisebox{1.5pt}{--}\kern-7ptXT∗l(ξ)+(1+do¯\raisebox{1.5pt}{--}\kern-7ptXT)

Walking is identified via DF and is utilized to perform the classification process. It is denoted by ρω(ϑ). The serial and current data are calculated. Incorporating an RF method improves accuracy. Time-based interval is utilized to make the decision in this method. Also, DF is observed in a better way.

Through a comparison study, this section explains the performance evaluation of the suggested EDF approach. The comparison takes into account the metrics identification accuracy, fusion error, and recognition time. In this comparison study, the suggested EDF is added to the current techniques TCNN [20], MVEA [22], and LSTM-CNN [15]. The material in [34–36] is used to analyze the suggested approach. The above-mentioned metrics are estimated using Dataset A from the source, which is 16 MB in size and contains the posture patterns of 20 participants. In this study, five participants were chosen from a total of 20 to examine the recognition of 12 occurrences each. Sub1, Sub2…, Sub5 were the names of the subjects, and Sub1 and Sub4 were young subjects. Through training, 110 posture patterns were assessed on the front and back from the observing point, with a maximum of 20 features retrieved and a 4 s observation interval.

The EDF method has certain limitations, such as its inability to handle uncertainty and inconsistency. Combining data from many sources with a multisensory DF algorithm exploits the data redundancy to help minimize the uncertainty. These sources can lead to inconsistent data and poor fusion when the multisensory DF’s performance is less than that of each individual sensor.

The other limitation is the inability of EDF to address the diverse needs of the patients. The research focuses on a specific group of patients, which implies that it cannot address the needs of all the patients requiring specific treatment. The research focuses only on posture recognition accuracy and cannot be applied in other areas. It is thus important to improve the results of the study to ensure that EDF it covers other areas and concerns of the patients.

This paper discusses the EDF approach for increasing the accuracy of posture identification through multi-feature analysis. In the beginning, the patients’ walking patterns are observed at various time intervals. The characteristics are then evaluated in relation to the saved data utilizing an RF classifier. This procedure is dependent on several time periods in order for the iterations to efficiently detect classification mistakes. Finally, conditional training is utilized to fuse the disaggregated errorless data to find the posture pattern that fits the stored pattern. Patterns and features are frequently evaluated in this classification process, and conditional training is computed depending on the prior error in order to improve identification accuracy. The results reveal that the proposed approach improves accuracy while reducing fusion and detection errors as well as computation time.