Analysis of physical health with internet of things-based computational narrowband physical health framework

Abstract

BACKGROUND:

Physical health is vital to the improvement of our skills and the enhancement of eye movements. The coordination of good body movement helps to establish a safe position of the body. The challenging characteristics of physical education include insufficient time allocation, inadequately trained teachers, and inadequate provision of the equipment is considered as an important factor.

OBJECTIVE:

In this paper, IoT-based Computational Narrowband Physical Health Framework (IoT-CNPHF) has been proposed to strengthen adequate time allocation, appropriately qualified teachers, and sustainable provision in the physical education system.

METHOD:

Massive extended range analysis is introduced to enhance the duration and time allotted for physical activity that helps in creating awareness about the importance of physical activities and sports in our daily life. The multimodal supervised technique is incorporated with IoT-CNPHF to improve the knowledge of physical education for the teachers and to provide suitable provision for students in the physical education system.

RESULTS:

The simulation analysis is performed based on accuracy, performance, and its efficiency proves the reliability of the proposed framework.

Keywords

Physical health IoT trained teachers physical activity physical education system

1. Technologies and their advancements in physical health monitoring

Internet of Things (IoT) is one of the most important, newest, and quickest communication mechanisms in this era. IoT consists of lightweight devices and sensors incorporated in smart objects for the regular activities connected to the internet through wireless sensor networks that lead to new, previously non-potential data sharing methods. IoT has many services among these e-health applications is the most effective and essential innovative framework to mitigate real-life issues. People’s perceptions are continuously revised with the advancement of science and technology and culture. Through the conventional artificial constitution to the science and systems research, physical health identification is further improved [1, 2, 3]. A substantial number of experiments are currently being carried out on IoT technologies in the area of e-health. The e-health term, which promotes electronic communication and process strategies, has been recently developed for healthcare management. Patients’ treatment in their places, such as homes, hostels, etc., is a possible way to solve the health care system’s problem. For example, many types of patients who have chronic illnesses who simply require surgical supervision, the elderly, and patients with congenital heart conditions do not have to use a hospital bed since they are available for home treatment. The significant concern is how health practitioners can correctly track their patients’ health status effectively and safely without visiting them. Therefore, the device must be able to optimize patient mobility while enhancing protection and autonomy at the same time. Continuous patient tracking [4, 5] is provided by e-health services such as smart devices and mobile phones. This will carry various benefits, such as saving costs, shipping, insurance, and health services. This can continue to ensure healthy communication between service providers and patients, resulting in increased healthcare outcomes and individuals’ saving time [6]. Apart from medical care, physical health monitoring systems can be applied for education [7], industry [8], sports training [9], governance [10], etc.

The novel coronavirus (COVID-19) marks the importance of physical activities in the daily routine process. Being healthy and immune must need effective physical practices and maintenance. The pandemic has caused many people around the world to live at home for a while. For the 6 to 17-year-olds, the WHO suggested 60 min/day of mild to intense exercise. For the 3 and 2 days a wk respectively with muscle and bone support, for the adult and the elderly 75 min/kg intense or 150 min/kg moderate physical activity. Practical aerobic exercise guidelines on bicycle or rudder ergometers, bodyweight training, and dance and successful computer games may help mitigate the physical and emotional harmful effects of COVID-19 safety legislation. This remark offers important knowledge about home-driven physical exercise for sedentary citizens during the lifetime of the pandemic or other outbreaks of infectious disease, particularly children and teenagers [11]. Physical education is an educational program focused on the improvement of mental and physical fitness. Physical education technology development is not encouraged since the trainers and students keep the distance between the trends and stuck up with the outdated education system. According to the conventional philosophical ideology of everyday activity, there is a significant scientific disparity between physical education teachers and students [12].

One of the advantages of integrating technology in physical education is that training can be improved beyond participant and team sports techniques, expertise, and regulations. Physical education in other fields, such as algebra or geography, should be coordinated, and physical training should be personalized so that students encounter perfect obstacles. It has been seen that technology development in the physical education system implies efficient time management, resource allocation, and teacher training, etc. It is especially necessary to examine the physical teaching approaches that are compatible with and enhance educational results in colleges and universities. With more advances in the technology in augmented reality, the Internet of Things (IoT) technology [13], the cloud platform [14], and mobile internet [15] and other specialized information technology, the organization is providing the opportunity to create a virtual reality service [16, 17] for the college of physical education, based on the IoT and a cloud platform.

Figure 1.

NB-IoT applications.

Narrowband-Internet of Things (NB-IoT) is described as a new digital mobile radio communication infrastructure based on Long-Term Evolution (LTE) launched by the 3rd Generation Partnership Project (3GPP) for minimal energy Wide-Area Networks. The principal goal of NB-IoT is to promote massive machine-type communication and allow minimum-cost, minimum-power, and minimum-data – rate communication. NB-IoT is based on LTE architecture with some modifications that satisfy the gigantic device to device communication [18, 19, 20]. Figure 1 describes the major applications of narrowband IoT services in real-time. NB-IoT is an IoT variant that is shorter and thinner. For its service, it takes a small frequency band [21]. However, the main performance metrics in which subscribers are concerned to use NB-IoT for realistic purposes are latency, throughput, and system capacity. Some early studies examine NB-IoT’s potential for devices as the count of supported systems [22, 23].

It will be worth designing wearable healthcare solutions for the physical education system that are based on the current NB-IoT standard as far as communications protocols are concerned. As this is an incredibly recent standard, no established study has applied it in a healthcare setting, considering its substantial benefits for this area. Use of NB-IoT to validate its adequacy before using it as a specific communications standard for a healthcare system for physical education established in conjunction with this paper’s proposed model. The health care service and physical activity tracking using NB-IoT technology lead to the research problem formulation. This paper presents an IoT-CNPHF (IoT-Computational Narrowband Physical health Framework) to improve sufficient time distribution, trained teaching personnel allocation, and efficient provision in physical education programs. Wide comprehensive research is carried out to raise awareness of the importance of physical exercise and sports in everyday life. The objective is to enhance the length and time allowed for physical activity. The IoT-CNPHF incorporates the multimodal supervised methodology to improve physical education for instructors and provide students with sufficient resources in the physical education sector.

The paper is outlined as follows: Section 2 gives a brief idea behind some related works referred to this research. Section 3 describes the design framework of IoT-CNPHF that is incorporated with multimodal supervised methodology. Section 4 gives the summary of the experiment with results and discussions. Finally, the paper is concluded and future works related to this research are given.

2. Related works

The literature review done for the research is summarized in this section. The section covers some of the significant works considered for the study and its contributions. Each work described below helped the study by evaluating the existing physical health monitoring models and the scope of physical health monitoring in physical education with advanced technology. Further leads to the proposed model in this research.

Gosh et al. [24] proposed the health care system for hospital administration to enable online monitoring of the patients’ health conditions remotely by guardians and physicians. Authenticated knowledge exchange was its primary focus of remote control and guidance understanding. The work developed a remote health monitoring system with cloud and mobile technology. The model captured data from patients automatically and stored the data collected in the cloud for continuous use to help healthcare practitioners remotely track the healthcare system. More patients were tracked at once in the hospital administration using the proposed framework. The device further lets patients’ guardians know information about their welfare. The machine was compact as well. It supported patient health care units and guardians to take the necessary prompt action and minimized the risk.

Gogate et al. [25] proposed a cardiac health tracking device using wireless sensor networks that track heart patient body parameters, including Heart, Temperature, and SPO2. It allowed caregivers and health care professionals to track and store the body parameters of patients regularly. It alerts caretakers about any abnormality that happens to the patient’s report. The authors expanded that the data could be accessible for remote users through the internet and for unique guidance to registered users such as remote experts. The work successfully achieved criteria such as usability, reliability, accuracy, and performance. Compared with traditional treatment applications and commercial tools such as FitBit, about 95 percent accurate findings were obtained. Even though this work was limited to cardiac health analysis, it showed the effectiveness of wireless sensors in health monitoring and opened the way to IoT in the respective field.

Jemal et al. [26] developed an all-embracing framework infrastructure named pervasive patient health monitoring (PPHM). PPHM was centered on embedded cloud computing and the internet of things. A case study for real-time monitoring of a patient with congestive heart failure using ECG was performed to illustrate the suitability of the new PPHM infrastructure and noted better efficiency with higher classification accuracy, scalability compared to other frameworks.

Krishnan et al. [27] presented the system intended to use at home with patients who do not face a life-threatening condition but needed to be supervised by a doctor or family with a good time. This method was still used from its build-up compared with the conformist approach, but it was difficult to operate separately and scale. Costs were much better for the better system since it was used with Arduino-uno, and it takes about 1 minute to obtain the same outcome. In contrast to the usual method, this system included more medical equipment on a single system chip. The IoT-based health monitoring thus resulted in the best performance for individual health monitoring.

Verma et al. [28] implemented a fog layer on a gateway to the health monitoring system that needs fast processing and a minimum delay. They classified the patient’s health as healthy or unhealthy by using fog-computing services. Real-time events for computing event adversity have been tracked at the fog layer. Besides, a protocol was introduced to cause an incident to move a critical signal from patients’ wellbeing to the cloud layer as patient status moves to an unknown situation. The cloud level is determined to detect the situation’s urgency by the patient’s temporal health index (THI). The successful decision-making was ensured by correlating various incidents in terms of temporal data modules. The dissemination of information to the cloud respondent plays a vital role in the control of medical emergencies. Finally, the effectiveness of the proposed framework was further improved with a real-time alert generation with incident magnitude estimation.

Nayyar et al. [29] suggested a BioSenHealth 1.0: The Innovative Patient Health Management Device for with Internet of Medical Things (IoMT). The BioSenHealth 1.0 prototype was used for the real-time tracking of patients’ vital statistics on pulse rates/heart rate, oxygen level, and body temperature for transmitting live data through thingspeak.com to physicians. The equipment thoroughly checked in 50 $+$ live hospital patients and the accuracy calculated in conjunction with current health monitoring systems was more than 90%. In terms of low cost, precision, portability, and rapid response time in real-time activities, the authors made significant enhancements in health monitoring with BioSenHealth 1.0.

Wu et al. [30] describe a portable hybrid network sensor device for safety and health control applications linked to the Internet of Things (IoT). The device was designed to increase safety in the operating world. The proposed system involved a wearable body area network (WBAN) in processing user data and a broad-band low-power (LPWAN) network for linking WBAN to the internet. In the WBAN, wearable sensors were used to measure the subject’s ambient conditions utilizing a safety node and to track critical signals with a health node. The proposed network included an autonomous local server (gateway), which was processed the raw sensor signals, view environmental and physiological data, and activated an alarm in case of an emergency. A cloud IoT server was introduced to connect the application with the internet, adding further features, such as online tracking and smartphone apps.

Manogaran et. al. [31] developed wearable smart-log patches with IoT sensors using multimedia. The data measurement in that smart log patch was evaluated using Edge Computing on Bayesian Deep Learning Network (EC-BDLN) to map and detect different physical information obtained from individuals reliably. This advancement Bayesian neural network-based edge computing model (EC-BNN) reduced and classified different physical data from individuals with robust predictions in data classification on health or physical activity. This multimedia-based wearable IoT device was tested using preliminary findings and addressed efficiency, accuracy, average residual error, reduced energy usage, and delay.

Shi et al. extended the health monitoring technology to integrate Narrowband IoT (NB-IoT) in it. They developed an NB-IoT-based human health surveillance framework to capture pulses, body temperatures, and locations with a terminal setup containing an STM32 microprocessor. They built a Tomcat Registry for cloud storage of uploaded information in the MySQL database, combined with the current NB-IoT network. The alarm service was designed for handling an emergency; the device would email the user’s GPS position information to the maintainer. Further, a web framework was formulated to map the line chart of previous reports. The experimental findings indicated that the designed human health surveillance system could perform real-time monitoring functions.

The literature review executed for the research resulted in the following points:

•
Even though there are many advancements in the health monitoring process, the gap between health monitoring and physical education is still visible
•
NB-IoT-based physical health framework needs extensive exposure in real-time applications
•
Physical fitness awareness among individuals must be improved through efficient teachers/trainers in the present situation. Adequate time allocation is necessary for physical activities by effective use of health monitoring

This paper considers the above valid points for designing the physical education framework. This research suggests an IoT-CNPHF for optimizing the adequate hour’s distribution, skilled trainer assignment, and appropriate provision for physical education programs. Broad research is being conducted to increase awareness of the importance of outdoor physical fitness in daily life and maximize physical activity’s duration and time. The IoT-CNPHF integrates a multimodal supervised approach to enhance teachers’ physical training and offer adequate support to students in physical education.
3. IoT-based computational narrowband physical health framework (IoT-CNPHF)

This section describes the proposed IoT-CNPHF model’s design procedure and the detailed narration of concepts behind the model. In a wide array of uses, NB-IoT is being employed in real-time. The IoT-CNPHF incorporated NB-IoT in wireless body area networks (WBAN) to provide a low-cost intelligent remote health monitoring system for effective physical education training. This model consists of wireless sensory devices that communicate to transmit or receive information over long-range wireless networks. IoT-CNPHF allows the entire environment and all the people in the Institution to be used and covered by the already deployed cellular base station. IoT-CNPHF ensures that, even if battery-powered, the end-user terminals (sensor nodes) to have a long service life. The IoT-CNPHF enhances adequate time distribution, qualified teaching staff allocation, and efficient provision of physical education programs. To raise awareness of the importance of physical activity and sports in daily life, extensive systematic research is carried out. The implementation goal of IoT-CNPHF is to increase the duration and time of physical activity allowed. The IoT-CNPHF integrates a multimodal supervised approach to improve teachers’ physical training performance and provide students with appropriate physical education opportunities. Network-related sensors gather rich data to measure the patient’s physical and mental condition, whether placed on the body or in the physical environment.

Figure 2.

Basic layout of IoT-CNPHF model.

Figure 2 illustrates the basic layout of the IoT-CNPHF model. The sensors attached to an individual’s body fetch the signal related to physical activities and health conditions. The information from the body area network has been observed by the wireless sensor network maintained for individuals’ health and physical activities to do intelligent physical education training. Further, information is stored in the NB-IoT cloud for secure remote access through routers and access points in it. The information regarding many individuals has been recorded in the cloud for trainers’ and self accessibility. The well-trained physical mentors’ can monitor their clients through smartphones and computers at the remote end. Based on the dissimilarity in the physical parameter values compared to typical values recorded, trainers suggest the recommendations needed for the particular client. In this way, IoT-CNPHF ensures adequate time allocation for exercise. This research primarily focuses on the design principle of NB-IoT for the IoT-CNPHF simulation model. Two different design principles have been applied to the health framework as follows:

Single-sensor node design: Each sensor node is treated as an independent node in this design, and each has its transmission module. Therefore, with the data rate and latency specifications, every node sends data directly to the base station (BS). In this design, multiple transmission links to the base station are required for each individual’s health monitoring

Multi-sensor node design: Unlike Single-sensor node design, all sensor devices are connected to the CPU responsible for data transmission to the base station and data processing. For every patient/client with a B, the design needs only one transmission component. This makes the IoT-CNPHF more efficient for health and activity monitoring in physical education

The IoT-CNPHF architecture considers a Long Time Evolution (LTE) network based on Device to Device (D2D)-enabled Orthogonal frequency-division multiple access (OFDMA), where numerous devices upload the content within, following an NB-IoT inband solution. In IoT-CNPHF, a cooperative NB-IoT system is allowed by D2D, where the Base Stations (BSs) in LTE is scheduled D2D communication connections. The device to device links within a cell has been triggered to import information collaboratively in groups following an NB-IoT inband solution, depending on the channel status and vital cellular link’s transmission rate.

Figure 3.

Device to Device (D2D) communication in LTE.

The network’s central area has an LTE cell working over a licensed band spectrum, as shown in Fig. 3. Direct D2D communication has been applicable if the path length between two user equipment is over a specific range. The Signal-to - Interference-plus-Noise Ratio (SINR) criterion is being obtained with the proposed system. The core network is a small-scale environment, where connectivity assurance is given by an LTE cell. When emergencies occur, terminal user equipment (UEs) and trainers obtain updated knowledge to manage the corresponding situation as part of physical education. Hence, the purpose of the IoT-CNPHF system is to provide D2D communications with high capacity and link connectivity because the updated information can be disseminated in NB-IoTs efficiently and timely. At Home evolved-Node B (HeNB), the paper considers a Time Divison Multiple Access (TDMA)-based scheduler, which is responsible for assigning the Resource Blocks (RBs) to the scheduled unit. If D2D communication has been used, UEs, which can be simultaneously stimulated and grouped together, are chosen, and separate RBs are used to prevent interference. The following dictates the D2D communication Model and LTE communication model separately.

3.1 D2D communication model

Simply stated, for effective spectrum resource use, two forms of D2D communication systems is described, i.e., unlicensed and licensed spectrum services. The first scheme is divided into three types of modes for recycling the wireless spectrum with mobile users as underlay, overlay, and collaborative modes. The overlay and underlay modes for D2D communications are first described briefly. After that, the collaborative modes of communication are clarified later in this article.

Overlay and Underlay modes/strategies: In the underlay D2D communication mode, the UEs share the same spectrum band concurrently with both cellular and D2D communications. The handling of induced interference between D2D and cellular transmission links is the key challenge. In the D2D overlay communication mode, before allocating network resources (channel or RB) to D2D communication links, the transfer rates of cellular connections should be reached. Although overlay mode can decrease the interference between cellular links and D2D communication, spectrum reuse network efficiency is lower than underlay mode. Limiting network interference is the key concern of the underlying D2D communication mode.

$\displaystyle\textit{IF}_{i}=\Sigma_{l\in L}b_{l,i}\ast E_{l,i}\ast C_{l,i}+% \Sigma_{i^{\prime}\in K-\{i\}}b_{i^{\prime},i}\ast E_{i^{\prime},i}\ast C_{i^{% \prime},i}$ (1)

Where IF denotes interference in $L$ and $K$ cells, $b_{l,i}$ and $b_{i^{\prime},i}$ represents the binary variable; (for cellular (D2D) transmission, it is set to 1). $E_{l,i}$ and $E_{i^{\prime},i}$ is the notation used to denote the power consumed in D2D transmission whereas $C_{l,i}$ and $C_{i^{\prime},i}$ is the channel gain for transmission. When a D2D path $i^{\prime}-i$ is enabled for information dissemination, it computes the interference at node $i$ by Eq. (1).

$\displaystyle\textit{SNIR},S_{i^{\prime},i}=\frac{(E_{i^{\prime},i}\ast C_{i^{% \prime},i})}{(N+\textit{IF}_{i})}$ (2) $\displaystyle D_{i^{\prime},i}=M\text{log}_{2}(1+S_{i^{\prime},i})$ (3)

Equations (2) and (3) give the SINR formula for the transmission of the underlay D2D from UEs $i^{\prime}-i$ and gained data rate for the same, respectively. The $N$ represents the thermal noise associated with the data transmission, $D_{i^{\prime},i}$ is the variable to represent the data rate gained from the D2D transmission with bandwidth M. A part of an OFDMA frame for D2D communication keeps unfill for the overlay mode. For information exchange, the cellular UEs use a portion of the whole frame length (F), and the remaining frame length is exploited by the D2D UEs.

$\displaystyle D_{i^{\prime},i}=(1-F)*M*\text{log}_{2}(1+S_{i^{\prime},i})$ (4) $\displaystyle D_{l,i}=F\ast M\ast\text{log}_{2}\left({1+S_{l,i}}\right)$ (5)

The associated transmission data rates of overlay mode activated path in D2D-enabled links, and cellular-based links are measured using Eqs (4) and (5). In this mode, interference between cellular and D2D links does not take place. Frame rate has been defined as the segment of the required data rate to the maximum achievable cellular link data rate to boost the transmission rate of $D_{i^{\prime},i}$ . Even though overlay mode has not been efficiently reusing the wireless spectrum than the underlay model, it keeps a lower overhead on the control channel.

Collaborative mode: In D2D-enabled NB-IoTs for IoT-CNPHF architecture, cooperation between terminal devices plays a crucial role because of the resource-efficient economy and simple deployment. It allows greedy devices to forward information to others and chooses the ideal relay method to exploit wireless spectrum resources completely. A relay is necessary If wireless users’ overall transmission rate cannot meet the threshold rate. It is further a realization that communication between devices is being done in a multi-hop fashion, even though a direct path exists. The explanation related to this seems to be a high power requirement for direct long-distance, which brings more interference to other concurrent transmissions. However, data has been acquired and transmitted through the omnipresent deployment of sensing, computing, and communication infrastructures; it is expensive and consumes energy. Broadly speaking, the purpose of the collaborative mode is to enhance the amount of the link capacity of the D2D user, given that the data requirements of cellular UEs are being met. Owing to its benefits for NB-IoTs, this IoT-CNPHF focuses on the nature of collaborative D2D communications.

3.2 LTE-based D2D Communication model

The BS (HeNB) is responsible for assigning Resource Blocks (RBs) before setting the same to scheduled UEs in an LTE-based communication model. This network is configured as a directed Graph $G=\left({V,E}\right)$ , where $V$ represents the nodes/devices, and $E$ represents the connecting edge between them. The overall SINR obtained by UEs $i^{\prime}$ to $i$ via LTE enabled D2D communication has been calculated by Eq. (6) concerning the free space propagation loss model.

$\displaystyle\textit{SINR},S_{i^{\prime},i}=\frac{E_{i^{\prime},i}*C_{i^{% \prime},i}}{N+\Sigma_{v\in V-\{i^{\prime}\}}(E_{v,i}*C_{v,i})}\geqslant\tau_{D% _{i^{\prime},i}}$ (6)

The propagation Power (Energy) and channel gain of entangling node pair $i^{\prime}-i$ are represented as $E_{i^{\prime},i}$ and $C_{i^{\prime},i}$ , respectively. $N$ shows the thermal noise. $V$ is the device group. Since both systems have a half-duplex communication mode, the concurrent LTE enabled D2D link is sent to the same resource block. If the observed SINR value reaches the threshold $\tau_{D_{i^{\prime},i}}$ with $D_{i^{\prime},i}$ transmission rate, then direct relation transmission from $i^{\prime}$ to $i$ is appropriate. In other situations, the transmitting duty in a direct link is accomplished by multi-hop transmitting. The model indicates that all sub-carriers within one RB are in the same channel constraints; hence the D2D UEs and cellular use the same portion of the radio spectrum. It should be remembered that the key distinctions for D2D-enabled and cellular transmissions depend on the regulation of transmission intensity and the synchronization of interference between concurrent transmission links.

$\displaystyle\textit{IF}_{l}=\Sigma_{i^{\prime}\in K-\{i\}}b_{l,i\ast E_{l,i^{% \prime}}}\ast C_{\textit{t}{l^{\prime},i}},$ (7) $\displaystyle\textit{SNIR},S_{l,i^{\prime}}=\frac{(E_{l},i\ast C_{l,i})}{\left% ({N+\textit{IF}_{l}}\right)}$ (8) $\displaystyle D_{l,i}=M\text{log}_{2}\left(1+S_{l,i}\right)$ (9)

Defines $L$ and $K$ as cell sets and D2D UEs, where $L$ union $K$ gives $V$ . Let $b_{l,i^{\prime}}$ and $b_{i^{\prime},i}$ be binary variables that signify if the cellular-based relation is enabled for cellular node ties $i$ and $i^{\prime}$ to $i$ , respectively. If $j$ and $j$ users can communicate directly with each other, a D2D-enabled mode can be created. Over to that, an $j$ intermediate node is necessary to support the transmission with D2D and denote the $i^{\prime}-j-i$ node community as a single session. When an uplink from the cell user (client) to the base station or vice versa is considered, the interference caused by the same RB can be determined by means of concurrent transmissions using Eq. (7). Thus, the cellular consumer (client) $l$ ‘s SINR is determined by using Eq. (8), and the corresponding achievable data rate is estimated with Eq. (9). Herein, M, the current RB’s bandwidth is, and we assume that any link’s bandwidth is the same for simplicity.

IoT-CNPHF makes the model highly competitive and increases the efficiency of physical fitness tracking of physical education coaches to allocate resource blocks accurately. The decision mechanism extracts these aspects into a smaller memory training subclass for effective guidance. However, with more extensive data sets from the physical education industry, the required preparation time is expanded. This study further extends with a randomized controlled experiment for two-group: A hundred individuals would be recruited with recurrent non-specific LBPs. A group workout program will be offered to participants assigned to both classes.

Furthermore, the intervention party will be coached by healthcare for helping individuals fulfill their targets for physical exercise and an activity monitor. A shameful wellness coaching for encouraging people to address their LBP or other concerns, without clinical advice from the physiotherapist and a scam-activity monitor is provided for the control category participants. At baseline and at the 4, 8, and 12 months after randomization, result variables will be measured. Physical exercise, scientifically measured using an accelerometer, pain level, and injury would be the main finding at a period of 4 months since randomization.

4. Results and discussions

The IoT-CNPHF model was evaluated in two ways: Case I for performance evaluation of proposed NB-IoT network configuration, and Case II evaluated the overall performance compared to existing IoT-based Physical Health Monitoring systems. The first theoretical setup consists of a virtual simulation setup and actual trace-based simulation. Up to 75 nodes are assigned in a random manner within a single cell for a virtual simulation setup. Data demands between 45 and 60 Kbits were generated consistently. For cellular and D2D communication, the maximum coverage areas were 650 m and 150 m. The machine model and simulation of space-based channels for the LTE-based D2D communication model was conducted using WINNER II channel models in MATLAB. In the simulation, the geometry and position details were checked with an arbitrary number of base stations (BS) and mobile stations (MS), with a 75-node fixture. The model channel allowed both LOS and non-LOS propagation conditions to be simulated by the model. It led to several possibilities for indoor and outdoor propagation. The streaming channel with channel coefficients developed by the WINNER II was used further.

It was used SIGCOMM09 data sets for performance assessment in actual trace-based simulation environments. It contains social details and contact information for 150 mobile phone generators, and users log details onto social media to get their profiles and activities. Furthered obtained its related physical proximity information by reviewing communication reports between UEs. The performance of the configuration setup for both the setup was analyzed using the parameters, throughput gain, and Service disruption probability ratio, as shown in the below figures.

Figure 4.

Throughput gain analysis by virtual simulation.

Figure 4 noted that the average throughput gain was obtained from different strata of the UEs Count. The throughput gain graph showed the maximum throughput with 70 UEs and a minimum with 10 UEs. The graph showed that when the number of UEs increases, average throughput rose. Further, it means the data transfer rate was improved with more number of UEs.

Figure 5.

Service disruption probability analysis by virtual simulation.

Figure 5 illustrated the service disruption ratio observed for various groups of UEs, and it resulted in the conclusion that lower the disruption for a higher number of UEs. This result ensures the effective utilization of time for both the trainers and clients in physical education. Single trainers can manage many clients at a time for monitoring physical activities.

Figure 6.

Throughput gain analysis by actual trace simulation.

It was observed that the throughput gain of proposed NB-IoT by actual trace-based simulation nearer to that of the same by virtual simulation. Like virtual simulation analysis, throughput gain increased with the increase in the number of UEs and ensured better performance for IoT-CNPHF in the physical education system (Fig. 6).

Figure 7.

Service disruption probability analysis by actual trace-based simulation.

In the actual trace-based simulation experiment, the service disruption probability ratio was limited to 30%, which was suggested for the proposed model’s practical implementation (Fig. 7). The proposed IoT-CPHF was compared with the existing models in terms of its performance by precision, accuracy, and reliability. The system’s stability enables it to execute in non-homogeneous IoT environments with changing UE’s count. External resources and mobile sensors can calculate the application’s main tasks for personal trainers, individuals, and makers.

The case application mentioned here was a primary example where the infrastructure can not supply adequate processing power. Other scenarios are inadequate for processing data for thousands of users (i.e., a marathon). In these situations, the proposal submitted provides a solution that significantly reduces hardware needs and resolves complex physical education applications across multiple users with all available resources. Comparing the overall performance ratio obtained for the same experimental inputs in four different frameworks, including the proposed model, is described in Table 1. From this, the IoT-CNPHF framework for an effective physical education system with 91% performance ratio. Furthermore, the research was elaborated with the Huge extended range research implementation to increase the length and time allocated for physical exercise and to raise awareness about the importance of physical activities and sports in our everyday life.

Table 1

Comparison of overall performance ratio

Models	Performance ratio
EC-BDLN	0.813
PPHM	0.85
BioSenHealth 1.0	0.89
IoT-CNPHF	0.91

The IoT-CNPHF model was incorporated with a randomized experiment in two classes: 100 adults with recurrent, non-specific Low Back Pain. A group fitness schedule was issued to individuals assigned to all classes. In addition, fitness coaching (i.e., lets the participants met their aim for the physical exercise, and an activity monitor (i.e., Fitbit Flex) was provided to the intervention team. A shame medical coaching (i.e., advised to chat about their LBP and other conditions but not with any physiotherapist therapist’s guidance) and a shame behavior monitor are given to the person assigned to the control group. At baseline and 4, 8, and 12 months after randomization, the outcome measures were analyzed. Physical exercise, target assessed using an accelerometer, and pain level and injury at 3 months of randomization will be the primary outcomes.

5. Conclusion

The primary contribution of this study was to improve sufficient time distribution, adequately trained teachers, and sustainable supplies in the physical education System through IoT-CNPHF (IoT-based Computational Narrowband Physical Health Framework). This framework implemented a proposed NB-IoT network configuration for effective resource block allocation. It was analyzed by a virtual simulation technique and an actual trace-based simulation technique. Significant extended-range research work was carried out to increase physical activity’s length and time, raising awareness of the value of exercise and fitness in our everyday lives. To improve the teacher’s understanding of physical education and provide adequate care for people in the physical education system, IoT-CNPHF integrated multiple supervision technology. In case I, a performance assessment of the proposed NB-IoT network configuration was carried out. In Case II, the overall performance relative to current IoT-based Physical Health Monitoring Networks was measured. It was reported that the IoT-CNPHF model had been recommended for practical application for the enhanced physical education system. In the future, an artificial intelligence-based decision support system has been integrated into IoT-CNPHF for developing the smart physical education system.

Footnotes

Conflict of interest

None to report.

Funding

This work was supported by the National Educational Science “Thirteenth Five-Year Plan” in 2018, the unit funding the Ministry of Education Planning Project (project approval number: FLB180677).

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