Research on Functional Modularity and Health Monitoring Design of Home Fitness Equipment

Abstract

Under the “Healthy China” strategy, the demand for home fitness equipment is increasing, but existing solutions face challenges such as large size, limited functionality, and lack of personalization. This study proposes an innovative integrated design framework for multifunctional home fitness equipment, combining modular design, space optimization, and intelligent health monitoring. The design integrates an exercise bike, rowing machine, and spring tensioner into a single unit, reducing equipment footprint by 30% while enabling seamless transitions between exercise modes. Multimodal sensors collect real-time physiological data, processed via Kalman filtering and adaptive algorithms to generate personalized fitness recommendations. The system achieves 95% monitoring accuracy for key metrics (heart rate: 97–147 bpm, energy consumption: 216–550 kcal) and improves user satisfaction by 40% compared to conventional equipment. This research demonstrates a scalable and intelligent solution that bridges the gap between multifunctional integration and user-centric health management, offering significant advancements over previous designs.

1. Introduction

With the accelerating pace of modern life and rising health awareness, home fitness has become crucial, especially under the “Healthy China” strategy [1]. It meets personalized needs and overcomes time–space limits [2]. Advanced tech like VR, IoT, intelligent devices, and big data make home fitness equipment more interactive, intelligent, and personalized [3]. Recently, VR-based fitness equipment entered the market, enhancing users’ interest and exercise science through feedback [4]. Intelligent fitness systems with wireless sensors and optical measurement technology can monitor heart rate, breathing rate, and exercise intensity in real time for more accurate health feedback [5,6]. The 2024 ACSM report shows the future fitness industry will focus on integrating intelligent fitness equipment with health management systems [7,8]. Home fitness equipment will achieve more personalized and accurate training programs with cloud storage and AI technology [9,10,11]. For example, improved algorithm-based smart wearables can track users’ exercise data and generate personalized recommendations [12,13]. The user experience has also been optimized. Modular design improves equipment’s scalability and flexibility for home users to combine freely [14]. The popularity of smart home devices enables fitness monitoring systems like HearFit, improving convenience [15]. Thus, the multi-functional integration and intelligent monitoring of household fitness equipment are the core of the future fitness industry [16]. Studying how to apply these technologies to home fitness equipment design is important [17]. Although the above-mentioned studies have optimized the functions and user experiences of fitness equipment to varying degrees, the existing home fitness equipment still has numerous drawbacks. Firstly, it occupies excessive space and fails to optimize household usability, especially in small households where long-term use is difficult. Secondly, its functions are rather single and fail to meet the diverse fitness requirements of users. Thirdly, there is a lack of comprehensive research on the practicality of fitness equipment and users’ personalized needs, which affects both the user experience and market promotion.

Compared to existing solutions, our proposed design offers several significant advantages in terms of both functionality and health monitoring accuracy. Traditional fitness equipment often has a large footprint and is limited to specific exercises, such as cycling or rowing. In contrast, our multifunctional design integrates an exercise bike, rowing machine, and spring tensioner into a single unit, reducing the equipment footprint by 30% while enabling seamless transitions between different exercise modes. This not only enhances versatility but also optimizes space utilization, making it ideal for home environments.

In terms of health monitoring, existing systems typically provide basic tracking of heart rate and energy consumption. However, they often lack the integration of multiple sensors and advanced data processing algorithms, which limits their accuracy and personalization. Our design incorporates multimodal sensors (heart rate, respiratory rate, temperature) and uses Kalman filtering and adaptive algorithms to process the collected data. This results in a monitoring accuracy of 95% for key metrics, such as heart rate (97–147 bpm) and energy consumption (216–550 kcal). By addressing the limitations of existing solutions, our design provides a more comprehensive and user-friendly approach to home fitness. The integration of multifunctional capabilities and advanced health monitoring not only enhances the practicality of the equipment but also promotes a healthier lifestyle through personalized guidance.

Based on this, the integrated design concept can effectively address these pain points. By integrating multi-functional modules, optimizing space utilization, and implementing intelligent human–computer interaction design, this concept not only enhances the equipment’s practicality but also offers users scientific fitness advice via the health monitoring system. This further promotes the development of a healthy family lifestyle.

2. Multi-Functional Integrated Analysis of Household Fitness Equipment

2.1. Functional Modular Design

Functional modular design is derived from the modular design concept. By dividing the product into independent and modular functional modules [18,19,20,21], it realizes the flexibility of customization, rapid development, and maintenance, improves the reusability and adaptability, and reduces the development cost and risk. In the design of home fitness equipment, through the different modules (such as aerobic, strength, stretching, etc.) that are independently designed, users can freely combine according to their needs, to achieve a personalized fitness experience.

In the design, the following key considerations are as follows: determine the type of functional modules, selecting the modules according to user requirements and space conditions; ensure that the interface between modules is clear, safe, and stable; introduce intelligent design to provide real-time feedback and guidance through sensors; and pay attention to adjustment and comfort to improve the overall experience. The modular design not only improves the versatility and flexibility of the fitness equipment but also saves space and adapts to different home environments.

2.2. Optimized Layout Design of Household Fitness Equipment

Space is an important constraint in the design of family fitness equipment, and especially in the modern small home environment, how to maximize the use of limited space is very important. To this end, the space optimization design should focus on the compactness and flexibility of the equipment to meet the needs of different home environments [22].

Folding and storage design: In home fitness equipment, the footprint of the equipment can be effectively reduced through the use of folding, rotating, and other design methods. For example, the key components of the exercise bike( METCON, Nantong City, Jiangsu Province, China), rowing machine(WaterRower Xiamen City, Fujian Province, China), and spring tensioner(Jiangsu Aierte Industrial Co., Ltd. is headquartered in Baoying County Economic Development Zone, Yangzhou City, Jiangsu Province, China) are reasonably integrated, and the base of the exercise bike and the rowing machine bracket can share a common part, which reduces the amount of material occupied and also enhances the overall stability of the equipment. After use, they can be stored by rotating or folding without occupying floor space when not in use [23]. In addition, devices such as treadmills ((METCON, Nantong City, Jiangsu Province, China) can also be designed folding mechanism, easily fold away, and reduce space footprint [24].

Multi-functional combination: The functional integration of the equipment is achieved through the design of multiple functions. Fitness equipment can be combined with aerobic exercise, resistance exercise. Exercise bikes and rowing machines, as representative aerobic training equipment, can enhance cardiovascular vitality and promote metabolism. While the spring tensioner belongs to the scope of resistance exercise training, it can enhance muscle strength and endurance. This combination not only enhances cardiorespiratory function but also effectively sculpts the body line, which not only meets the diversified training needs of users but also achieves the effect of overall health and fitness. By combining an exercise bike, rowing machine, and spring tensioner into one, the other parts can be hidden or stored away when using one of the functions. This not only makes full use of vertical and horizontal space, but also provides users with a variety of fitness experiences.

3. The Multifunctional Integration and Health Monitoring Design of Home Fitness Equipment

3.1. Structural Design Practice of Home Fitness Equipment

Based on the above multi-functional integrated analysis of functional modular design, spatial optimization layout, and human–computer interaction design, the structurally innovative design of home fitness equipment is obtained [25]. This home fitness equipment combines three types of exercise equipment: an intelligent exercise bike, a rowing machine, and a spring tensioner. Combining aerobic exercise and strength training can effectively exercise many muscle groups in the body, such as legs, buttocks, waist and abdomen, back, shoulders, etc. Its structure is reasonably designed in the functional layout [26].

As shown in Figure 1 and Figure 2, the height of the seat, the distance between the seat, and the armrests, as well as the height of the armrests, can be adjusted according to the human body parameters of different exercisers; the fixed straps on the footrests and the foot braces ensure safety during exercise.

In Figure 1, the front part of the exercise bike is the bracket of the rowing machine module, and the front bracket is perfectly nested with the front part of the exercise bike by shrinking and rotating, which realizes the function of storage and greatly saves space. In Figure 2, a smart device holder is provided at the handrail, which is convenient for users to place a tablet computer or mobile phone; meanwhile, the monitoring devices on both sides of the handrail are able to monitor data such as heart rate and energy consumption in real time, and the exercise bike module mainly provides aerobic exercise. In addition, the spring tensioner component is placed on both sides of the exercise bike between the main bracket and the cushion, providing users with the option of resistance training. The direction of its pull is primarily oblique and overhead, enabling targeted exercise of muscle groups such as the deltoids, pectorals, and obliques. Figure 3 shows the unfolded state of the rowing machine. Rollers underneath the seat drive the user to slide in a grooved track for tension exercises. It is a combination of aerobic and resistance training equipment.

This design is significantly different from existing composite fitness equipment through three core breakthroughs: structural innovation, intelligent interaction, and functional synergy. In terms of structural design, the shrinkable rotating nesting technology is used to integrate the rowing machine module with the main body of the exercise bike, which saves space in the home and shortens the time for switching function modes than the traditional splicing design. In terms of human–computer interaction, the multi-dimensional adjustment of the equipment helps users to precisely adjust the position of the seat and armrests according to their own body shape, and combined with the real-time heart rate and energy expenditure monitoring device at the armrests, it can provide personalized exercise feedback. In terms of functional synergy, the data of the exercise bike, rowing machine, and spring tensioner are interconnected, and the system can automatically generate a cross-module training program based on the user’s fitness goals (e.g., fat loss, muscle gain), which improves training efficiency compared to traditional equipment. These innovations make the product uniquely competitive in terms of space utilization, user experience, and training science.

3.2. Intelligent Monitoring Design Based on Multidimensional Health Data

3.2.1. Acquisition Technology of Biological Signals

Biological signal collection is the basis of health monitoring. Common exercise data such as heart rate, respiratory rate, energy consumption, exercise duration, and other indicators can be collected through a variety of sensing technologies. In order to obtain comprehensive health data, various types of sensors are often integrated in intelligent monitoring devices, such as accelerometers, gyroscopes, heart rate sensors, blood oxygen sensors, etc. Multi-modal sensor fusion technology can effectively integrate data from different sensors. In the process, we exemplify some of the engineering difficulties and solutions.

• Space Optimization Trade-offs

One of the primary engineering challenges was optimizing the space utilization of the multifunctional fitness equipment. The integration of an exercise bike, rowing machine, and spring tensioner into a single unit required careful consideration of mechanical design, user accessibility, and overall stability. The primary trade-off was between compactness and ease of use. For example, while a highly compact design would save space, it might compromise the user’s ability to transition smoothly between different exercise modes. To address this challenge, we employed a modular and foldable design approach. The equipment was designed with shared components, such as a common base for the exercise bike and rowing machine, which significantly reduced the overall footprint. Additionally, the use of rotating and folding mechanisms allowed the equipment to be stored efficiently when not in use. Extensive user testing was conducted to ensure that the design did not compromise usability or stability. The final design achieved a 30% reduction in equipment footprint while maintaining user satisfaction and ease of use.

2.

Sensor Calibration Under Dynamic Movement

Calibrating sensors to accurately measure physiological data under dynamic movement was another significant challenge. Sensors such as heart rate monitors, accelerometers, and gyroscopes are needed to provide reliable data despite the varying motion artifacts introduced during different exercise modes (e.g., cycling, rowing, resistance training). Ensuring consistent and accurate data collection required addressing issues related to sensor placement, signal interference, and real-time data processing. To overcome these challenges, we implemented a multi-step calibration process. Initially, sensors were placed at optimal locations on the equipment to minimize interference and maximize signal quality. For instance, heart rate sensors were integrated into the armrests, while accelerometers and gyroscopes were strategically positioned to capture motion data accurately. Additionally, we employed advanced signal processing techniques, such as Kalman filtering, to reduce noise and improve data accuracy in real-time. The Kalman filter continuously adjusted its parameters based on the dynamic movement patterns, ensuring that the physiological data remained reliable and accurate throughout the exercise session.

3.

Integration of Multimodal Sensors

Integrating multimodal sensors (e.g., heart rate, respiratory rate, temperature) into a cohesive monitoring system presented both hardware and software challenges. Ensuring that these sensors worked harmoniously and provided synchronized data required addressing issues related to data fusion, sensor compatibility, and real-time processing. We developed a centralized data processing unit that integrated inputs from all sensors. The system used a combination of hardware synchronization and software algorithms to ensure that data from different sensors were aligned in real-time. For example, heart rate data from photoelectric sensors were synchronized with motion data from accelerometers using timestamping techniques. This allowed us to create a comprehensive health monitoring system that provided real-time feedback to users. Extensive testing was conducted to validate the accuracy and reliability of the integrated system, resulting in a monitoring accuracy of 95% for key metrics such as heart rate and energy consumption.

The fusion method of the Kalman filter algorithm is based on the system state model and observation model to optimally estimate the measured values from different sensors. For example, the exercise posture data measured by the accelerometer and gyroscope are fused with the heart rate data measured by the heart rate sensor to more accurately assess the athlete’s exercise intensity and physical condition. The Kalman filter algorithm continuously updates the weights of the predicted and measured values, which improves the accuracy and stability of the data.

To address the concerns regarding the Kalman filtering algorithm and the lack of experimental details and statistical analysis, the following comprehensive explanation is provided:

In this study, the Kalman filtering algorithm is employed to integrate and optimize the accuracy of physiological data collected from multiple sensors, such as heart rate, respiratory rate, and energy consumption. The Kalman filter continuously updates the state estimates by comparing predicted values with actual measurements, thereby minimizing noise and improving data reliability. The algorithm parameters are carefully set based on the specific characteristics of the sensors and the physiological signals being measured. For example, the process noise covariance (Q) is set to account for inherent variability in heart rate (HR), respiratory rate (RR), and energy consumption (EC), while the measurement noise covariance (R) is adjusted according to sensor accuracy and environmental noise.

The experimental setup includes a sample size of 60 participants, evenly divided into three age groups (20–30, 31–45, and 46–60 years) with balanced gender distribution. Participants performed a standardized exercise routine using the integrated fitness equipment, while physiological data were continuously collected at a frequency of 10 Hz. To validate the accuracy of the health monitoring system, statistical significance tests were conducted. The Shapiro–Wilk test confirmed the normality of the data. Paired t-tests showed no significant differences between the system’s heart rate and energy consumption measurements and reference values, indicating high accuracy. Additionally, a strong positive correlation was found between heart rate/energy consumption and exercise intensity, further supporting the system’s effectiveness. User satisfaction was also assessed, with a mean score significantly higher than neutral, indicating high user approval.

Heart rate monitoring uses a photoelectric sensor (Figure 2), where one places their palm/fingers on the armrests and measures the vascular pulse of their heart rate. Breath monitoring is via a PPG optical sensor, supplemented by an inertial or pressure sensor to compensate for motion disturbances. The PPG sensor penetrates the skin tissue by emitting a specific wavelength of light (e.g., green or red) and detects changes in the intensity of the reflected light. Inertial or pressure sensors assist in breath detection by monitoring small vibrations in the user’s body (e.g., chest rise and fall). Other data, such as energy expenditure and frequency of movement, are monitored by fusing encoders, PPG sensors, pressure sensors, magneto-resistive sensors, and intelligent algorithms (Figure 3). The sensors collect and send data to the processing unit.

Measuring real-time exercise data can help users keep track of their physical condition, adjust the intensity and frequency of exercise, and better achieve the effect of exercise and fitness. At the same time, these data are usually shown on the display of the fitness equipment, so that users can view them in real time, such as heart rate, cycling speed, exercise duration, etc., and can also store, analyze, and track the progress of fitness through mobile applications. The air outlet device at the bracket faucet of the equipment can be integrated with a resistance adjustment module, which monitors the cycling intensity in real time through the wind speed transmission and pressure sensing sensors, and adjusts the air delivery volume in a linked manner to provide users with a good fitness experience. In addition, thresholds for key health indicators can be set, such as the upper limit of heart rate and the lower limit of blood oxygen saturation. When the data monitored by the sensor in real time exceed the threshold value, the system will immediately issue an alarm to remind the exerciser to adjust the exercise intensity or take corresponding measures. For example, when the exerciser’s heart rate exceeds the safety threshold, the smart display will emit vibration and sound alerts to avoid physical injuries caused by excessive exercise.

3.2.2. Data Processing and Analysis

Data Source

The user’s exercise data are monitored in real time through fitness sensors, including exercise intensity, heart rate, respiration, energy consumed, etc. These data not only cover the movement status of various parts of the body but also combine personal information, such as age, height, and weight, to form a multidimensional health dataset, as shown in Figure 4. A brief description of the various types of sensors and multidimensional health data acquisition is as follows:

(1) Cardiac Sensors: These can monitor heart-related data and obtain indicators such as heart rate and heart rhythm. These data are essential for assessing cardiac function and cardiovascular burden during exercise. In research, heart rate and other cardiac data are an important part of multi-dimensional health data, which can help analyze the impact of exercise intensity on the heart, as well as for exercise risk assessment and health warning.

(2) Body temperature sensors: Used to measure the body temperature of the human body, the figure shows the body temperature as 36.5 °C. Body temperature is one of the basic indicators that reflect the health status of the human body, and during exercise, the change in body temperature can reflect the body’s metabolism and fatigue level. In multi-dimensional health data monitoring, body temperature data are combined with other physiological indicators to help have a more comprehensive understanding of the health status of the human body during exercise or daily state.

(3) Pulse/Oxygen Sensor: Pulse and oxygen saturation can be monitored at the same time. The pulse reflects the rhythm and frequency of the heartbeat, while the oxygen saturation reflects the amount of oxygen in the blood. In exercise monitoring, pulse and blood oxygen data are very important for evaluating the body’s oxygen supply and energy metabolism, which are important dimensions of multi-dimensional health data that can provide a basis for exercise intensity adjustment and health status judgment.

(4) Breathing Sensor: It can monitor breathing-related data, such as breathing rate, breathing depth, etc. Respiratory status is closely related to exercise intensity and the body’s oxygen demand, and in this study, respiratory data, together with other physiological indicators, constitute a complete sports health monitoring system, which helps to analyze the breathing pattern and the body’s adaptation during exercise.

(5) Designated/ automated search and rescue systems: Designated/automated search and rescue systems show location information (117.2° E, 39.13° N) and the “SOS!” sign in the image. The relevance of this system to multi-dimensional health data intelligent monitoring lies in the fact that when human health data are abnormal (such as high heart rate, shortness of breath, and other potentially life-threatening conditions), detected by various sensors, the system can automatically trigger rescue signals and provide accurate location information to achieve timely rescue. This shows that the intelligent monitoring system can not only obtain and analyze health data, but also take action in emergency situations to ensure the safety of people’s lives, which is an indispensable part of the whole research, and improves the complete chain from data monitoring to emergency treatment.

In summary, the various sensors and systems in the figure provide hardware support and technical support for the acquisition, analysis, and emergency treatment of multi-dimensional health data, which are highly related to the research on intelligent monitoring design based on multi-dimensional health data, and are the basic components of intelligent and comprehensive health monitoring.

4. Design of the Health Monitoring System for the Intelligent Exercise Bike

4.1. Collection of Human Physiological Parameters

4.1.1. Collection of Heart Rate

Heart rate (HR), the number of heart beats per minute, is an important parameter reflecting human health status. Heart rate varies with age, gender, and physical condition. With the increase in age, heart rate gradually slows down; among adults, the heart rate of women is slightly faster than that of men; those who often engage in physical exercise and manual labor have a slower resting heart rate. Under normal circumstances, the heart rate of normal adults in a quiet state is between 60 and 100 beats per minute, with significant individual differences.

Exercise heart rate refers to the heart rate maintained during exercise. Whether it is aerobic exercise or anaerobic exercise, an appropriate exercise heart rate range can bring better exercise effects. Maintaining the optimal exercise heart rate is important for both the exercise effect and personal safety. If the heart rate is too fast, it may be harmful to health, causing dizziness, chest tightness, and nausea; if the heart rate is low, it is not harmful to the body, but the exercise effect is not good.

Everyone has different exercise purposes, and different exercise purposes require different heart rate ranges to be controlled. Generally, the exercise intensity and exercise type are calculated and divided according to the percentage of the maximum heart rate, as shown in

Table 1. Classification of exercise intensity and exercise type.

Exercise Interval	20 (Years)	25 (Years)	30 (Years)	35 (Years)	40 (Years)	45 (Years)	50 (Years)	55 (Years)	65 (Years)	70 (Years)
100% (Maximum oxygen consumption, maximum exercise intensity) (bpm)	200	195	190	185	180	175	170	165	155	150
90% (Anaerobic exercise, physical fitness, strength training, etc.) (bpm)	180	176	171	167	162	158	153	149	140	135
80% (Aerobic exercise, cardiopulmonary exercise, strength training, etc.) (bpm)	160	156	152	148	144	140	136	132	124	120
70% (Weight control, fitness/fat burning) (bpm)	140	137	133	130	126	123	119	116	109	105
60% (Exercise, stay fit/warm up) (bpm)	120	117	114	111	108	105	102	99	93	90
50% (Exercise, stay fit/warm up) (bpm)	100	98	95	93	90	88	85	83	78	75

. The general formula for calculating the maximum heart rate is as follows: maximum heart rate = 220 − age. Around 90–100% of the maximum heart rate belongs to the highest-intensity exercise of maximum oxygen uptake, which is the limit of human exercise; 80–90% of the maximum heart rate belongs to anaerobic exercise, and its training goals are physical fitness, speed, and strength, etc.; 70–80% of the maximum heart rate belongs to aerobic exercise, and its training goals are cardiopulmonary function and endurance, etc.; 60–70% of the maximum heart rate belongs to weight control exercise, and its training goals are general fitness and fat burning; 50–60% belongs to light exercise, and its training goals are to maintain body shape or warm up before exercise.

Heart rate detection mainly utilizes the characteristic that blood is a highly opaque liquid, and the light penetration in general tissues is dozens of times greater than that in blood. The heart rate signal is detected by a photoelectric sensor, and intelligent heart rate detection is realized through single-chip microcomputer technology for data processing.

The photoelectric sensor adopts a fingertip transmissive photoelectric sensor. The light-emitting diode adopts a red light-emitting diode with a wavelength of 600–700 nm, and the phototransistor adopts a 3DU31 NPN type phototransistor with high sensitivity and good linearity with light changes.

Since the signal output by the photoelectric sensor is relatively weak and there is DC signal interference, the signal needs to be filtered and amplified. The filter amplification circuit mainly consists of three parts: the front-stage processing amplification circuit, the filter circuit, and the back-stage amplification circuit. The front-stage processing amplification circuit consists of a DC-blocking low-pass inverting amplifier, which suppresses high-frequency signals, removes DC voltage, preliminarily attenuates the 50 Hz interference signal, and preliminarily amplifies the useful heart rate signal with a magnification of 10 times and a cutoff frequency range of approximately 0.05–20 Hz. The magnification $A=R4 / R3$. According to the magnification of 10, select $R4=1 \mathrm{M}$ and $R3=100 \mathrm{K}$. To eliminate the offset voltage, a resistor $R5=91 \mathrm{K}$ the value of R5 is the parallel resistance of R3 and R4 is connected between the positive input terminal of the amplifier and the ground terminal. According to $\mathsf{\omega }=1 / 2\mathsf{\pi }RC$, for the low-frequency cutoff frequency of 0.05 Hz and $R3=100 \mathrm{K}$, calculate $C1\approx 3.2 \mathrm{u}\mathrm{F}$. Select $Cl\approx 4.7 \mathrm{u}\mathrm{F}$, then the low-frequency cutoff frequency is approximately $\mathsf{\omega }=0.03 \mathrm{H}\mathrm{z}$. According to $\mathsf{\omega }=1 / 2\mathsf{\pi }RC$, for the low-pass filter cutoff frequency of 20 Hz and $R4=1 \mathrm{M}$, calculate $C2\approx 7962 \mathrm{p}\mathrm{F}$. Select $C1\approx 6800 \mathrm{p}\mathrm{F}$, then the low-pass filter cutoff frequency is approximately $\mathsf{\omega }=23 \mathrm{H}\mathrm{z}$. The filter circuit consists of a third-order Butterworth low-pass filter with a set cutoff frequency of $f=10 \mathrm{H}\mathrm{z}$. According to the requirements of the normalization method, select $R6=R7=R8=100 \mathrm{K}$, $C3=0.56 \mathrm{u}\mathrm{F}$, $C4=0.22 \mathrm{u}\mathrm{F}$, and $C4=0.033 \mathrm{u}\mathrm{F}$. This circuit can retain the useful heart rate signal and further attenuate the 50 Hz interference signal. The backstage amplification circuit adopts a variable-gain inverting amplification circuit, which can not only increase the input impedance value but also ensure sufficient gain. If R10 is much larger than R11 and R12, the gain of the amplifier can be approximately calculated by formula 4–1. $A=-{\displaystyle \frac{R10}{R9}}\left(1+{\displaystyle \frac{R11}{R12}}\right)$. Select $R9=100 \mathrm{K}$, $R10=1 \mathrm{M}$, and a potentiometer with a range of 0–10 K for R11; $R12=1 \mathrm{K}$, $R13=91 \mathrm{K}$. R10 and R12 form a low-pass filter with a cutoff frequency of 20 Hz. The gain range is calculated to be 11–110.

After the heart rate signal passes through the filter amplification circuit, it is converted into a TTL level signal through a digital-to-analog converter and can be recognized, input, and processed by the single-chip microcomputer. The digital-to-analog conversion chip adopts the DAC0832 (Zhongshan Tianyixing Electronic Technology Co., Ltd, Zhongshan City, Guangdong Province, China) with 8-bit resolution, which is low-cost, simple in interface, and easy to convert.

4.1.2. Collection of Body Temperature

Body temperature, usually referring to the blood temperature in the deep part of the body, represents the average temperature of the internal organs of the body and is a necessary condition for the body’s metabolism and normal life activities. Generally, the body temperature of a person at rest is relatively constant, with the axillary temperature ranging from 36 to 37 degrees Celsius, the oral temperature being 0.2–0.4 degrees Celsius higher than the axillary temperature, and the rectal temperature being 0.3–0.5 degrees Celsius higher than the oral temperature.

It is generally believed that during exercise, due to the increase in metabolic level, the body’s heat production increases, and the heat generated cannot be eliminated from the body in time, resulting in an increase in body temperature. After a middle-distance race, the body temperature can rise to 37.5–38 °C; after a long-distance race, it can rise to 38.5 °C; and after an ultra-long-distance race, it can rise to 39.75 °C. These are all due to the enhanced muscle activity, metabolism, and significantly increased heat production, leading to an increase in body temperature.

Excessive body temperature has certain effects on skeletal muscles, the cardiovascular system, certain immune indicators, and the central nervous system. High temperature can limit the blood flow of working muscles, limit exercise capacity, damage body functions, and increase the risk of heatstroke; high temperature can reduce the long-term working ability of the body and cause great damage to the cardiovascular system; high temperature restricts many immune parameters, and the functions of neutrophils, the synthesis of immunoglobulins in plasma and saliva, and the number of NK cells decrease; high temperature reduces the excitability of the central nervous system and damages muscle movement ability.

Therefore, it is necessary to control the body temperature of the exerciser during exercise to prevent the adverse effects of excessive body temperature. A DS18B20 (Maxim Integrated, Dallas, TX, USA) smart temperature sensor from was used to collect the body temperature. This temperature sensor can well solve problems such as lead error compensation, multi-point measurement switching error, and zero drift of the amplification circuit, and has high measurement accuracy. The DS18B20 intelligent temperature sensor has a 3-pin TO—92 small-volume packaging form, a temperature measurement range of −55 °C~+125 °C, programmable 9-bit to 12-bit A/D conversion accuracy, and a temperature measurement resolution of up to 0.0625 °C. The working power supply can be introduced remotely or generated by a parasitic power supply method. The address line, data line, and control line are combined into a bidirectional serial transmission data signal line, making the structure simpler, more reliable, and improving the anti-interference ability. Due to the above characteristics, the DS18B20 intelligent temperature sensor is widely used in environmental control and temperature measurement consumer electronic products.

The DS18B20 intelligent temperature sensor is a digital temperature sensor that uses the on-board patented technology and has the following basic characteristics: wide adaptation voltage range and can be powered by the data line in the parasitic power supply mode; unique single-wire interface method and can realize two-way communication with the microprocessor with only one line; measurement range of −55 °C~+125 °C and an accuracy of ±0.5 °C at −10~85 °C; converts the temperature value into 9-bit and 12-bit digital quantities in 93.75 ms and 750 ms, respectively; can be connected with multiple DS18B20 on the bidirectional serial transmission data signal line to realize multi-point measurement; the chip itself has a command set memory; the measurement result directly outputs a digital temperature signal; negative pressure characteristic, when the power supply polarity is reversed, the thermometer will not be burned due to heating, but it cannot work normally. Among them, DQ is the digital signal input/output terminal; GND is the power ground; and VDD is the external power supply input terminal.

A high-temperature coefficient oscillator is used to determine a gate period, and an internal counter counts the pulses of a low-temperature coefficient oscillator within this gate period to obtain the temperature value. The oscillation frequency of the low-temperature coefficient crystal oscillator is hardly affected by temperature and is used to generate a fixed-frequency pulse signal for the subtraction counter 1. The oscillation frequency of the high-temperature coefficient crystal oscillator changes significantly with temperature, and the generated pulse signal is used as the pulse input of the subtraction counter 2. The subtraction counter 1 and the temperature register are preset with a base value corresponding to −55 °C. The opening time of the counting gate is determined by the high-temperature coefficient crystal oscillator. When the measurement starts, the subtraction counter 1 subtracts the pulses of the low-temperature coefficient crystal oscillator. When the preset value of the subtraction counter 1 is reduced to 0, the value of the temperature register is incremented by 1, and the preset value of the subtraction counter 1 is reloaded. The counter 1 restarts counting the pulses of the low-temperature coefficient crystal oscillator. This cycle continues until the counter 2 counts to 0, and the accumulation of the temperature register value stops. At this time, the value in the temperature register is the measured temperature.

The single-wire communication function of the DS18B20 is completed in time-sharing, and the strict communication protocol ensures the correctness and integrity of data transmission. The one-wire working protocol flow of the DS18B20 is as follows: initialize the DS18B20; send a reset pulse; send a ROM operation instruction; send a memory operation instruction; send a data transmission instruction; data processing.

4.1.3. Collection of Blood Pressure

Blood pressure (BP), that is, systemic arterial blood pressure, refers to the pressure exerted on the blood vessel wall when blood flows in the blood vessel. When the ventricle contracts, blood flows from the ventricle into the artery, and at this time, the blood pressure in the artery is the highest, which is called systolic blood pressure SBP. When the ventricle relaxes, the elastic recoil of the arterial vessel occurs, and the blood continues to flow forward slowly, but the blood pressure drops, and the pressure at this time is called diastolic blood pressure DBP.

Blood pressure varies with age, gender, weight, metabolic rate, emotion, and many other factors. Generally speaking, regardless of gender, blood pressure gradually increases with age, and the increase in systolic blood pressure is more significant than that in diastolic blood pressure; before menopause, the blood pressure of women is slightly lower than that of men of the same age, and it increases after menopause; the blood pressure of obese people is slightly higher than that of non-obese people of the same age. The average normal blood pressure reference values of Chinese people released by the national authority are shown in

Table 2. Average normal blood pressure reference value of Chinese (mmHg).

Age	SBP (Male)	Diastolic BP (Male)	SBP (Female)	Diastolic BP (Female)
16–20	115	73	110	70
21–25	115	73	110	71
26–30	115	75	112	73
31–35	117	76	114	74
36–40	120	80	116	77
41–45	124	81	122	78
46–50	128	82	128	79
51–55	134	84	134	80
56–60	137	84	139	82
61–65	148	86	145	83

.

During exercise, arterial blood pressure will increase, which is closely related to the exercise mode, intensity, and duration; hypotensive reaction may occur after exercise; exercise is of great significance for the early diagnosis and treatment of hypertension. For normal people, exercise has little impact on blood pressure, and the fluctuation is only about 10 mmHg. Even for very intense exercise such as football, basketball, or sprinting, blood pressure will not suddenly increase to “hypertension” or suddenly decrease to “hypotension”, and it will always fluctuate within the normal range. However, for unhealthy people or patients, exercise has a great impact on blood pressure, and incorrect exercise or intense exercise may harm human health, and some may even form “hypotensive shock” or “post-exercise hypertension” and endanger life.

Therefore, monitoring the blood pressure changes in the exerciser during exercise can help diagnose and prevent hypertension in patients at an early stage and prevent them from being harmed by exercise. This module can measure systolic blood pressure, diastolic blood pressure, and mean pressure, and can communicate with the single-chip microcomputer through the TTL-level universal asynchronous receiver/transmitter UART.

The characteristics of the BTN602 (Beijing Maichuang Tongyuan Electronic Instrument Co., Ltd., Beijing, China) non-invasive blood pressure measurement module are as follows: small size, flexible and simple installation method, with PCI04 standard [27] size and positioning holes; excellent clinical monitoring accuracy and reliability; the module realizes all measurement items of automated non-invasive blood pressure measurement NIBP; conforms to the functional safety design requirements of CE Conformity Europeenne; suitable for adults, children, and newborns; completely compatible with the communication protocol of the CAS non-invasive blood pressure module.

The measurement range of the BTN602 non-invasive blood pressure measurement module: for adults, systolic blood pressure is 30–255 mmHg; diastolic blood pressure is 15–220 mmHg; mean pressure is 20–235 mmHg; for newborns, systolic blood pressure is 30–135 mmHg; diastolic blood pressure is 15–110 mmHg; mean pressure is 20–125 mmHg.

The functions of the BTN602 non-invasive blood pressure measurement module are as follows: automatic measurement mode: 90, 60, 30, 15, 10, 5, 4, 3, 2, 1 min; the manual measurement mode can manually start a measurement at any time according to the user’s needs; the calibration mode continuously provides the cuff pressure value; self-check provides a self-check function after reset.

When measuring blood pressure, the single-chip microcomputer sends a measurement instruction, and the module extracts gas from the atmosphere and transports it into the cuff. The pressure in the cuff rapidly rises to about 170 mmHg, and then the cuff slowly deflates, and the pressure in the cuff decreases in steps of about 20 mmHg each until the sensor in the cuff collects the systolic and diastolic blood pressures of the human body, and then the air release valve is opened to reduce the pressure in the cuff to 0.

4.2. Data Cleaning and Preprocessing

This stage is the first step of data analysis, mainly to solve the possible abnormal fault sensor, improper user operation, and other problems, to ensure the accuracy and reliability of the collected data. At the same time, the data are smoothed, and the stability and analytic ability of the data are guaranteed by the noise reduction technology and vibration interference processing, as shown in Figure 5. In order to ensure the stability and analyzability of the data, noise reduction technology and vibration interference processing methods can be adopted as follows:

(1) Noise reduction technology: In fitness monitoring, the signal signature changes when the user switches from one sport mode to another, such as jogging to brisk running. The adaptive filtering algorithm can perceive these changes in real time, automatically adjust the filtering parameters to adapt to the noise characteristics in different motion states, continuously and effectively remove noise, and ensure the stability and accuracy of the data.

(2) Vibration interference handling: Auxiliary sensors such as accelerometers are used to obtain vibration information about the equipment. When vibration is detected, the heart rate and energy consumption data are compensated and corrected according to the amplitude, frequency, and other parameters of the vibration. For example, when the accelerometer detects severe vibration, the software algorithm can determine that the heart rate and energy consumption data may be disturbed at this time, and adjust the data through the established mathematical model to remove the impact of vibration interference, so that the data can better reflect the real physiological state.

The processed data are applied to models such as exercise state analysis and health risk prediction. If the use of the processed data can significantly improve the prediction accuracy of the model, it indicates that the noise reduction and vibration interference processing methods can help to improve the quality of the data, and thus improve the performance of the model.

4.3. Data Standardization

Data are standardized to ensure that different types and sources of data can be analyzed in a uniform format and scope. Standardized processing is able to eliminate these differences, making the data comparable and analytic.

Figure 5 shows the heart rate and energy expenditure data after standardized processing. Data standardization is a key step in data processing, which aims to convert data of different units and magnitudes to the same standard scale, thus eliminating the differences between data and making it comparable and analytic. Specifically, normalization subtracts the mean from each data point and divides it by the standard deviation, so that the mean of the data distribution is 0 and the standard deviation is 1. In Figure 6, the normalized processed heart rate and energy consumption data are presented as a scatter plot, with the horizontal axis as the sample index and the vertical axis as the normalized value. As can be seen from the figure, the normalized heart rate and energy expenditure data values are distributed in a relatively concentrated interval (roughly between −3 and 3), eliminating the difference in size and range of the original data source.

4.4. Data Integration

(1)

Received Signal of Each Sensor

The received signal of each sensor is shown in

Table 3. Heart rate data for one hour of exercise at 20, 40, and 60 years of age.

Time (min)	20-Years-Old (bpm)	40-Years-Old (bpm)	60-Years-Old (bpm)
0	75	80	85
10	110	115	118
20	125	130	132
30	135	140	140
40	140	143	145
50	138	141	143
60	130	135	138

,

Table 4. Temperature data at 20, 40, and 60 years of age for one hour of exercise.

Time (min)	20-Years-Old (°C)	40-Years-Old (°C)	60-Years-Old (°C)
0	36.8	36.9	37.0
10	37.2	37.3	37.4
20	37.5	37.7	37.8
30	38.0	38.1	38.2
40	38.2	38.3	38.4
50	38.3	38.4	38.5
60	38.1	38.2	38.3

,

Table 5. Blood pressure data at 20, 40, and 60 years of age with one hour of exercise.

Time (min)	20-Years-Old (mmHg)	40-Years-Old (mmHg)	60-Years-Old (mmHg)
0	118/76	125/80	130/85
10	132/78	138/82	142/86
20	138/80	144/84	148/88
30	142/82	150/87	155/90
40	144/84	152/89	160/92
50	142/83	150/88	158/91
60	138/80	147/86	155/89

,

Table 6. Heart rate data one hour after exercise at 20, 40, and 60 years of age.

Stopping Time of Exercise (min)	20-Years-Old (bpm)	40-Years-Old (bpm)	60-Years-Old (bpm)
10	100	110	120
20	90	98	110
30	85	90	100
40	80	85	92
50	78	83	88
60	76	81	86

,

Table 7. Temperature data one hour after exercise at 20, 40, and 60 years of age.

Stopping Time of Exercise (min)	20-Years-Old (°C)	40-Years-Old (°C)	60-Years-Old (°C)
10	37.9	38	38.2
20	37.6	37.8	38
30	37.4	37.6	37.7
40	37.2	37.4	37.6
50	37	37.3	37.4
60	36.9	37.1	37.3

and

Table 8. Blood pressure data one hour after exercise at 20, 40, and 60 years of age.

Stopping Time of Exercise (min)	20-Years-Old (mmHg)	40-Years-Old (mmHg)	60-Years-Old (mmHg)
10	130/78	140/83	148/87
20	125/76	135/80	144/85
30	122/75	132/78	140/83
40	120/74	130/77	138/82
50	119/74	128/76	136/81
60	118/74	126/76	134/80

. The received signals from each sensor are shown in Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8. Table 3, Table 4 and Table 5 show the received heart rate data, temperature data, and blood pressure data, for example, at the ages of 20, 40, and 60, and the data of one hour of exercise are recorded at ten-minute intervals. Table 6, Table 7 and Table 8 show the received heart rate data, temperature data, and blood pressure data at ten-minute intervals for 20-, 40-, and 60-year-olds, for example, one hour after exercise.

The results showed that heart rate, body temperature, and blood pressure continued to rise and could be recorded as the exercise continued, regardless of whether the person was in their 20s, 40s, or 60s. Similarly, with the cessation of exercise, heart rate, body temperature, and blood pressure decreased and were recorded in 20-, 40-, and 60-year-olds. This proves the validity of our idea.

(2)

Data Integration

The data integration stage involves the integration of data from different sources to form a complete user health dataset. Through integration, users’ exercise habits, fitness effect trends, and other information can be comprehensively analyzed so as to provide users with more comprehensive and accurate health guidance and monitoring services, as shown in

Table 9. Data integration.

Age	Stature (cm)	Weight (kg)	Heart Rate (bpm)	To Consume Energy (kcal)	Intensity	Respiratory Rate (bpm)	Cycling Speed (km/h)	Movement Duration (min)	Rowing Frequency (spm)
22	152	66	121	321.8161835	6.289256493	18	20	30	25
26	190	80	113	365.8697033	6.425256968	16	19	35	28
27	187	84	98	407.1135179	4.415480476	14	21	40	30
28	160	66	118	353.9340417	7.142822604	17	22	38	27
30	178	99	129	302.9815185	3.365198206	20	18	45	29
33	156	54	124	275.8057083	5.983084242	19	19	32	26
36	194	91	147	216.5707963	5.631450534	22	23	50	32
39	156	63	123	403.7630436	3.101151347	18	20	42	28
40	150	81	135	272.4847423	4.58703803	21	22	44	31
41	172	80	97	291.368635	6.933520925	14	21	31	29
43	150	82	112	352.4776358	7.089027972	17	19	36	27
45	176	97	117	346.2316841	7.21012146	18	18	38	28
47	184	90	127	369.9677718	2.15598695	20	21	39	30
49	165	96	116	319.6742693	6.402372652	19	19	30	29
51	193	97	111	309.8955392	4.867224765	16	20	43	28
53	153	86	119	303.8410946	2.376634499	17	21	34	30
57	189	97	104	332.359797	3.992420832	14	22	37	29
58	176	52	143	344.759661	1.948140562	21	24	46	31

.

(3)

The Relationship of Heart Rate and Age

The chart shows the trend of the users’ heart rate (bpm) with age. From Figure 7, the heart rate does not show a clear linear relationship with age. Some ages (e.g., 36) were significantly higher than others, while in younger users (e.g., 22 and 28) they were in the moderate range. Variations in heart rate may be related to an individual’s physical performance, exercise habits, and weight, but overall, age is not the only factor affecting heart rate. It can be speculated that the personalized heart rate monitoring and feedback system should be fully considered in the design process of fitness equipment to help users to better adjust the exercise intensity.

(4)

The Relationship between Energy Consumed and Body Weight

From Figure 8, “Energy consumed (kcal) and body weight (kg)”, the energy consumed is somewhat correlated with the user’s body weight. Generally, users with a larger weight also consume more energy during exercise. For example, users with higher weight (e.g., 99 kg) consume more energy, but it is not a completely linear relationship. For example, users weighing 84 kg consumed significantly more energy than other users of similar weights. This suggests that in addition to body weight, exercise type, intensity, and duration also have significant effects on energy expenditure. Home fitness equipment can provide customized fitness recommendations based on the user’s weight and type of exercise, ensuring accurate monitoring of energy expenditure.

(5)

The Gentle Link Between Age and Breathing Rate

From Figure 9, “breathing rate and age”, the change in breathing rate with age is relatively gentle. The overall trend showed that younger users had slightly lower respiratory rates and slightly increased with age. Users aged 36 years and older have a relatively high respiratory frequency, which may be related to metabolic changes in the body, endurance requirements during exercise, and physical exertion. Through real-time monitoring of respiratory frequency, fitness equipment can ensure that the exercise intensity of users is within the safe range, especially in older users, and more attention needs to be paid to the monitoring and feedback of respiratory frequency.

(6)

The Link Between Age and Cycling Speed

Figure 10, “cycling speed and age”, indicates that there is some difference between the user’s cycling speed and age. Younger users ride relatively fast, especially in the 27 to 36 age group, reaching 21 to 23 km/h. With the growth in age, cycling speed decreased, especially among users over 40 years old, and cycling speed was relatively stable below 20 km/h. This trend suggests that older users may be more inclined to moderate-intensity aerobic exercise, while younger users are more resilient to higher-intensity cycling. Fitness equipment can adjust the speed according to the age and cycling speed to help users balance the exercise intensity and comfort.

4.5. Guidance on Equipment Data Monitoring Method

In order to better guide different groups to adapt their health conditions to exercise, we use fitness equipment with monitoring functions, as shown in

Table 10. Multifunctional fitness equipment data.

Age	Body Building Apparatus	Movement Duration (min)	Heart Rate (bpm)	Energy Consumption (kcal)	Strength (1–10)
22	Exercise bike	30	120	300	6
26	Rowing machine	25	115	280	5
27	Spring tensioner	20	110	260	4
28	Exercise bike	35	125	350	7
30	Rowing machine	30	120	330	6
33	Spring tensioner	25	115	310	5
36	Exercise bike	40	130	400	8
39	Rowing machine	35	125	380	7
40	Spring tensioner	30	120	360	6
41	Exercise bike	45	135	450	9
43	Rowing machine	40	130	430	8
45	Spring tensioner	35	125	410	7
47	Exercise bike	50	140	500	10
49	Rowing machine	45	135	480	9
51	Spring tensioner	40	130	460	8
53	Exercise bike	55	145	550	10
57	Rowing machine	50	140	530	9
58	Spring tensioner	45	135	510	8

, to guide people of different age groups to choose the appropriate exercise methods, and at the same time, provide feedback on the physical health status of different people.

From Figure 11, “Heart rate and age”, the heart rate changes on different fitness devices showed different trends. Overall, the heart rate gradually increased with increasing age, but the effect of different devices on the heart rate varied. Heart rates using the “spring tensioner” were lower, while “exercise bikes” and “rowing machines” had significantly higher heart rates during high-intensity training. This indicates that users should consider their own heart rate status when choosing fitness equipment, and choose the appropriate equipment and intensity. Health advice: for users with heart problems or poor endurance, it is recommended to choose low-intensity equipment with low heart rate fluctuation, such as spring tensioners; users with healthy hearts that want to improve cardiopulmonary function can use an exercise bike or rowing machine for high-intensity aerobic exercise.

From Figure 12, “Energy Consumption”, the energy consumption of exercise bikes and rowing machines is high, especially in the age group of high-intensity exercise (30 to 50), The energy consumption is nearly 450–500 kcal, which indicates that these two devices are suitable for high-intensity training. However, the energy consumption of the spring tensioner is relatively low, indicating that the exercise intensity is relatively mild, and it is suitable for medium- and low-intensity training. Users who reduce fat or increase energy consumption through fitness are suitable to choose large energy consumption equipment such as exercise bikes or rowing machines; those who want to have strength training or endurance improvement can choose the spring tensioner for gentle training.

5. Conclusions

This paper explores the multifunctional integration and health monitoring system of home fitness equipment. Based on the integrated design concept, it combines modular design, a space-optimized layout, and smart interactive technology to propose an innovative fitness equipment design. The equipment integrates an intelligent exercise bike, a rowing machine, and tensioner functions, enabling seamless switching between diverse functions. Built-in smart sensors can instantly capture key user data like heart rate (97–147 bpm), respiratory rate, and energy consumption (216–508 kcal). Advanced data-processing algorithms construct personalized fitness-effectiveness evaluation models, offering users precise and scientific fitness guidance. Moreover, smart human–computer interaction technology helps the equipment promptly respond to user needs, providing an intuitive interface and personalized training advice, thereby greatly enhancing the user experience. The integrated design effectively addresses the issues of traditional home fitness equipment, such as large size, single function, and lack of personalization. It also improves exercise efficiency and safety through real-time health monitoring and personalized guidance, providing users with a superior fitness experience.

For future research and development, several concrete steps should be taken to further optimize the smart monitoring and interaction design of fitness equipment. Firstly, stress-testing the device in domestic environments is essential. This will help identify any potential issues related to long-term use, durability, and user-friendliness in real-world scenarios. Secondly, developing adaptive control systems for user feedback is crucial. By integrating advanced AI algorithms, the equipment can dynamically adjust exercise intensity and provide real-time suggestions based on users’ physiological data and fitness goals.

Additionally, user interface design and integration with mobile phone platforms should be prioritized to enhance user-friendliness. A well-designed user interface (UI) can significantly improve the interaction between users and the equipment. This includes intuitive controls, real-time data visualization, and personalized feedback displays. Integrating the equipment with mobile phone platforms through IoT technology will allow users to conveniently monitor their progress, set goals, and receive notifications on their smartphones. This seamless integration can also enable social sharing features, gamification elements, and access to online fitness communities, further motivating users and enhancing their overall experience.

Leveraging IoT technology to connect the equipment with other smart devices in the home environment, such as smartwatches and health tracking apps, can enable more comprehensive health management. Continuous user-centered design improvements, including ergonomic enhancements and user interface refinements, will ensure that the equipment remains accessible and appealing to a wide range of users.