Abstract
s paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance. Keywords:athlete well-being; vital sign monitoring system; IoMT; physiological parameters; smart glove; training optimization 1. Introduction The real-time monitoring of human physiological functions has gained considerable momentum, particularly in the fields of sports and healthcare. This increasing focus highlights the valuable insights that can be obtained
outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance. Keywords:athlete well-being; vital sign monitoring system; IoMT; physiological parameters; smart glove; training optimization 1. Introduction The real-time monitoring of human physiological functions has gained considerable momentum, particularly in the fields of sports and healthcare. This increasing focus highlights the valuable insights that can be obtained from analyzing the body’s responses during various activities, thereby enabling more effective optimization of training programs and healthcare interventions [1]. The ability to monitor physiological parameters provides a crucial opportunity to enhance athletic performance and overall well-being, making it an essential element in both sports science and health monitoring systems [2–4]. This advancement has been driven by the rapid progress of the Internet of Things (IoT), supported by innovations in sensor technology, communication systems, and personal computing devices. These developments have paved the way for new possibilities in wearable healthcare devices, making sensors more accessible and cost-effective while enabling the real-time monitoring of critical physiological parameters such as functional movements, biomechanical factors, and vital signs [5,6]. For athletes, such data are essential for optimizing performance and reducing injury risk by allowing training plans to be customized to individual needs. Wearable monitoring systems facilitate continuous data collection, enabling the design of precise, personalized training and treatment strategies aimed at injury prevention [7]. Heart rate (HR) is a fundamental physiological metric, extensively utilized to evaluate cardiovascular responses both during physical exertion Sensors2024,24, 6500.
Sensors2024,24, 6500 2 of 26 and throughout recovery phases. Since HRmax is a crucial measure for prescribing exercise intensity and has a strong correlation with maximum oxygen uptake [7], establishing a threshold based on the “220-age” formula [8] is essential for ensuring safe running practices. This threshold helps in providing timely alerts to athletes if their heart rate exceeds the recommended limits, thereby preventing potential health risks. Illustrated in Figure are the applications of IoT in sports, showing the integration of smart sports devices and wearables equipped with embedded sensors. Figure 1.Sports devices and wearables with integrated sensors. In our study, the reasons for the use of wearable gloves in athletes, especially runners, are briefly as follows: Sensor placement: The proposed wearable gloves can offer more precise or stable position- ing for certain sensors (e.g., gyroscope) that require better alignment with hand movements for accuracy. For example, while the gyroscope placed on the glove measures speed and acceleration, the sensor placed on the fingertip of the glove measures pulse and SpO2. Targeted application: The glove can provide better integration of multiple sensors in a single wearable device that captures runner-specific hand movements, which may not be as effective in current wristband-like products on the market. In our study, it is possible to place sensors in appropriate locations on the glove to collect data with high accuracy. Enhanced measurement: The glove can also allow for more accurate or consistent measure- ments of hand movement, temperature, or other parameters that are specifically relevant to runners. In addition to the potential advantages mentioned above, we propose an alternative wearable system for athlete tracking in our study. Moreover, when compared to similar products on the market, the proposed wearable glove approach stands out with its low cost, easy accessibility, and scalability features. The primary objective of this paper is to introduce a wearable glove system equipped with an alarm feature that monitors runners’ speed, heart rate, SpO2, body temperature, and ambient temperature across three distinct phases: resting, walking, and running. The system provides insights into the data collected and alert state based on the
its low cost, easy accessibility, and scalability features. The primary objective of this paper is to introduce a wearable glove system equipped with an alarm feature that monitors runners’ speed, heart rate, SpO2, body temperature, and ambient temperature across three distinct phases: resting, walking, and running. The system provides insights into the data collected and alert state based on the monitored parameters during these different phases. The structure of the paper is as follows: Section literature, Section
Sensors2024,24, 6500 3 of 26 tests, results, challenges encountered, and future research directions. Finally, Section concludes the paper with a summary of the findings. 2. Literature Review Monitoring athletes’ vital signs during running is essential for optimizing performance and ensuring well-being, providing timely guidance when potential issues arise. Various methods have been developed to track physiological parameters under diverse and chal- lenging conditions. One prevalent approach is the use of wearable smartwatches, which monitor heart rate, SpO2, and hand movement [9–11]. Additionally, more specialized systems have been designed, including backpacks equipped with satellite antennas and microcontrollers connected to heart rate sensors worn on the athlete’s finger [12], and similar without satellite communication features, to monitor a variety of physiological parameters through integrated wearable sensors, including heart rate, oxygen saturation, body temperature, blood pressure, and other relevant metrics [13]. Other innovations include smart gloves designed for monitoring fitness exercises [14,15] and combat sports, such as boxing, to aid in mastering defense and punching techniques [16]. Furthermore, body sensor network systems have been developed for mountaineers to enhance safety and performance [17]. Zhao et al. pioneered a wearable sports posture measurement system integrated with Internet of Things (IoT) technology [6]. Through experiments on common sports postures, their system provides critical insights into performance. However, they emphasize the need for caution when assessing heart rate variability (HRV) using non-ECG devices, recommending ECG signals and a minimum respiratory rate of 10 breaths per minute for more accurate HRV analysis in athletes. Similarly Neupert et al. and Karlsson et al. have conducted parallel studies examining athlete monitoring techniques in elite sports, focusing on the United Kingdom [18], as well as acclimatization and training responses of elite cross-country skiers and biathletes at an altitude of 1800 m over a 17–21-day period [19]. Thornton et al. focused their research on the analysis and visualization of athlete data to optimize performance and training strategies [20]. Pawlik et al. conducted practical research on fatigue and training load factors in volleyball, aiming to evaluate both internal and external loads experienced by players during a competitive season [21]. Utilizing accelerometers and
m over a 17–21-day period [19]. Thornton et al. focused their research on the analysis and visualization of athlete data to optimize performance and training strategies [20]. Pawlik et al. conducted practical research on fatigue and training load factors in volleyball, aiming to evaluate both internal and external loads experienced by players during a competitive season [21]. Utilizing accelerometers and subjective assessments like the perceived exertion (RPE) and total quality recovery scale (TQR) questionnaires, they monitored eleven female athletes across five training days. Their results showed that tracking acceleration counts could provide valuable insights into the specific demands placed on volleyball players, aiding in the optimization of training and recovery. Similarly, Rebelo et al. explored this topic with male volleyball athletes, reinforcing the importance of load monitoring in sports [22]. Additionally, innovations such as the system developed by Oktavius et al., which monitors cyclists’ body fatigue by analyzing cortisol levels through a sweat sensor, enable real-time assessments of stress and endurance during cycling performance [23]. Elshafei and Shihab took a different approach by focusing on muscle fatigue monitoring using sensors during repetitive muscle contractions, which is particularly useful for gym and bodybuilding activities [24]. In terms of heart rate monitoring, Huifeng et al. introduced a heart rate sensor designed to track electrical activity in the heart, offering valuable cardiovascular data across various sports disciplines [2]. Zhao and Li expanded this field with the development of a body sensor network (BSN) aimed at monitoring body motion and physiological parameters, which enhances both performance and safety across multiple sports [25]. The use of electromyography (EMG) sensors to track muscle activity has also gained prominence, with Taborri et al. demonstrating how EMG can improve training regimens and prevent injuries by analyzing muscle dynamics during body movements [26]. Mean- while, Scataglini et al. developed a temperature monitor to assess skin convective heat
Sensors2024,24, 6500 4 of 26 flux, particularly beneficial for runners in managing heat exposure and optimizing cooling strategies during races [27]. Finally, Gao et al. introduced a cutting-edge wearable sweat analysis technology that monitors not only psychological and physical conditions but also metabolism and body adaptability. This technology has broad applications across different sports and provides comprehensive data to better manage athletic performance [28]. In addition to sports applications, notable advancements have been made in the healthcare sector to address hand-arm vibration syndrome. Aizuddin and Jalil provided a thorough review of this field, highlighting the critical enabling technologies such as sensor and wireless communication technologies. They also proposed a conceptual model for a smart wearable system designed to measure hand-transmitted vibrations, underscoring the potential of such systems in improving healthcare outcomes [29]. In our comprehensive literature survey, it is noteworthy that none of the methods investigated thus far incorporate a built-in alert system. The absence of such a critical feature poses a significant limitation, particularly in the context of athlete monitoring. Real-time alerts play a pivotal role in promptly notifying athletes when potential issues or anomalies arise during training or performance. The integration of a robust alert system is imperative for enhancing the overall effectiveness of athlete monitoring systems, ensuring timely interventions, and ultimately contributing to the optimization of athletic performance and well-being. In light of this gap in existing methodologies, our research endeavors to address this critical need by incorporating an advanced built-in alert system within our wearable technology. 3. Proposed System This section outlines the components of the proposed system, which integrates ad- vanced sensor technologies to provide comprehensive vital sign monitoring for professional athletes. The system features the MPU6050 for motion tracking and the MAX30100 for real-time heart rate and SpO2 measurements, along with other sensors described below. These sensors are seamlessly integrated into a unified framework, enabling efficient data acquisition and transmission. Additionally, an algorithm has been implemented to analyze the collected data and generate alerts when predefined thresholds for physiological parameters are exceeded. This ensures that any critical changes in the athlete’s health are promptly
heart rate and SpO2 measurements, along with other sensors described below. These sensors are seamlessly integrated into a unified framework, enabling efficient data acquisition and transmission. Additionally, an algorithm has been implemented to analyze the collected data and generate alerts when predefined thresholds for physiological parameters are exceeded. This ensures that any critical changes in the athlete’s health are promptly addressed. Figure hardware and software components. The hardware elements include the MAX30100 for the real-time measurement of the pulse, the MPU6050 for the tracking of motion, the vibration motor for the provision of tactile feedback, the MLX9064 for ambient and body temperature measurement, and the ESP8266 for the transmission of data via wireless communication. These hardware components function in conjunction with one another to enable the acqui- sition, processing, and transmission of data. In addition to the aforementioned hardware components, software elements such as the Firebase database serve to provide the necessary backend infrastructure for the storage and management of the collected data. This compre- hensive representation offers a detailed overview of the system’s composition, highlighting the integration of diverse elements to facilitate vital sign monitoring for professional ath- letes. supply and real-time data transfer through the UART protocol for immediate screening and testing. 3.1. Hardware Setup The front and back views of the proposed smart glove system are shown in Figure. The hardware configuration is meticulously designed to capture comprehensive data essen- tial for monitoring an athlete’s physiological parameters during exercise. This integrated hardware setup ensures a robust foundation for our monitoring system, combining preci-
Sensors2024,24, 6500 5 of 26 sion in data acquisition with real-time communication mechanisms for a comprehensive understanding of an athlete’s health and performance. Figure 2.The proposed IoMT-empowered athlete health monitoring and alert system.Figure 3.The front (a) and back (b) views of the prototype IoMT-based athlete health monitoring and alert system. As seen in Figure, the key components of the hardware setup of the proposed smart glove system consist of ESP8266 as a microcontroller, MAX30100, MPU6050, MLX9064 sensors connected to this microcontroller, and a vibration motor for alert. In this work, the ESP8266 microcontroller is a versatile and powerful component programmed with the Arduino IDE. This microcontroller plays a pivotal role in collecting data from various
Sensors2024,24, 6500 6 of 26 sensors, including the MPU6050 and pulse sensor. It facilitates the seamless integration of different data streams, ensuring efficient communication between the sensors and the central processing unit. The ESP8266 is also responsible for retrieving real-time heart rate, pulse data, and accurate datetime from an Network Time Protocol (NTP) server. The integration of the MAX30100 into the system adds a crucial dimension to the hardware setup. This component continuously provides real-time heart rate and SpO2 data, a vital metric for evaluating an athlete’s physiological response during exercise. Its seamless incorporation ensures accurate and timely monitoring of the cardiovascular aspect of the athlete’s performance. The MPU6050 sensor is employed to monitor the athlete’s movement by capturing accelerometer and gyroscope data on the x, y, and z axes. This sensor offers invaluable insights into the dynamic aspects of physical activities, enabling a comprehensive analysis of the athlete’s motion and posture during different exercises. The MLX9064 sensor is utilized for measuring body and ambient temperatures, providing critical environmental data for assessing the athlete’s thermal status during activity. The vibration motor integrated into the system functions as an alert mechanism that increases the interaction of the hardware. It provides immediate feedback to the athlete by vibrating when anomalies such as a high heart rate or velocity exceeding the threshold are detected. 3.2. Data Collection and Transmission In this work, the ESP8266 microcontroller plays a pivotal role in the seamless collection and transmission of data. Upon gathering relevant information, the ESP8266 meticulously processes the collected data, employing an efficient algorithm tailored to the specific requirements of the application. This algorithm ensures precision and accuracy in the treatment of data, laying the foundation for robust and reliable subsequent analyses. The transmission of processed data is facilitated through a secure connection to the Firebase Real-time Database (RTDB), a cloud-based platform known for its scalability and real-time synchronization capabilities. This integration allows for the swift and efficient transfer of data to the cloud, creating a centralized repository for storage and retrieval. The Firebase RTDB serves as a dynamic and responsive backend, accommodating the influx
processed data is facilitated through a secure connection to the Firebase Real-time Database (RTDB), a cloud-based platform known for its scalability and real-time synchronization capabilities. This integration allows for the swift and efficient transfer of data to the cloud, creating a centralized repository for storage and retrieval. The Firebase RTDB serves as a dynamic and responsive backend, accommodating the influx of data in real-time, which is crucial for applications demanding instantaneous updates and monitoring. To ensure the temporal precision of our data collection, our system integrates an NTP server. Given that ESP8266 lacks a real-time clock module, we rely on retrieving date and time information from an online NTP server when the device is connected to Wi-Fi. This integration plays a pivotal role in furnishing accurate timestamping and synchronization, ensuring coherence among data points. For the device to interact with the Firebase RTDB, several specific steps are necessary. On the device side, we first ensure the latest library version is installed on the Arduino, enabling communication with the Firebase endpoint for data transmission and reception. To facilitate dynamic Wi-Fi connection, we utilize the Wi-Fi manager library, allowing us to input Wi-Fi credentials at runtime rather than hardcoding them, thus enabling connectivity to any available Wi-Fi access point. Once successfully connected to Wi-Fi and after verifying sensor connectivity, data transmission commences at one-minute intervals. This frequent interval is crucial for promptly detecting any fluctuations in the athlete’s vital sign status. On the Firebase side, as per the library documentation, data storage at a designated node in the Firebase RTDB involves using specific functions such as set, setInt, setFloat, setDouble, setString, setJSON, setArray, setBlob, and setFile. In our case, we utilize setFloat for parameters like acceleration (x, y, z), gyroscope readings (x, y, z), temperatures, heart rate (BPM—beats per minute), SpO2, and timestamps (in UTC format), while setInt is employed for alert status. Retrieval of these data points is facilitated using the getFloat and getInt functions, respectively. Authentication credentials are imperative for accessing our Firebase RTDB. Hence, we establish a username–password combination, alongside utilizing the Firebase API key in the connection handler
y, z), temperatures, heart rate (BPM—beats per minute), SpO2, and timestamps (in UTC format), while setInt is employed for alert status. Retrieval of these data points is facilitated using the getFloat and getInt functions, respectively. Authentication credentials are imperative for accessing our Firebase RTDB. Hence, we establish a username–password combination, alongside utilizing the Firebase API key in the connection handler on the device side. Furthermore, we implement a design mod-
Description
The study introduces a wearable glove for monitoring athletes' vital signs.