A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks

thletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community's interest because of its capability to provide real-time biofeedback to coaches and athletes when on the eld and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with suf cient accuracy to provide valuable information as regards training

involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with suf cient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, suf ciently small for real-world scenarios, easy to use, and achieves the speci ed communication range. The system's high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice. Keywords: accelerometer; ground reaction force; GRF; inertial measurement unit; IMU; jumping; performance; running; sport; wearables 1. Introduction Athletic performance, technique assessment, and injury prevention are all aspects of great importance in sports at present, for coaches and athletes alike, and are experiencing a growth in interest from the research community. As a consequence, the development of automated, objective, and reliable performance monitoring and evaluation systems, through quantitative analyses of performance variables, is now seen as an essential tool for the improvement of athletic performance and the minimization of injury risk [1,2]. Wearable sensors represent an alternative to gold-standard lab-based assessments because of their potential to monitor performance without hindering it while providing real-time feedback with no space limitation or infrastructure set-up procedures, as well as their advantages in the areas of portability, low-cost, and ease-of-use [3–5]. For instance, wearable technology may be used to estimate temporal (e.g., stride time), kinematic (e.g., joint range of motion), and dynamic parameters (e.g., joint forces and moments), as well as motor capacity, workload, and technique, in a number of sport tasks (e.g., swimming, running, team sports, jumping, and strength assessment) [6]. However, few studies have investigated the possibility of combining those disciplines to support coaches in predicting and minimizing injuries [7]. For example, running-related injuries have a complex and multifactorial etiology that is also dependent on aberrant Appl. Sci.2021,11, 5258.

sports, jumping, and strength assessment) [6]. However, few studies have investigated the possibility of combining those disciplines to support coaches in predicting and minimizing injuries [7]. For example, running-related injuries have a complex and multifactorial etiology that is also dependent on aberrant Appl. Sci.2021,11, 5258.

Appl. Sci.2021,11, 5258 2 of 23 biomechanics and training load errors [8]. Wearables, therefore, can provide important insights into the kinetics potentially responsible for injurious tissue loads, as well as indicate the effectiveness of an intervention [8]. Thus, coaches' decision-making can be greatly enhanced by the use of wearable sensors to ensure that a biomechanical intervention is truly helping the athletes by minimizing the risk of running-related injuries [8]. In coaching practice, sport-speci c activities (such as change-of-direction), jumping, running, and sprinting tasks are largely adopted for athletic performance evaluation [9,10] in a number of sports, e.g., soccer, rugby, etc. For example, it is common to examine jumps before and after training to assess the effectiveness of a speci c intervention [11]. Nevertheless, only a limited number of solutions, both in literature and on the market (for example, Xybermind [12]), have been developed to tackle most of the scenarios sport coaches deal with, with the majority of them merely targeting an automatic classi cation of different sport activities [13] and their intensity [14]. Running and running-related injuries have been widely investigated through the use of wearable sensors [15–17], typically considering spatio-temporal variables such as cadence, contact and swing time, stride length, symmetry, and so on. Nevertheless, loading and related metrics, with particular reference to the vertical ground reaction force (GRF), are also gaining the researchers' attention [18] because of their high correlation with tibial shock and stress injuries in runners [8]. A number of papers have shown that GRF waveforms could be estimated effectively via neural networks [19–24] with superior results compared to biomechanical modeling, as demonstrated in a recent comparative study [25]. On the other hand, jumping tasks have also been extensively studied using wearable technology [26–28]. However, many of those investigations generally focused on correctly estimating temporal events (e.g., take-off, landing), jump height, and ight time. Given that the application of wearable sensors will revolutionize exercise science research because of their portability and capability of collecting a multitude of movement data, research in the area will be facilitated by the development of a meaningful solution able to identify

of those investigations generally focused on correctly estimating temporal events (e.g., take-off, landing), jump height, and ight time. Given that the application of wearable sensors will revolutionize exercise science research because of their portability and capability of collecting a multitude of movement data, research in the area will be facilitated by the development of a meaningful solution able to identify and provide insights based on the analysis of a substantial amount of data collected [29]. The aim of the present work is, therefore, to develop a complete integrated solution that could be used by coaches while athletes are performing running and jumping tasks for monitoring performance and evaluate possible injury risks. The objectives of the investigation are, therefore, many-fold: To develop a wearable solution based on inertial measurement units (IMUs) which could be worn on different body locations and are suitable for different physical tasks; To automatically detect every individual jump performed, as well as segment the running bouts and, as a consequence, each running stride from both legs; To provide running performance metrics from the data recorded by the IMUs, such as contact time, step time, mean force, stability, cadence, etc.; To provide vertical GRF waveforms for each segmented running stride for both legs and extrapolate the associated metrics; To provide jumping metrics from the kinematics recorded by IMU, including ight time, jump height, peak force, mean force, etc., and for the different phases of the jump (eccentric and concentric); To provide an easy-to-use graphical interface for an effective visualization of the estimated variables. The achievement of all these objectives will allow the development of a complete wearable solution that coaches and athletes could use in their real-world practice, which represent the ultimate goal of this work. The manuscript is organized as follows. The proposed system architecture is discussed in Section, while the hardware and the software components of the system are illustrated in Sections, respectively. The graphical interface is shown in Section. Testing and results are discussed in Section. A state-of- the-art comparison with products on the market is illustrated in Section. Final discussion and conclusions are illustrated

is organized as follows. The proposed system architecture is discussed in Section, while the hardware and the software components of the system are illustrated in Sections, respectively. The graphical interface is shown in Section. Testing and results are discussed in Section. A state-of- the-art comparison with products on the market is illustrated in Section. Final discussion and conclusions are illustrated in Sections, respectively.

Appl. Sci.2021,11, 5258 3 of 23 2. System Architecture The system has been built to provide performance-related metrics based on the sce- nario where jumping tests are performed before and after training, and running is the main activity performed during training, as it generally occurs in sports such as soccer or rugby. While running, the system relies on two wirelessly synchronized boards located on the left and right shanks; on the other hand, only one device worn on the pelvis is required for jumping (Figure). The pelvis was preferred over the shank for the prediction of jumping forces since the sensor would be mounted closer to the subjects' center of mass, while the estimation of the total impact force would be unaffected by asymmetrical landings between the left and right foot. Adjustable Velcro straps are used in both cases for attachment to the athlete under test. The athlete can, therefore, use the same boards between activities by simply placing the boards on different body locations. This approach minimizes the number of devices simultaneously worn, which is never more than two units. The boards can perform in a number of modes (i.e., “USB”, “running”, or “jumping”) depending on the number of times a touch button is pressed by the user. This control feature is also used for starting and stopping the collection of data which is stored internally on an SD memory card for post-processing. Whenever a device operates in “USB” mode, it can also be plugged in any computer able to read USB mass storage drives. Complex back-end analytics, including activity recognition algorithms to automat- ically separate the time segment of interest for the analysis, are used separately on a computer to provide the various metrics requested for the different activities based on the data stored on the plugged-in device. A graphical user interface (GUI) has been developed and integrated in the system to visualize, export and save the results of the analysis and to allow coaches and athletes to interact with the system. A graphical depiction of the system adoption is shown in Figure.Appl. Sci. 2021, 11, x FOR

activities based on the data stored on the plugged-in device. A graphical user interface (GUI) has been developed and integrated in the system to visualize, export and save the results of the analysis and to allow coaches and athletes to interact with the system. A graphical depiction of the system adoption is shown in Figure.Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 23 interface is shown in Section 5. Testing and results are discussed in Section 6. A state-of- the-art comparison with products on the market is illustrated in Section 7. Final discussion and conclusions are illustrated in Sections 8 and 9, respectively. 2. System Architecture The system has been built to provide performance-related metrics based on the sce- nario where jumping tests are performed before and after training, and running is the main activity performed during training, as it generally occurs in sports such as soccer or rugby. While running, the system relies on two wirelessly synchronized boards located on the left and right shanks; on the other hand, only one device worn on the pelvis is required for jumping (Figure 1). The pelvis was preferred over the shank for the prediction of jumping forces since the sensor would be mounted closer to the subjects’ center of mass, while the estimation of the total impact force would be unaffected by asymmetrical land- ings between the left and right foot. Adjustable Velcro straps are used in both cases for attachment to the athlete under test. The athlete can, therefore, use the same boards be- tween activities by simply placing the boards on different body locations. This approach minimizes the number of devices simultaneously worn, which is never more than two units. The boards can perform in a number of modes (i.e., “USB”, “running”, or “jump- ing”) depending on the number of times a touch button is pressed by the user. This control feature is also used for starting and stopping the collection of data which is stored inter- nally on an SD memory card for post-processing. Whenever a device operates in “USB” mode, it can also be plugged

of modes (i.e., “USB”, “running”, or “jump- ing”) depending on the number of times a touch button is pressed by the user. This control feature is also used for starting and stopping the collection of data which is stored inter- nally on an SD memory card for post-processing. Whenever a device operates in “USB” mode, it can also be plugged in any computer able to read USB mass storage drives. Complex back-end analytics, including activity recognition algorithms to automati- cally separate the time segment of interest for the analysis, are used separately on a com- puter to provide the various metrics requested for the different activities based on the data stored on the plugged-in device. A graphical user interface (GUI) has been developed and integrated in the system to visualize, export and save the results of the analysis and to allow coaches and athletes to interact with the system. A graphical depiction of the system adoption is shown in Figure 2. Figure 1. Devices placement on shanks and pelvis during running and jumping tasks. Figure 1.Devices placement on shanks and pelvis during running and jumping tasks.Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 23 interface is shown in Section 5. Testing and results are discussed in Section 6. A state-of- the-art comparison with products on the market is illustrated in Section 7. Final discussion and conclusions are illustrated in Sections 8 and 9, respectively. 2. System Architecture The system has been built to provide performance-related metrics based on the sce- nario where jumping tests are performed before and after training, and running is the main activity performed during training, as it generally occurs in sports such as soccer or rugby. While running, the system relies on two wirelessly synchronized boards located on the left and right shanks; on the other hand, only one device worn on the pelvis is required for jumping (Figure 1). The pelvis was preferred over the shank for the prediction of jumping forces since the sensor would be mounted closer to the subjects’ center of mass, while the estimation of the total impact force

synchronized boards located on the left and right shanks; on the other hand, only one device worn on the pelvis is required for jumping (Figure 1). The pelvis was preferred over the shank for the prediction of jumping forces since the sensor would be mounted closer to the subjects’ center of mass, while the estimation of the total impact force would be unaffected by asymmetrical land- ings between the left and right foot. Adjustable Velcro straps are used in both cases for attachment to the athlete under test. The athlete can, therefore, use the same boards be- tween activities by simply placing the boards on different body locations. This approach minimizes the number of devices simultaneously worn, which is never more than two units. The boards can perform in a number of modes (i.e., “USB”, “running”, or “jump- ing”) depending on the number of times a touch button is pressed by the user. This control feature is also used for starting and stopping the collection of data which is stored inter- nally on an SD memory card for post-processing. Whenever a device operates in “USB” mode, it can also be plugged in any computer able to read USB mass storage drives. Complex back-end analytics, including activity recognition algorithms to automati- cally separate the time segment of interest for the analysis, are used separately on a com- puter to provide the various metrics requested for the different activities based on the data stored on the plugged-in device. A graphical user interface (GUI) has been developed and integrated in the system to visualize, export and save the results of the analysis and to allow coaches and athletes to interact with the system. A graphical depiction of the system adoption is shown in Figure 2. Figure 1. Devices placement on shanks and pelvis during running and jumping tasks. Figure 2. Graphical depiction of the system adoption. After data collection for a speci c task, the device(s) are plugged-into a computer via USB, where a GUI will use back-end algorithms to process the data gathered in the device(s) and visualize the results to the

Figure 2. Figure 1. Devices placement on shanks and pelvis during running and jumping tasks. Figure 2. Graphical depiction of the system adoption. After data collection for a speci c task, the device(s) are plugged-into a computer via USB, where a GUI will use back-end algorithms to process the data gathered in the device(s) and visualize the results to the end-users.

Appl. Sci.2021,11, 5258 4 of 23 3. Hardware Design The following section deals with the design and development of the hardware compo- nents of the proposed wearable system for performance assessment in sport tasks. 3.1. Hardware Platform The requirements of the developed wearable solution involved the ideation of a system easy to use and to wear, requiring no physical connection between the two devices placed on the legs, while the units must exchange information wirelessly and allow easy access to the data from a computer. For this purpose, two identical boards were developed and designed to be autonomous from each other in terms of power supply and computational perspectives. When the units are located on the shanks while running, they store inertial data, which are wirelessly synchronized to each other by keeping the two devices connected with a constant wireless Bluetooth connection. In addition, both units can work in a stand-alone asynchronous mode in order to analyze jumping tasks. In this case, the user can choose either of the two devices and use it to record and analyze the jumping activity. Each board consists of a number of building blocks. Overall, the microcontroller is the main component of the system as it deals with the motion sensors, the wireless communication, the power management, the memory for data storage, and the computer interface to guarantee access to the les stored in the memory card for post processing. The microcontroller selected is the STM32F417IG from STMicroelectronics [30] as it offers low-power operations and high-performance by relying on an ARM ®® Cortex ®® -M4-based 32 bit architecture, with a single precision oating point unit, and an operating frequency of up to 168 MHz, and up to 1MB of Flash and 196 Kbytes of RAM. The board also includes a 9 DoF IMU (MPU-9250 from InvenSense [31]) connected to the microcontroller via I2C interface, with ranges of 16 g and 2000 dps for the accelerometer and the gyroscope, respectively. However, a magnetometer was not adopted due to the impact that magnetic interferences may have on the measurements. As recommended by the manufacturer, the IMU

This paper presents a wearable system for assessing athletic performance during running and jumping tasks.