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Faculties of Physical Therapy and Engineering, Cairo University, Egypt1
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Trust Research Center, Mokkatam, Cairo, Egypt2
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Multimedia Processing, Communication, and Interaction Lab, Arab Academy for Science, Technology, and Maritime Transport, Alexandria , Egypt3
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VRapeutic Inc., Ottawa K2M1T2, CanadaAliaa Rehan Youssef
Mohammed Gumaa
Ahmad Al-Kabbany
This study is concerned with the application of virtual reality (VR) in the rehabilitation programs for faulty neck posture which is a primary source of neck pain (NP). The latter is a highly prevalent musculoskeletal disorder that is associated with serious societal and economic burden. VR has been shown effective in the physical rehabilitation of various diseases. Specifically, it has shown to improve patients&#; adherence and engagement to carry out physical exercises on a regular basis. Many games have been used to manage NP with different immersion levels. Towards this goal, we present a VR-based system that targets a specific neck problem, the so called forward head posture (FHP), which is a faulty head position that abnormally stresses neck structures. The system can also generalize well to other neck-related disorders and rehabilitation goals. We show the steps for designing and developing the system, and we highlight the aspects of interaction between usability and various game elements. Using a three-point scale for user experience, we also present preliminary insights on the evaluation of the system prototype, and we discuss future enhancement directions based on the users&#; feedback.
The rest of this article is structured as follows. In section 2, we highlight previous research on gamification-based neck rehabilitation that is relevant to the proposed study. In section 3, we present the design and development of the proposed system. Section 4 features the results obtained from the preliminary prototype evaluation, before the study is concluded in section 5.
We highlight a set of challenges that we faced during the implementation of the system design framework, and we present the adopted alternatives as well as the pros and cons of the initial and eventual approaches.
Previous research referred to the role of deep neck flexor (DNF) muscles strength and endurance as principal factors for improving NP especially in patients&#; with FHP [ 18 ] which is strongly associated with NP in adults [ 8 ] . However, to authors&#; knowledge, no VR games was specifically designed to target faulty head posture or FHP in those patients. Accordingly, developing a VR-based deep cervical training might be useful in improving FHP, decreasing NP, and improving users&#; acceptance to exercises. In this research, we present the design, development, and prototype evaluation processes of a new VR-based module named Necknasium TM for correcting FHP. This module takes place in a gymnasium (hence the name) where the users are instructed to do specific repetitions of movements that involves lifting a weight for a specific vertical distance in order to help improving their FHP and hence manage a potential primary source of their NP. The contributions of this research can be summarized as follows:
Technology-based therapy for neck disorders and pain has been the focus of an ever-growing body of research [ 15 ] . There are three main directions for incorporating technology in rehabilitation programs. First, it has been used as the medium through which specific exercises are being displayed to the user, which capitalizes on the use of technology as a source of motivation for completing the exercises [ 9 ] . Second, technology was incorporated in other studies as means for tracking motions [ 16 ] . This would represent a repository of performance data acquisition according to which therapy plans can be developed and modified. Third, technology is some-times used as means of tele-rehabilitation [ 4 ] . The research on this application, in particular, had thrived while COVID-19 was soaring worldwide.
The research literature on applying technology in physical therapy and rehabilitation is immense. Diverse forms of software and hardware technology can be found in the literature including 2D and 3D serious games [ 10 ] , tablets and VR headsets [ 9 ] , sensors, therapeutic robots [ 6 ] , and tele-rehabilitation-based delivery [ 17 ] .
With the increased use of handheld devices, prolonged office work hours, and other activities that force neck into bad posture, neck pain (NP) is increasing as a global problem nowadays. There are other more common musculoskeletal disorders, yet NP is not getting less significant [ 1 , 3 ] . Several studies have highlighted the severe societal and economic burden of spinal pain especially the neck in the US [ 5 ] , other African [ 13 ] and Scandinavian [ 14 ] countries. This just further emphasizes the highly-prevalent and global nature of this problem and its growing adverse impact as a burden on the healthcare systems and the world&#;s economy.
It is worth noting that none of the described studies targeted neck retraction, which is a translation movement performed specifically by the DNF muscles and is the primarily exercise employed to correct FHP.
Towards the reduction of pain and functional disability, VR was shown to have an advantage over traditional exercises in [ 12 ] . This study mainly targeted proprioception which refers to the sense of movement and position of the neck. The proposed Cervigame ® is a video game in which the user is required to control the movements of a rabbit using their head movements. The amount of required control progresses over fifty levels. The proposed systems involved placing a reflective marker just above the eyebrows of the patient. Moreover, a Head Mouse Extreme ® (Origin Instruments Corporation, Grand Prairie, Texas, USA) was position at the top of laptop&#;s monitor and aimed on reflective markers. This setup enabled head motions to control a pointer movement that was placed on the laptop&#;s monitor.
The authors of [ 2 ] addressed the ROM in addition to the movement velocity and accuracy. They developed a system for clinic and home-based neck rehabilitation that featured two main components. First, a hardware component with an Oculus Rift TM and 3D motion sensors. Those sensors include a gyroscope, an accelerometer, and a magnetometer as well as Complementary Metal-Oxide-Semiconductor (CMOS) sensor for positional tracking. Second, there is a software component which is represented by the VR modules and the motion data analysis programs specially designed for that study. Three modules were developed, each of which addresses ROM, velocity, and accuracy. The modules were designed to visually-stimulate cervical motion by the patient. This study supported VR efficacy in the assessment and treatment as evident by improving pain, disability, movement velocity and accuracy, and quality of life after up to three months follow-up.
The authors of [ 9 ] proposed a smartphone-based VR exergame app for deep neck flexor (DNF) endurance exercise. Mainly, it addressed posture correction and improving the range of motion (ROM). The prototype validation was accomplished on three healthy individuals. However, this system still lacks scientific evidence of its clinical efficacy. Another system was proposed in [ 11 ] . The user is asked to chase a butterfly. This task was chosen to train head-neck movement coordination during high speed motion in addition to static positioning when the butterfly standstill in the air. The system consisted of assessment and treatment modules that are consumed through Oculus Rift TM . Lastly, the research was concluded with a case study that featured higher interest and enjoyment.
It is worth mentioning that the proposed system, with its reliance on the em-bedded sensors in the VR headset, can be adopted in other use cases for neck disorders, not just FHP-related NP that focuses on improving retraction movement. Because we have access to all the information about device position, velocity, and acceleration in the three dimensions as a function in time, the proposed system can be incorporated in rehabilitation plans that involve other movements other in different plans such as neck bending and extension.
The task inside the VR environment is to raise a weight bar in a gym, hence the name&#;Necknasium, for a certain number of repetitions. Following the calibration, step, from a user&#;s/trainee&#;s perspective, the system provides six levels for doing the aforementioned task. The first three levels require the user to perform a neck retraction movement to at least 30%, 60%, and 90% of the maximum range, respectively. Each of these levels require thirty retractions or repetitions in order to train DCF muscles strength. Levels four, five, and six require the user to do the same task as the first three levels except that they also require the user to stay in the retraction position for a longer time to target the neck muscles&#; endurance. Fig. 1(d,e) show two screen-shots from inside the VR environment where the bar is at its initial and end positions, respectively, where the end position is associated/marked with a visual effect and an auditory stimuli to complement the VR environment.
As shown in the main menu in Fig. 1(a), automatic and manual calibrations are supported by the system. From the therapist point of view, it is helpful to be able to set the maximum range of neck retraction manually to ensure personalized rehabilitation based on each individual patient&#;s needs. On the other hand, in remote rehabilitation scenarios, it might also be helpful to make the system capable of doing an automatic calibration. This might serve as a validation of the subjective distance that a therapist can estimate remotely. When the manual calibration is chosen, the menu in Fig. 1(b) is displayed, where the user can set a maximum retraction distance using a slider. The automatic calibration, however, requires the user to long-press a button on the controller twice, at the beginning and the end of the neck retraction motion, then the maximum retraction distance is saved automatically.
An Oculus Quest 1 ® VR headset was used throughout the software development and the implementation of this study. This standalone VR headset offers access to several types of motion information. Figure 1(a) shows the types of motion information which we had access to from the VR headset being shown on the left of the main menu of the system. Following several experiments, we found that the device position can be relied on to detect the neck retraction movement, which is required during the FHP correction exercise. We also found that the angular velocity or acceleration can be used to detect erroneous movements during the exercise. Exceeding a threshold of angular velocity or acceleration is set to display a warning to the user.
Akin to [ 7 ] , we initially tried to design and develop external inertial motion units (IMUs) which were supposed to communicate with the exercise on the VR headset through a WiFi module. The main role of the IMUs was to sense the motion of the neck in real-time, send the magnitude of this motion, and then this motion is translated to a game event in the VR environment. The sole purpose of building our own IMUs was to have full control over the type of data they collect throughout the exercise. However, the manufactured IMUs had suffered from sporadic noise and unstable behavior often. This was quite apparent when the readings of the IMUs were compared to motion video analysis using the open source Kinovea software ® . Accordingly, we resorted to the motion sensors embedded in the VR headset.
The task to be accomplished within the virtual environment, which represents the neck exercise, that should look as realistic as possible and should be a real-life task to guarantee an acceptable level of motivation and engagement.
The system design requirements are inspired by the following aspects: 1) the targeted user experience from the perspective of the rehabilitation professionals and trainees, 2) the targeted exercise dynamics for FHP correction, 3) and the targeted level of training customization or personalization. We require the proposed system to satisfy the following points:
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Results and Discussion&#;A Preliminary Prototype EvaluationThe clinical effectiveness of the proposed system is beyond the scope of this study. Yet previous studies that highlighted the influence of neck exercises regarding reduction of pain and its associated disability. These studies include [4,5] and [12]. Instead of clinical effectiveness, we aim at investigating the feasibility of realizing engaging exercises through VR exergaming, which would positively impacts patients&#; adherence to exercises and hence improving sustainability of physical therapy rehabilitation to achieve its target outcome.
As stated in the previous section, neck movements is tracked in real-time using the motion sensors embedded in the VR headset. Hence, those senors represent a source of quantitative evaluation for the user performance and progress achieved throughout the therapy program. In addition, the system provides real-time feedback that motivates patients&#; to exert maximum possible effort during the training process. Based on this continuous real-time monitoring, a therapist would develop and modify the rehabilitation program to target changing individual&#;s needs. These data include basic statistics (e.g., maximum, minimum, range) of the distance moved by the neck throughout the repetitions of the exercise.
Towards conducting a preliminary prototype evaluation, the users are instructed to complete the following settings:
1.
Level 3 of the VR module, which is the last level that addresses the DNF muscles&#; strength. In this level, the user is required to complete thirty neck movements, each of which should last for at least six seconds. We refer to this setting as Setting 1 in the rest of this section.
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Level 6 of the VR module, which is the last level that addresses DNF muscles&#; endurance. In this level, the user is required to complete thirty neck movements, each of which should last for at least ten seconds. We refer to this setting as Setting 2 in the rest of this section.
Three volunteers were recruited to participate in this evaluation, all of them are males in the age range from 21-24 years old. Two of those volunteers are undergraduate engineering students with one year of experience in game development. The third participant is a junior game developer with one year of work experience in VR development. All participants were healthy and asymptomatic with no history of NP. Initially, all participants received full description of the study purpose and the required tasks. Prior to actual game trials, they were shown a video that explains how traditional neck retraction exercise looks like. As will be shown below, this part becomes a core component of this participation in the rest of the evaluation study.
Two aspects of evaluation were covered in this study, namely, a subjective rating for engagement and preference of VR-based intervention and another subjective rating for user&#;s experience. The experience was evaluated on a scale from -/+1. The former rating comprises the following seven questions: 1) Was the exercise engaging?, 2) How fun was the activity?, 3) Did it feel like a physical exercise?, 4) Would you repeat performing this exercise?, 5) Would you perform it again in case of health necessity?, 6) Would you prefer this exercise over conventional training video that was shown in the YouTube video?, and 7) Would the audio-based feedback constitute an essential element in the exercise? Figure 2 shows the distribution of responses on questions 1 through 7 for Setting 1 and Setting 2.
Figure 2:
Distribution of responses on question 1 to question 7&#;the subjective rating for engagement and preference of VR-Based intervention&#;for a) setting 1 and b) setting 2. The x-axis represents the seven questions representing the subjective rating.The responses of the participants show that although the exercise is engaging, they did not consider it fun. In this research, we adopt the following operational definitions for what is engaging and what is fun. An engaging activity is defined as a task that attracts a participant&#;s attention to complete it, yet it does not get their interest. An activity is considered fun if a participant is interested in doing it, and hence it does not fail to acquire their attention by default. The participants might have found the exercise engaging but not interesting due to the nature and the environment of the task. Also, all of them practice e-sports regularly but only one of them goes to gym on a weekly basis. Towards better engagement, it might worth it to offer more diversity in the tasks required from the users. Also, sensory integration, such as auditory stimulation, might not be critical for strength exercises. However, as the training becomes more demanding, the influence of the sound element might become more apparent. This is something to be considered in future designs of the system. Moreover, the existence of a health urgency seems to be a significant factor for participants to use such a system, although they didn&#;t consider the exercise to have a fun factor. Finally, the results shown in Fig. 2 indicates that VR is still more appealing to the participants compared to traditional exercises.
The subjective rating of the user experience comprises a set of seven aspects. These aspects are obstructive/supportive, complicated/easy, confusing/clear, boring/exciting, not interesting/interesting, conventional/inventive, and usual/leading edge. Table 1 shows the mean score for each of those aspects. For each aspect, the user either gives -1 or 1 if inclined towards one of the two sides, or zero if neutral.
Table 1:
The mean scores for each of the seven aspects included in the subjective rating of user experience. Please see text for more details.Mean Score
Obstructive
1Supportive
Complicated
0.67Easy
Confusing
0.33Clear
Boring
0.33Exciting
Not interesting
-0.67Interesting
Conventional
0.67Inventive
Usual
0Leading Edge
The above table shows that further work should be done to make the instructions clearer to the user. The level of interesting-ness is expected in light of the previous subjective rating for engagement, since the content was found to be lacking fun by all the participants, although engaging. This results makes it more imperative to work on a larger variety of tasks. Moreover, although the setting might have seemed inventive, the potential of virtual reality as a visual stimuli still needs more improvement. This was apparent in the score of the last aspect which shows that the user were neutral, although the system design was meant to be leading edge. These mean scores will guide the development process of the future releases of the proposed system.
The ability to monitor position and movement profiles of an athlete is critical in developing improved training regimens to maximize individual performance (Fig. ). The accuracy of devices, such as pedometers has been in question and was recently studied. Researchers compared the accuracy of the &#;step-count&#; feature between dedicated smartphone-based pedometer applications (Galaxy S4 Moves App, iPhone 5s Moves App, iPhone 5s Health Mate App, iPhone 5s Fitbit App) and wearable devices (Nike Fuelband, Jawbone UP24, Fitbit Flex, Fitbit One, Fitbit Zip, and Digi-Walker SW-200) with direct observation of step counts.28 Results showed a relative difference between actual and reported mean step count of &#;0.3% to 1.0% for pedometers and accelerometers, &#;22.7% to &#;1.5% for wearable devices, and &#;6.7% to 6.2% for smartphone applications. Such differences were attributed to the robustness of the IC technology and software algorithms used to determine a step. Step counts are often used to derive other measures of physical activity, such as distance traveled or calories expended.28 Hence, improving measurement accuracy is crucial to measure and appropriately tailor workout regiments for elite-level athletes.
Movement-based sensors currently in use for sports-medicine include accelerometers and global positioning satellite (GPS) devices, often used in combination (Table ). Accelerometers generate highly accurate analyses of movement with high sampling rates and have been included in wrist-based devices, such as the Nike Fuelband, Jawbone UP, and Microsoft Band. This technology has been widely adopted in the sporting community ranging from Australian Football,29 Rugby,30,31 NFL,32 National Hockey League (NHL),33 and swimming.34&#;36 Energy expenditure can be determined from tri-axial accelerometers via the integration of acceleration over time.37,38 The determination of energy expenditure, position, movement, and balance control during practices or games has shown to be instrumental in tailoring the training regimen of athletes to minimize the incidence of soft tissue injuries.
Banister et al. postulated that athletic performance can be estimated as a function of fatigue and fitness39 (Fig. ). Building upon this model, Morton et al. suggested that an opportune training stimulus is one that maximizes performance by utilizing an appropriate training load, while simultaneously minimizing injury and fatigue.40 A working definition of fatigue is &#;any exercise-induced or non-exercise-induced loss in total performance due to various physiological factors, athlete reported psychological factors, or a combination of the two&#;.41 It is well known that fatigue decreases athletic performance and that training induces numerous neurophysiological and psychological changes in an athlete&#;s body. There are two forms of fatigue: central fatigue and peripheral fatigue. Central fatigue is the fatigue resulting from the central nervous system (CNS) and the transmission of signals from the brain to the muscle.42 Central fatigue is related to the interaction between the brain and the spinal cord.43 Researchers have hypothesized that the differentiation between a good athlete and an elite-level athlete is their individual ability to ignore such neurotransmissions during high-acuity situations (e.g. high profile matches or workouts).42 Peripheral fatigue is the failure to maintain an expected power output caused by the depletion of glycogen, phosphate compounds, or acetylcholine within the muscular unit or by the accumulation of lactate or other metabolites that are released during activity.44,45 Peripheral fatigue occurs within the muscle and can be thought of as &#;muscle fatigue&#;.43 As such, wearable sensors can be used to measure parameters indicative of the peripheral fatigue of the athlete, as is discussed in detail throughout this review. For simplicity purposes, we refer to peripheral fatigue as simply fatigue.
Monitoring internal (e.g. physiological or perceptual &#;response&#;) and external training loads (e.g. physical &#;work&#;) can enable sports trainers and clinicians to assess the fatigue and fitness levels of athletes in real time. Internal workload includes the session rate of perceived exertion (sRPE) and heart rate.46 At the completion of each training session, athletes provide a 1&#;10 &#;rating&#; based on the intensity of the session.46 The intensity of the session is multiplied by the session duration to provide the internal training load.46 The product can be thought of as the athletes&#; &#;exertional minutes&#;.46 Advancements in MEMS fabrication techniques and device packaging have allowed for the detection of multi-axial movement to calculate an external training load (e.g. PlayerLoad&#;3). External workload can be thought of as how much load is placed on the body and can be quantified using torso-mounted wearable devices which contain a GPS and a tri-axial accelerometer.46 PlayerLoad&#; can be calculated via the instantaneous rate of change of acceleration. Accumulated PlayerLoad&#; can be calculated as the summation of PlayerLoad&#; over the desired time interval (usually over a span of 1&#;7 days).
Metrics such as total distance run, weight lifted, number and intensity of sprints or collisions can be determined using GPS-based sensors. Position sensors triangulate signal transmission from multiple GPS satellites orbiting the earth and can accurately determine the velocity and position (within 1&#;m) of an athlete on a field. These devices are playing an instrumental role in sports performance analysis by allowing coaches, physicians, and trainers to better understand real-time physical demands of an athlete.30,37,47 GPS silicon chips combined with tri-axial accelerometers have been used to record physical activities during different times of the day and for specific position groups on a team.48 The majority of work to assess human motion and its correlation to sports performance has involved the use of commercial GPS-based devices, such as the Catapult devices (OptimEye S5) and Zebra Technologies GPS device. The Catapult product, for example has a fully packaged processing IC, accelerometer, gyroscope, and magnetometer to measure body position, impact forces, velocity, acceleration, and direction in a continuous manner.49 In a study utilizing the Catapult OptimEye S5 and video tracking technology, 20 professional Australian Football League (AFL) players were studied during four in-season matches to describe and quantify the frequency, velocity, and acceleration at impact during tackling29 (Fig. ). Distributions in tackles were quantified and classified as a function of percent distribution of tackles versus player load (Fig. ), player velocity versus tackle intensity (Fig. ), and player load versus tackle intensity (Fig. ). Differences in accelerometer data between tackles were observed to be progressively greater in intensity thereby providing support for the use of accelerometers to assess impact forces in contact-based sports.29 In another study, GPS sensors and related analytics were used by National Collegiate Athletic Association (NCAA) Division I Football athletes to record workload, velocity, distance, and acceleration during both practices and games.48,50 The studies found significant variation in movement profiles among collegiate football players and the authors identified the need for position-specific and game-specific physical conditioning strategies to maximize player performance, limit the effects of fatigue, and minimize the onset of injuries.
The combination of the internal and external workloads of the athlete determine the training outcome.46 An athlete&#;s internal or external workload can be computed over a 1-week period (acute workload) and over a 3&#;4-week period (chronic workload). Work by Gabbett suggested that the ratio of the acute-to-chronic workload, herein referred to as &#;ACWR&#;, can be used to determine if an athlete is overtraining, undertraining, or training at the opportune intensity46 (Fig. ). Furthermore, Gabbett showed that calculation of this ratio enables sports scientists to predict the chance an athlete suffers an injury as a result of improper load management.46 Deriving this ratio provides an index of athlete preparedness and considers the training load that the athlete has performed relative to the training load that the athlete has been prepared for.51 The use of the ACWR emphasizes both the positive and negative consequences of training. The first study to investigate the relationship between ACWR and the risk of injury was performed on elite cricket fast bowlers.52 Training loads were estimated from both sRPE and balls bowled. When the acute workload was similar to or lower than that of the chronic workload (e.g. ACWR&#;&#;&#;0.99), the likelihood of injury for fast bowlers in the next 7 days was 4%.52 However, when the ACWR was &#;1.5 (e.g. workload in the current week was 1.5 times greater than what the bowler was prepared for), the risk of injury was 2&#;4 times greater in the subsequent 7 days.52 While such observations are indicative of the sport being studied, until more robust data sets are available, caution must be heeded when applying these recommendations to individual sport athletes. Despite this, a general trend can be concluded. If the acute training load is low (e.g. the athlete is experiencing minimal fatigue) and the rolling average (RA) chronic training load is high (e.g. the athlete has developed &#;fitness&#;), then the athlete will be in a well-prepared state and thus, the ACWR will be &#;1.46 If the acute load is high (e.g. training loads have been rapidly increased resulting in &#;fatigue&#;) and the RA chronic training load is low (e.g. the athlete has performed inadequate training to develop &#;fitness&#;), then the athlete will be in a fatigued state and thus, the ACWR will be &#;1.46 In terms of injury risk, ACWRs within the range of 0.8&#;1.3 could be considered the training &#;sweet spot&#;, while an ACWR&#;&#;&#;1.5 could represent the &#;danger zone&#;.46
The RA model53 (Eqs. (1&#;4)) and exponentially weighted moving average (EWMA) model54 (Eqs. (5&#;10)) are two methods used to calculate the training load of the athlete with or without the use of wearable sensors like the Catapult OptimEye S5 (Eqs. (11&#;14)).3 The RA model uses an absolute (i.e. total) workload performed in one week (acute workload) relative to the 4-week chronic workload (e.g. 4-week average acute workload).53 Equation (1) represents the exertional minutes per workout which is the product of the session rating of perceived exertion and the duration of the workout in minutes. The sRPE is a scale from 1 to 10 with progressing intensity of the workout deemed by the athlete and training staff. Equation (2) shows the acute player load (PL) which is the summation of the exertional minutes per workout for a given week (e.g. from day 1 to day 7). For the sake of simplicity, we assume the athlete is completing one workout per day. Equation (3) shows the chronic PL which is computed by taking the average of the acute PL over the duration of weeks (denoted as n). Equation (4) shows the ACWR which is the ratio between the acute PL for the given week (Eq. (2)) and the chronic PL (calculated from Eq. (3)). The RA model suggests that each workload in an acute and chronic period is equal. In other words, the model considers there to be a linear relationship between load and injury. The assumption in this model is that all workload in a given time period is equivalent. Key drawbacks of this model are that the model does not account for any decays in fitness and it does not accurately represent variations in the manner in which loads are accumulated.
Exertionalminutesperworkout=SRPE×durationofworkoutinminutes
1
AcutePL= &#;D=1D=7exertionalminutesperworkout
2
ChronicPL=&#;W=1W=nAcutePLn
3
ACWR=AcutePLforgivenweekChronicPL
4
Sports scientists have started to apply the EWMA model to circumvent the drawbacks posed by the RA model.54 The EWMA model places a greater weight on the most recent workload an athlete has performed by assigning a decreasing weighting for each older workload value (time decay constant, λa) and the non-linear nature of injury occurrence and workload.54 Equation (5) shows the exertional minutes per workout which is the product of the session rating of perceived exertion and the duration of the workout in minutes. The sRPE is a scale from 1 to 10 with progressing intensity of the workout deemed by the athlete and training staff. Equation (6) shows the degree of decay, λa, which is a value between 0 and 1, with higher values of λa discounting older observations in the model at a faster rate. In the following equation, n represents the time decay constant. Equation (7) shows the formula to calculate the EWMA for a given day which is based on the exertional minutes, calculated from Eq. (5), the degree of decay from Eq. (6), and the EWMA from the preceding day. Equation (8) shows the acute player load (PL) which is the summation of the EWMA for a given week (e.g. from day 1 to day 7). For the sake of simplicity, we assume the athlete is completing 1 workout per day. Equation (9) shows the chronic PL which is computed by taking the average of the acute PL over the duration of weeks (denoted as n). Equation (10) shows the ACWR which is the ratio between the acute PL for the given week (Eq. (8)) and the chronic PL (calculated from Eq. (9)). A recent study sought to investigate if any differences existed between the RA and EWMA models pertaining to ACWR calculation and subsequent injury risk in elite Australian footballers.54 Fifty-nine athletes from an AFL club participated in this 2-year study. A total of 92 individual sessions were recorded. Each season consisted of a 16-week preseason phase comprised of both running and football-based sessions, followed by a subsequent 23-week in-season competitive phase. The Catapult OptimEye S5 GPS sensor, sampled at 10&#;Hz, was used to quantify training and match workloads of players. The triaxial accelerometer, gyroscope, and magnetometer were each sampled at 100&#;Hz. The study demonstrated that a high ACWR was significantly associated with an increase in injury risk for both models. The EWMA model had significantly greater sensitivity to detect increases in injury likelihood at higher ACWR ranges during both the preseason and in-season periods. The study concluded that the EWMA model may be better suited to modeling workloads and injury risk than the RAs than the ACWR model. Regardless of the ACWR model utilized, spikes in acute workload were significantly associated with an increase in injury risk.
Exertionalminutesperworkout=SRPE×durationofworkoutinminutes
5
λa=2N+1,where0<λa<1
6
EWMAtoday=Exertionalminutesperworkoutλa+1-λaEWMAyesterday
7
AcutePL= &#;D=1D=7EWMAtoday
8
ChronicPL=&#;W=1W=nAcutePLn
9
ACWR=AcutePLforgivenweekChronicPL
10
Wearable sensors are currently being used to minimize injury in professional football via careful monitoring of training load and other biometrics during the rehabilitation period (Fig. ). The variability of GPS data and accelerations of the torso have been in question when it comes to monitoring the loads of the lower limbs. This is because distance traveled and velocity do not represent the mechanical load experienced by the musculoskeletal tissue. This is specifically relevant in sports such as basketball, which are constrained to a small-space, where players experience high loads of physical stress by performing explosive jumping and landing activities, which are not accurately captured by distance, speed, or torso athlete movement analysis systems.55,56 To mitigate such issues, the Zebra GPS device and Catapult OptimEye S5, both of which are considered the most accurate wearable devices in sports today, are housed in player tracking devices in an attempt to negate some of the aforementioned issues. Additionally, the Catapult device has shown to mitigate such issues by incorporating tri-axial movements into their analytical models to accurately calculate PlayerLoad&#; from their sensor.3 The Zebra GPS device is currently approved by the NFL for use to track player movement and has been utilized by select teams to monitor training loads.57 Equation (11) provides the analytical platform of the Catapult OptimEye S5 which utilizes a tri-axial accelerometer to calculate PlayerLoad&#; (PL) based on acceleration in the x, y, and z directions. Equation (12) shows the summation of PL from the initial to end time of interest (in most cases this is from the start to the end of a training session) denoted as AccPL&#;. Equation (13) shows how the RA model can be used to calculate Acute PL, analogous to Eq. (2). However, in this case, PL is calculated from Eq. (11) using a wearable sensor. Eq. (14) shows how the EWMA model can be used to calculate PL for the given day using PL calculated from a wearable sensor. The ACWR can be calculated utilizing either model, adapting the set of equations presented (rolling average, Eqs. (1&#;4); EWMA, Eqs. (5&#;10).
PL=fwdt=i+1-fwdt=i2+sidet=i+1-sidet=i2+upt=i+1-upt=i2
11
AccPL= &#;t=0t=nfwdt=i+1-fwdt=i2+sidet=i+1-sidet=i2+upt=i+1-upt=i2
12
AcutePL= &#;D=1D=7AccPL
13
EWMAtoday=PLλa+1-λaEWMAyesterday
14
In a specific example reported by an American sporting network, the device was used to accurately track the recovery of an athlete after the individual suffered a season ending injury the previous year.57 The sensor was placed underneath the shoulder pads (analogous to that of the Catapult device) or sewn into the jersey to generate biometric measurements, such as movement profiles and workload to gauge the athlete&#;s performance and workload during recovery relative to his peak performance and workload prior to the injury. Additionally, utilizing the Catapult OptimEye S5 wearable sensor, authors of this review have recently studied the effects of player workload on soft tissue injuries in a single NFL team over two seasons.32 Rapid changes in workload over a one-week period when compared to the average workload over a month were associated with a significant increase in risk of hamstring and other soft tissue injuries. The studies demonstrated that monitoring athletic training programs during the pre-season compared to the post-season utilizing wearable technology have assisted team athletic trainers and medical staff in developing programs to optimize player performance and minimize soft-tissue injuries.32
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