具体实施方式：
[0101]In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. However, it should be apparent to those skilled in the art that the present teachings may be practiced without such details. In other instances, well-known methods, procedures, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure.
[0102]While this disclosure includes a number of embodiments in many different forms, there is shown in the drawings and will herein be described in detail particular embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principles of the disclosed methods and systems, and is not intended to limit the broad aspects of the disclosed concepts to the embodiments illustrated. As will be realized, the subject technology is capable of other and different configurations, several details are capable of modification in various respects, embodiments may be combine, steps in the flow charts may be omitted or performed in a different order, all without departing from the scope of the subject technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.
A. Definitions
[0103]This section identifies a number of terms and definitions that are used throughout the Application. The term “player” is a person who wears the protective sports helmet, is gender neutral and is synonymous with the term “helmet wearer” or “wearer.” The term “designer” is a person who designs, tests, manufactures the helmet.
[0104]The term “anatomical features” can include any one or any combination of the following: (i) dimensions, (ii) topography and/or (iii) contours of the player's body part including, but not limited to, the player's skull, facial region, eye region and jaw region. Because the disclosed helmet is worn on the player's head and the energy attenuation assembly makes contact with the player's hair, the “anatomical features” term also includes the type, amount and volume of the player's hair or lack thereof. For example, some players have long hair, while other players have no hair (i.e., are bald). While the present disclosure, as will be discussed in detail below, is capable of being applied to any body part of an individual but has particular application the human head. Therefore, any reference to a body part is understood to encompass the head and any reference to the head alone is intended to include applicability to any body part. For ease of discussion and illustration, discussion of the prior art and the present disclosure is directed to the human head, by way of example and is not intended to limit the scope of discussion to the human head.
[0105]The term “product region” or “component region” means a volume of the product that has a perimeter that is defined between two volumes of the energy attenuation members that have different mechanical properties.
[0106]The term “optimized helmet prototype model” is a digital or computerized model of a protective helmet that has been altered based upon information that has been gathered from a selected player group, wherein the information may be: (i) body part models and impact matrixes, (ii) only body part models, or (iii) only impact matrixes.
[0107]The term “complete helmet model” is a digital or computerized model of a protective helmet that is derived from an optimized helmet prototype model. In contrast to the optimized helmet prototype model that is not designed to be manufactured, the complete helmet model is designed to be manufactured.
[0108]The term “lattice cell” is the simplest repeating unit contained within the product. It should be understood that various types of lattice cells are contemplated by this disclosure, some of which are shown in FIG. 46. As shown in FIG. 46, some of the lattice cell types are comprised of a number of lattice struts that intersect with one another to form the specific geometry of the lattice cell. While the lattice cell's overall shape may change depending on various variables (e.g., lattice struts thicknesses and lattice struts lengths), the underlying geometry will not change for a given lattice cell. It should further be understood that minor variations in the specific geometry of the lattice cells due to manufacturing tolerances or product configuration will not be considered a new or different type of lattice cell. As will be discussed in great detail below, each product can have a single or multiple types of lattice cells.
[0109]The term “lattice cell region” is a volume of the product that is predominantly composed of one lattice cell type. As discussed above, the lattice struts thickness and/or the lattice struts lengths may change within this lattice region, but only minor variations in the lattice cell's underlying geometry is permitted within one lattice region. It should be understood that if there is more than a minor variation in the lattice cell's underlying geometry, then those lattice cells shall make up a new or second lattice cell region. As will be discussed in great detail below, each product can have a single or multiple lattice cell regions.
[0110]The term “lattice density” is the density a lattice cell, while the term “lattice density region” is a volume of the product that is predominantly composed of one density value. It should be understood that minor variations in the lattice densities due to manufacturing tolerances or a product's configuration will not be considered a new or additional lattice density region. It should be understood that if there is more than a minor variation in the lattice cell's underlying density, then those lattice cells shall make up a new or second lattice density region. As will be discussed in great detail below, each product can have a single or multiple lattice density regions.
[0111]The term “lattice angle” is the angle at which a lattice cell is positioned relative to a normal surface of the product and the term “lattice angle region” is a volume of the product that is predominantly composed of one angle value. It should be understood that minor variations in the lattice angles due to manufacturing tolerances or a product's configuration will not be considered a new or additional lattice angle region. It should be understood that if there is more than a minor variation in the angle of the lattice cell, then those lattice angles shall make up a new or second lattice angle region. As will be discussed in great detail below, each product can have a single or multiple lattice density regions.
[0112]The term “actual stock helmets” or “stock helmets” are helmets that are pre-manufactured helmets that are not specifically designed or bespoke for one player, but instead are designed for a “player group” from amongst a larger population of helmet wearers. Stock helmets provide a number of benefits to the helmet manufacturer, including but not limited to improved efficiencies in manufacturing, raw material usage and inventory management. The term “player group” is a group or subset of players that are part of larger population of players who participate in the sporting activity. In the context of helmets, the player group is a subset of players wearing helmets from amongst the broader group of players wearing helmets. The term “actual stock helmet components” or “stock helmet components” are pre-manufactured components for protective helmets that are not specifically designed for one player, but instead are designed for a defined player group from amongst a larger population of helmet wearers.
B. Introduction/Overview
[0113]As will be explained in greater detail below, the flow chart shown in FIG. 1 shows a multi-step method 1 with a number of processes and sub-processes, which function together to design and manufacture a protective helmet for a selected group of players from amongst a larger population of helmet wearers. This multi-step method 1 starts by collecting information from a population of players in steps 100, 110, wherein this collection of information may include information about the shape of a player's head and information about the impacts the player has received while participating in the sport. This information is collected from numerous players and is then processed in step 120 to create player population information. Next, this information is used to create groups of players (i.e., shape based player data sets) in step 130.2 (see FIG. 16) by sorting the population of players into categories based on at least one characteristic (e.g., player position) of the sport that the population plays. Also in Step 130.2, advanced mathematical techniques (e.g., clustering algorithms) are utilized to further sort these categories into groups (e.g., shape based player data sets) based on attributes of the individual players (e.g., shape of each player's head). Once the shape based player data sets 130.2.2.99a-d, 130.2.4.99a-d (see FIGS. 17a-17d and 19a-19d) are identified in step 130.2 (see FIG. 16), a multi-step method is utilized to design optimized helmet prototype model 130.28.2.99, 130.28.4.99, 130.28.6.99 (collectively, 130.28.99) for each group (see FIGS. 43b, 44, 45). These optimized helmet prototype models 130.28.99 are then transformed into a complete helmet models 140.12.99 (see FIG. 51) in step 140 (see FIG. 16) by determining a structural design and chemical composition that is manufacturable and has mechanical properties that are substantially similar to the optimized helmet prototype model 130.28.99. Next, physical helmet prototypes 1000 are created in step 150 (see FIG. 16) from the complete helmet models 140.12.99 using advanced manufacturing techniques (e.g., additive manufacturing). Each of the physical helmet prototypes 1000 are tested using a unique helmet standard 130.8.99, 130.26.99 (see FIGS. 16, 24) that were derived from information associated with each group of players. Once the physical helmet prototypes 1000 pass their unique helmet standard 130.8.99, 130.26.99, the complete helmet models 140.12.99 can be mass manufactured to create the stock helmets 166a or helmet components 166b for future players whose characteristics and attributes place them within the selected group.
[0114]The collection of information in steps 100, 110 includes collecting information about each player's level, player's position, information about the impacts the player receives while engaged in the contact sport and information about the shape of the player's head. Specifically, information about the impacts the player receives while playing the contact sport may be collected using a plurality of sensors 100.2.4.4a-e that are contained within the player's helmet and are specifically designed to analyze and record impact information. In addition, information about the shape of the player's head may be collected using a scanning apparatus 110.4.2. Once the above information is collected, operations are performed to prepare this information for further analysis. As shown in FIG. 11, said operations may include: (i) removal of information in steps 120.4-120.8, 120.52-120.56, (ii) creating models from this information in steps 120.58, (iii) refining the models in steps 120.60-120.62, (iv) aligning the models in steps 120.66, and (v) removing surface data from the models that is not relevant to the fitting of the helmet in steps 120.68.
[0115]The multi-step method of designing optimized helmet prototype models 130.28.99 (see FIGS. 43b, 44, 45) includes generation digital headform prototypes 130.12.99 (see FIG. 26) based upon a generic digital headform 130.10.99 (see FIG. 25) and the mean head shapes 130.8.99.2 (see FIG. 23) from each group of players that are contained within shape based player data sets 130.2.2.99a-d, 130.2.4.99a-d, 130.2.6 (collectively, 130.2.99, which are shown in FIGS. 16, 17a-17d, 19a-19d). These digital headform prototypes 130.12.99 will then be utilized to modify generic digital helmets 130.14.99 (see FIG. 27) in order to create specific helmets for each data set 130.16.99 (see FIG. 28b). In particular, [[a]] each data set specific helmet 130.16.99 is created for each data set 130.2.99. Each data set specific helmet 130.16.99 is then optimized based upon associated impact information, wherein the impact information is derived from impacts that are received by the players that are contained within each shape+impact based player or “HS+IBP” data sets 130.22.2.99, 130.22.4.99 (collectively, 130.22.99, see FIGS. 41-42). Finally, this optimized helmet prototype model 130.28.99 is compared against various unique helmet standards 130.8.99, 130.26.99 to ensure that it complies with these standards.
[0116]The optimized helmet prototype models 130.28.99 are then transformed into the complete helmet models 140.12.99 (see FIG. 51) to enable a designer to manufacture the optimized helmet prototype models 130.28.99. Each complete helmet model 140.12.99 may have various portions, which have different mechanical properties. Specifically, the mechanical properties of the energy attenuation assembly contained within one of the complete helmet models 140.12.99 may be configured such that: (i) one member in the energy attenuation assembly has different mechanical properties in comparison to all other members, (ii) one region contained within the energy attenuation assembly may have different mechanical properties in comparison to all other regions, or (iii) multiple regions contained within a single member may have different mechanical properties in comparison to each other. To create differing mechanical properties, the structural design and chemical composition of the energy attenuation assembly are altered. Alterations to the structural design may include changes to: (i) lattice cell type, (ii) lattice angle, or (iii) lattice density. In an exemplary embodiment, a rear combination energy attenuation member that was created using an additive manufacturing process may contain at least four different regions that have different mechanical properties.
[0117]Physical helmet prototypes 1000 are created in step 150 from the complete helmet models 140.12.99 using advanced manufacturing techniques. Examples of such advanced manufacturing techniques include additive manufacturing technologies, such as VAT photopolymerization, powder bed fusion, binder jetting, material jetting, sheet lamination, material extrusion, directed energy deposition, or a hybrid of these technologies. Once the physical helmet prototypes 1000 are created, each of the physical helmet prototypes 1000 are tested using a unique helmet standard 130.8.99, 130.26.99 that were derived from information associated with each group of players. Once the physical helmet prototypes 1000 pass their unique helmet standard 130.8.99, 130.26.99, the complete helmet models 140.12.99 can be mass manufactured to create the stock helmets 166a or helmet components 166b for future players whose characteristics and attributes place them within the selected group.
[0118]In addition to applying to a football player, hockey player, lacrosse player, the disclosure contained herein may be applied to helmets for: baseball player, cyclist, polo player, equestrian rider, rock climber, auto racer, motorcycle rider, motocross racer, skier, skater, ice skater, snowboarder, snow skier and other snow or water athletes, skydiver, boxing, sparring, wrestling, and water polo or any other athlete in a sport. Other industries also use protective headwear, such as construction, soldier, firefighter, pilot, other military person, or other workers in need of a safety helmet, where similar technologies and methods may also be applied. The method, system, and devices described herein may be applicable to other body parts (e.g., shins, knees, hips, chest, shoulders, elbows, feet and wrists) and corresponding gear or clothing (e.g., shoes, shoulder pads, elbow pads, wrist pads).
C. Collecting Information
[0119]This multi-step method starts by collecting information in steps 100, 110, which may include information about the shape of a player's head and the impacts the player receives while participating in the sport.
1. Collecting Impact Information
[0120]Referring to FIG. 1, steps 100, 300 describe acquiring information about impacts the players experience while participating in an activity (e.g., playing a football game). One example of a method of collecting this impact information is described within FIGS. 2a-2b. In step 100.2, 200.2, an impact sensor system is utilized to carry out the steps in the method shown in FIGS. 2a-2b. FIG. 3 illustrates an exemplary system 100.2, 300.2 that includes: (i) helmets 1000 that each have an in-helmet unit (IHU) 100.2.4, 300.2.4, (ii) a receiving device 100.2.6, 300.2.6, which in this embodiment may be an alerting unit 100.2.6.2, 300.2.6.2, (iii) a remote terminal 100.2.8, 300.2.8, (iv) a team database 100.2.10, 300.2.10, and (iv) a national database 100.2.12, 300.2.12. The IHU 100.2.4, 300.2.4 may be specifically designed and programmed to: (i) measure and record impact information, (ii) analyze the recorded information using the algorithm shown in FIGS. 2a-2b, and (iii) depending on the outcome of the algorithm shown in FIGS. 2a-2b, transmit the recorded information to a receiving device 100.2.6, 300.2.6 that is remote from the IHU 100.2.4, 300.2.4.
[0121]FIG. 4 illustrates an exemplary schematic of the IHU 100.2.4, 300.2.4. As shown, the control module 100.2.4.2, 300.2.4.2 is connected to each sensor 100.2.4.4a-e, 300.2.4.4a-e via separate leads 100.2.4.6a-e, 300.2.4.6a-e. The five distinct sensors 100.2.4.4a-e, 300.2.4.4a-e may be placed at the following locations on a player's head: top, left, right, front, and back. The control module 100.2.4.2, 300.2.4.2 includes a signal conditioner 100.2.4.8, 300.2.4.8, a filter 100.2.4.10, 300.2.4.10, a microcontroller or microprocessor 100.2.4.12, 300.2.4.12, a telemetry element 100.2.4.14, 300.2.4.14, an encoder 100.2.4.16, 300.2.4.16, and a power source 100.2.4.18, 300.2.4.18. The control module 100.2.4.2, 300.2.4.2 includes a shake sensor 100.2.4.20, 300.2.4.20 that may be used to turn the IHU 100.2.4, 300.2.4 ON or OFF based on a specific shake pattern of the player helmet 20. Alternatively, the IHU 100.2.4, 300.2.4 may have control buttons, such as a power button and a configuration button, for example. Additional information about the positioning and configuration of the IHU 100.2.4, 300.2.4 is described within U.S. Pat. No. 10,105,076 and U.S. Provisional Application 62/364,629, both of which are fully incorporated herein by reference.
[0122]Returning to FIG. 2a, the IHU 100.2.4, 300.2.4 continually monitors for a value from any sensor 100.2.4.4a-e, 300.2.4.4a-e that exceeds a predetermined noise threshold, which is programmed into the IHU 100.2.4, 300.2.4. As shown in step 100.4, 300.4, once the IHU 100.2.4, 300.2.4 determines that a sensor 100.2.4.4a-e, 300.2.4.4a-e has recorded a value that is greater than the predetermined noise threshold, then an impact has been detected. The microcontroller 100.2.4.12, 300.2.4.12 wakes up to record information from all sensors 100.2.4.4a-e, 300.2.4.4a-e and perform both algorithms shown in FIG. 2a-2b. The first algorithm or head impact exposure (HIE) algorithm 100.10, 300.10 does not weight the impact magnitude value based on the location of the impact, while the second algorithm or alert algorithm 100.50, 300.50 weights the impact magnitude value based on the location of the impact. The first algorithm or HIE algorithm 100.10, 300.10 compares the impact magnitude value to a 1st threshold or an impact matrix threshold in step 100.10.2, 300.10.2. The 1st threshold or an impact matrix threshold is set between 1 g and 80 gs and preferably between 5 gs and 30 gs. If the impact magnitude value is less than the impact matrix threshold, than the microcontroller 100.2.4.12, 300.2.4.12 will disregard the impact magnitude value shown in step 100.10.10, 300.10.10. However, if the impact magnitude value is greater than the impact matrix threshold, than the microcontroller 100.2.4.12, 300.2.4.12 will add the impact magnitude value to the impact matrix in step 100.10.4, 300.10.4.
[0123]An exemplary player impact matrix 120.2.75, 320.2.75 is shown in FIG. 12. Specifically, the exemplary impact matrix 120.2.75, 320.2.75 is comprised of 5 columns and 7 rows, where the 5 columns correspond to the location of the impact on the player's head (e.g., front, back, left, right, and top) and the 7 rows correspond to the severity of the impact (e.g., 1st, 2nd, 3rd, 4th, 5th severity, single impact alert, or cumulative impact alert). Each of these severity values (e.g., 1st, 2nd, 3rd, 4th or 5th) correspond to a range of impact magnitude values. For example, the 1st range may include impact magnitude values between the impact matrix threshold and the 50th percentile of historical impact magnitude values for players of similar position and playing level. The 2nd range may include impact magnitude values between the 51st percentile and the 65th percentile of historical impact magnitude values for players of similar position and playing level. The 3rd range may include impact magnitude values between the 66th percentile and the 85th percentile of historical impact magnitude values for players of similar position and playing level. The 4th range may include impact magnitude values between the 86th percentile and the 95th percentile of historical impact magnitude values for players of similar position and playing level. The 5th range may include impact magnitude values above the 95th percentile of historical impact magnitude values for players of similar position and playing level. The single impact alerts and the cumulative impact alerts are based upon a second algorithm or alert algorithm 100.50, 300.50. It should be understood that these percentile ranges are based on historical impact magnitude values that have been collected using the proprietary technologies owned by the assignee of the present Application and are disclosed in U.S. Pat. Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768, each of which is fully incorporated by reference herein. It should be understood that these values may be updated in light of additional impact information that has been collected by this system or other similar systems.
[0124]Returning to FIG. 2a, once the microcontroller 100.2.4.12, 300.2.4.12 has added the impact magnitude value to the impact matrix in step 100.10.4, 300.10.4, the microcontroller 100.2.4.12, 300.2.4.12 determines if a 1st predefined amount of time or an impact matrix transmit time period has passed from the time the IHU 100.2.4, 300.2.4 last transmitted the impact matrix to a receiving device 100.2.6, 300.2.6. The impact matrix transmit time period may be set to any time, preferably it is set between one second and 90 days and most preferably between 30 seconds and 1 hour. If the amount of time that has passed since the unit last transmitted the impact matrix to a receiving device 100.2.6, 300.2.6 is less than the impact matrix transmit time period, then the microcontroller 100.2.4.12, 300.2.4.12 will perform no additional steps, as shown in step 100.10.10, 300.10.10. However, if the amount of time that has passed since the unit last transmitted the impact matrix to a receiving device 100.2.6, 300.2.6 is greater than the impact matrix transmit time period, then the control module 100.2.4.2, 300.2.4.2 of the IHU 100.2.4, 300.2.4 will transmit the impact matrix from the IHU 100.2.4, 300.2.4 to a receiving device 100.2.6, 300.2.6 (e.g., an alert unit 100.2.6.2, 300.2.6.2) in step 536. Upon the completion of this decision, the IHU 100.2.4, 300.2.4 has finished performing the HIE algorithm 100.10, 300.10.
[0125]While the IHU 100.2.4, 300.2.4 is performing the HIE algorithm 100.10, 300.10, the IHU 100.2.4, 300.2.4 is also performing the alert algorithm 100.50, 300.50 shown in FIG. 2b. Referring to FIG. 2b, the microcontroller 100.2.4.12, 300.2.4.12 will calculate an impact value in step 100.50.2, 300.50.2. In one embodiment, this is done by first determining the linear acceleration, rotational acceleration, head injury criterion (HIC), and the Gadd severity index (GSI) for the given impact. The algorithms used to calculate these values are described in Crisco J J, et al. An Algorithm for Estimating Acceleration Magnitude and Impact Location Using Multiple Nonorthogonal Single-Axis Accelerometers. J BioMech Eng. 2004; 126 (1), Duma S M, et al. Analysis of Real-time Head Accelerations in Collegiate Football Players. Clin J Sport Med. 2005; 15(1):3-8, Brolinson, P. G., et al. Analysis of Linear Head Accelerations from Collegiate Football Impacts. Current Sports Medicine Reports, vol. 5, no. 1,2006, pp. 23-28, and Greenwald R M, et al. Head impact severity measures for evaluating mild traumatic brain injury risk exposure. Neurosurgery. 2008; 62(4):789-798, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. Once the linear acceleration, rotational acceleration, head injury criterion (HIC), and the Gadd severity index (GSI) are calculated for a given impact, these scores are weighted according to the algorithm set forth in Greenwald R M, et al. Head impact severity measures for evaluating mild traumatic brain injury risk exposure. Neurosurgery. 2008; 62(4):789-798, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. This resulting weighted value is a HITsp value for the given impact, which will be the calculated impact value in this first embodiment. While not diagnostic of injury, HITsp has been shown to be more sensitive and specific to diagnose concussions than any of the component measures alone. Specifically, HITsp has been shown to be 50% more sensitive to predict a subsequently diagnosed concussion than the usage of any individual measure by itself (e.g., linear acceleration).
[0126]In another embodiment, the calculated impact value may be equal to the linear acceleration for the given impact. In a further embodiment, the calculated impact value may be equal to the HIC score for the given impact. In another embodiment, the calculated impact value may be equal to the rotational acceleration for a given impact. In another embodiment, the impact value may be equal to the linear acceleration weighted by a combination of impact location and impact duration. In another embodiment, the impact value may be equal to the weighted combination of linear acceleration, rotational acceleration, HIC, GSI, impact location, impact duration, impact direction. In another embodiment, the impact value may be equal to a value that is determined by a learning algorithm that is taught using historical information and diagnosed injuries. In even a further embodiment, the impact value may be equal to any combination of the above.
[0127]Referring to FIG. 2b, once the impact value is calculated in step 100.50.2, 300.50.2 by the microcontroller 100.2.4.12, 300.2.4.12, the impact value is compared against a 2nd threshold or high magnitude impact threshold in step 100.50.4, 300.50.4. This high magnitude impact threshold may be set to the 95th percentile for impacts recorded by players of similar playing level (e.g., youth, high school, college and professional players) and similar position (e.g., offensive line, running backs, quarterback, wide receivers, defensive linemen, linebackers, defensive backs and special teams). If the impact value is less than the high magnitude impact threshold, than the microcontroller 100.2.4.12, 300.2.4.12 will not perform any additional operations, as shown in step 100.50.6, 300.50.6. However, if the impact value is greater than the high magnitude impact threshold, than the impact value will be added to the cumulative impact value in step 100.50.6, 300.50.6 and compared against a 3rd threshold or single impact alert threshold in step 100.50.18, 300.50.18. This single impact alert threshold may be set to the 99th percentile for impacts recorded by players of similar playing level and position. It should be understood that all percentiles (e.g., 95th and 99th) contained in this application are based on historical impact magnitude values that have been collected using the proprietary technologies owned by the assignee of the present Application and are disclosed in U.S. Pat. Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768, each of which is fully incorporated by reference herein. However, it should be understood that these percentiles may be updated in light of additional impact information that has been collected by this system or other systems.
[0128]Referring to FIG. 2b, if the impact value is greater than the single impact alert threshold, the control module 100.2.4.2, 300.2.4.2 transmits alert information that is associated with the single impact alert to the receiving device 100.2.6, 300.2.6 (e.g., an alert unit 100.2.6.2, 300.2.6.2) in step 100.50.22, 300.50.22. The alert information may include, but is not limited to: (i) the impact value (e.g., graphical or non-graphical display of the magnitude of the impact), (ii) impact location (e.g., graphical or non-graphical), (iii) impact time, (iv) impact direction, (v) player's unique identifier, (vi) alert type, (vii) player's heart rate, (viii) player's temperature and (ix) other relevant information. If the impact value is less than the single impact alert threshold, the microcontroller 100.2.4.12, 300.2.4.12 will not perform any additional steps 100.50.20, 300.50.20 along this path of the algorithm 100.50, 300.50.
[0129]While the microcontroller 100.2.4.12, 300.2.4.12 is determining whether the impact value is greater than the single impact alert threshold in step 100.50.18, 300.50.18, the microcontroller 100.2.4.12, 300.2.4.12 also calculates a weighted cumulative impact value that includes this new impact value, in step 100.50.10, 300.50.10 shown in FIG. 2b. Specifically, the weighted cumulative impact value is calculated based on a weighted average of every relevant impact value that is over a 2nd threshold or high magnitude impact threshold. To determine this weighted average, every impact value that is over a 2nd threshold is weighted by a decaying factor. For example, an impact that was recorded 4 days ago maybe multiplied by 0.4 decaying factor, thereby reducing the magnitude level of this impact. After the weighted impact values are determined, these values are summed together to generate the weighted cumulative impact value. It should be understood that the microcontroller 100.2.4.12, 300.2.4.12 will exclude irrelevant impact values that are old enough to cause their weighted impact value to be zero due to the decaying factor. For example, if the decaying factor for an impact that is over 7 days old is 0; then regardless of the impact value, this impact is irrelevant to this calculation and will not be included within this calculation. One skilled in the art recognizes that weighting variables (e.g., time window, decay function, input threshold) are adjustable.
[0130]Once the weighted cumulative impact value has been calculated in step 100.50.10, 300.50.10 in FIG. 2b, this value is compared against a 4th threshold or a cumulative impact alert threshold in step 100.50.12, 300.50.12. This cumulative impact alert threshold may be set to the 95th percentile for weighted cumulative impact values recorded by players of similar playing level and position. If the weighted cumulative impact value is less than the cumulative impact alert threshold, than the microcontroller 100.2.4.12, 300.2.4.12 will not perform any additional steps 100.50.16, 300.50.16. However, if the weighted cumulative impact value is greater than the cumulative impact value threshold, the control module 100.2.4.2, 300.2.4.2 of the IHU 100.2.4, 300.2.4 transmits alert information that is associated with a cumulative impact alert to the receiving device 100.2.6, 300.2.6 (e.g., an alert unit 100.2.6.2, 300.2.6.2) in step 100.50.14, 300.50.14. As discussed above, the alert information may include, but is not limited to: (i) the impact value (e.g., graphical or non-graphical display of the magnitude of the impact), (ii) impact location (e.g., graphical or non-graphical), (iii) impact time, (iv) impact direction, (v) player's unique identifier, (vi) alert type, (vi