TRAINING SYSTEM

A training system for using a firearm is provided. The training system has a training region having fixed or movable targets, a firearm, user detection groups for detecting user physical status data, firearm detection groups for detecting firearm status data, and target detectors for detecting target data. A data management and analysis unit, operatively connected to the user detection groups, to the firearm detection groups and to the target detectors, is suitable for receiving the user physical status data, the firearm status data and the target data, and is configured to create a virtual training model as a function of set/stored user features, as a function of set/stored environmental features, as a function of set/stored training session features, and as a function of expected results, to compare the user physical status data, the firearm status data and the target data with the virtual training model.

The present invention relates to a training system for using a firearm. Furthermore, the present invention relates to a training method using said system.

In particular, but not in a limiting manner, the present invention pertains to the defense sector.

It is clearly of importance to be as trained as possible in the use of a firearm. In fact, the use of a firearm contextualizes a situation of potential danger both for those who use it and for those who are in proximity to the user. More training corresponds to more limited and contained potential danger. It is only practice and constant training that confers to the user of the firearm the experience necessary in order to exploit it effectively and to use it safely.

It is therefore known in the prior art to find solutions to train, or coach, people and/or law enforcement agencies and/or the military in the use of firearms.

The more training, or coaching, takes place, under conditions that are similar to actual conditions, the greater the preparation and benefit derived by the user and those around them, limiting as much as possible a potentially hazardous situation.

In the prior art, training sessions are given by instructors who monitor the performance of users/students, detecting and verifying any situations to be improved.

At the same time, with known training systems, in addition to the feedback of the instructor, during a training session the user experiences personal feelings and has feedback when it hits, or misses, a target.

Such training systems, therefore, present a multiplicity of problems.

First of all, the attention of the instructor must always be maximum and must always be directed at the user. It is therefore not uncommon for an instructor to miss certain events and not be able to give complete feedback. This problem is also exacerbated in group training sessions and/or training sessions that take place in training regions with extensive metrics.

Secondly, the user experiences sensations that remain only in their head and that are difficultly transmissible and/or are used as a common factor.

The need to provide a training system that solves these problems is therefore strongly felt turning out to be extremely useful and versatile for both users and instructors.

The object of the present invention is to provide a training system that solves said need.

Such object is achieved by means of the training system claimed in claim1. Similarly, such object is achieved by means of a training method according to claim14. The claims dependent thereon describe preferred variant embodiments involving further advantageous aspects.

With reference to the accompanying figures, with the reference number1is indicated a training system1for using a firearm according to this invention.

The training system1comprises a training region2, in which training sessions are performable.

The training region2is such that the user is free to perform movements, displacements, and similar.

The training region2comprises fixed or movable targets20.

Furthermore, the training region2comprises training region status sensors29that are suitable for detecting region environmental conditions, e. g., temperature, humidity, wind, and similar.

According to a preferred embodiment, the training region2is an actual outdoor area habitable by one or more users.

According to a preferred embodiment, the training region2is an area virtualized by a user. Preferably, in such a preferred embodiment, the targets are virtualized.

In addition, the training system1comprises a firearm3, which is wieldable by the user.

By means of such a firearm3, the user has to hit the targets20provided in the training region2.

According to a preferred embodiment, the firearm3is a real firearm.

According to a further preferred embodiment, the firearm3is a simulacrum firearm.

Depending on the type of training region2, the type of firearm3to be used also varies.

According to the present invention, the training system1comprises user detection means4suitable for detecting user physical status data during a training session.

According to a preferred embodiment, the user detection means4comprise a cardiac status detection group, preferably suitable for detecting the user's heartbeat and/or electrocardiogram.

According to a preferred embodiment, the user detection means4comprise a sweat detection group, preferably suitable for detecting the galvanic response of the user's skin (GSR).

According to a preferred embodiment, the user detection means4comprise a respiratory status detection group, preferably suitable for detecting the respiratory rate.

According to a preferred embodiment, the user detection means4comprise a limb status detection group, preferably suitable for performing a myography of the limbs and of the main muscles of the limbs.

According to a preferred embodiment, the user detection means4comprise a position detection group, preferably suitable for detecting the user's position in the training region2.

According to a preferred embodiment, the user detection means4comprise a user activity detection group, preferably suitable for detecting the user's speed and/or acceleration and/or angular speed in the training region2.

According to a preferred embodiment, the user detection means4are positioned on the user, e.g., worn by the user.

According to a preferred embodiment, the user detection means4are positioned remotely with respect to the user, e.g., positioned in the training region2.

According to a preferred embodiment, the user detection means4are positioned both on the user and in the training region2.

According to the invention, the training system1comprises firearm detection means5suitable for detecting firearm status data.

According to a preferred embodiment, the training system1the firearm detection means5comprise a firearm status detection group, preferably suitable for detecting the operational status of the firearm, for example the presence thereof in the holster, or the aiming thereof.

According to a preferred embodiment, the training system1the firearm detection means5comprise a firearm configuration detection group, preferably suitable for detecting the safe configuration thereof, or the configuration thereof in semi-automatic or automatic mode, or the armed configuration thereof.

According to a preferred embodiment, the training system1the firearm detection means5comprise a firearm grip detection group, preferably suitable for detecting the modes in which the firearm is gripped by the user.

According to a preferred embodiment, the training system1the firearm detection means5comprise a trigger guard engagement detection group, preferably suitable for detecting the presence of the user's finger in the firearm's trigger guard.

According to a preferred embodiment, the training system1the firearm detection means5comprise a shooting and shooting mode detection group, preferably suitable for detecting the shooting of the firearm and/or the modes, e.g., the actuation on the trigger performed by the user.

According to a preferred embodiment, the training system1the firearm detection means5comprise a firearm activity detection group, preferably suitable for detecting speed and/or acceleration and/or angular speed of the firearm3in the training region2.

According to a preferred embodiment, the firearm detection means5are positioned on the firearm.

According to a preferred embodiment, the firearm detection means5are positioned remotely in respect to the firearm, for example positioned in the training region2.

According to a preferred embodiment, the firearm detection means5are positioned both on the firearm and in the training region2.

According to the present invention, the training system1comprises target detection means6suitable for detecting target data.

According to a preferred embodiment, the target detection means6are suitable for detecting whether and how a shot performed with the firearm3hit the target20.

According to a preferred embodiment, the target detection means6are positioned on the user.

According to a preferred embodiment, the target detection means6are positioned on the firearm.

According to a preferred embodiment, the target detection means6are positioned in the training region2positioned on the target20.

According to a preferred embodiment, the target detection means6are positioned on the user, on the firearm, and in the training region.

According to a preferred embodiment, the training system1comprises devices wearable by the user7, comprising haptic devices70suitable for producing haptic signals on the user.

Preferably, said haptic devices70are suitable for simulating, for example by means of an electrical signal or a vibratory signal, the recoil and/or noise of a shot. Preferably, such a solution is usable for simulating determined behaviors typical of a real firearm in a situation wherein a simulacrum firearm is used.

According to the present invention, the training system1comprises a data management and analysis unit9.

The data management and analysis unit9is operatively connected to the user detection means4, to the firearm detection means5and to the target detection means6and is therefore suitable for receiving the user physical status data, the firearm status data and the target data. That is to say, the data management and analysis unit9receives all data detected by the user detection means4and therefore receives all information relating to the physical state of the user, for example, heart status, speed, etc. That is to say that the data management and analysis unit9receives all of the data detected by the firearm detection means5and therefore receives all of the information relating to the status of the firearm, for example the grip, inclination, etc. That is to say that the data management and analysis unit9receives all of the data detected by the target detection means6and therefore receives all of the information relating to the targets, for example target hit, target missed, etc.

According to a preferred embodiment, the data management and analysis unit9is operatively connected to the user detection means4by means of physical connections and/or wiring.

According to a preferred embodiment, the data management and analysis unit9is operatively connected to the user detection means4by means of wireless connections.

According to a preferred embodiment, the data management and analysis unit9is operatively connected to the firearm detection means5by means of physical connections and/or wiring.

According to a preferred embodiment, the data management and analysis unit9is operatively connected to the firearm detection means5by means of wireless connections.

According to a preferred embodiment, the data management and analysis unit9is operatively connected to the target detection means6by means of physical connections and/or wiring.

According to a preferred embodiment, the data management and analysis unit9is operatively connected to the target detection means6by means of wireless connections.

According to a preferred embodiment, the data management and analysis unit9comprises a memory99in which all of the data are storable.

According to the invention, the data management and analysis unit9is configured to create a virtual training model as a function of set/stored user features, for example coming from theoretical models resulting from scientific studies, as a function of set/stored environmental features, as a function of set/stored training session features, and as a function of the expected results, for comparing the user physical status data, firearm status data and target data with the virtual training model.

In other words, the data management and analysis unit9creates a virtual training model starting from of the data received. The virtual training model presents all the information relating to the user, the firearm and the training region.

According to the present invention, the virtual training model is compared with the detected data: as a function of said received data and said comparison between the model and the received data, the result of the training session is determined and any eventual points to be improved in order to improve the results are identified.

For example, it is determined whether the physical status of the user should or should not be improved.

For example, it is determined whether the use of the firearm should or should not be improved.

For example, it is determined whether the aim should or should not be improved.

In this way, any eventual user deficiencies are highlighted and specific ad hoc training sessions may be provided, aimed at improving such determined deficiencies.

According to a preferred embodiment, the data management and analysis unit9for each training session collects the user physical status data, the firearm status data and the target data, and collects the data in a memory.

Preferably, the data management and analysis unit9updates the set/stored user features, the set/stored environmental features, and the set/stored training session features and updates the virtual training model in order to compare new user physical status data, firearm status data and target data collected with the updated virtual training model.

According to a preferred embodiment, the data management and analysis unit9updates the virtual training model between one training session and another.

According to a preferred embodiment, the data management and analysis unit9updates the virtual training model during a training session.

According to a preferred embodiment, the data management and analysis unit9sets a training session in function of the initial physical user status data.

For example, the data management and analysis unit9sets a training session for a young user with certain targets, while sets a training session for an older user with different targets.

According to a preferred embodiment, the data management and analysis unit9sets a training session based on the expected results.

For example, the data management and analysis unit9sets a training session with a multiplicity of targets with the aim of improving the results of a user.

According to a preferred embodiment, the data management and analysis unit9is for example a computer, a tablet, a smartphone, or a workstation.

According to a preferred embodiment, the data management and analysis unit9is connectable to a further external electronic device, for example a computer, a tablet, a smartphone, wherein, through said external device10, the data in the data management and analysis unit9is accessible, modifiable and integrable, for modifying the virtual training model.

According to a further preferred embodiment, the data management and analysis unit9is the external electronic device10itself.

According to a preferred embodiment, the firearm3is a simulacrum firearm, wherein the training system1comprises devices wearable by the user7, comprising haptic devices70suitable for producing haptic signals on the user70, wherein said device wearable by the user7is operatively connected to the data management and analysis unit9, wherein the data management and analysis unit9controls the actuation of the haptic devices70.

That is to say, the training system1simulates a situation as real as possible in function of what is required.

According to a preferred embodiment, the training region2comprises training region status sensors29suitable for detecting region environmental conditions, e.g., temperature, humidity, wind, and similar.

Preferably, the data management and analysis unit9updates the virtual training model as a function of what is detected by the training region status sensors29.

As mentioned, it is an object of the present invention also a training method for using a firearm by means of a training system1having the characteristics described above.

According to the present invention, the method comprises the steps of:detecting the user physical status data, firearm status data and target data;creating a virtual training model as a function of set/stored user features, e.g., coming from theoretical models resulting from scientific studies, as a function of set/stored environmental features, as a function of set/stored training session features and as a function of the expected results;comparing the user physical status data, the firearm status data and the target data with the virtual training model.

Furthermore, according to a preferred embodiment, the training method further comprises the step of:collecting the user physical status data, the firearm status data and the target data;updating the set/stored user features, the set/stored environmental features, and the set/stored training session features;updating the virtual training model to compare new collected user physical status data, firearm status data and target data with the updated virtual training model.

According to a preferred embodiment, the step of updating the virtual training model is performed between one training session and the other.

According to a preferred embodiment, the step of updating the virtual training model is performed during a training session, in real time.

Innovatively, the training system and the training method largely fulfill the purpose of the present invention in overcoming the problems that are typical of the prior art.

Advantageously, in fact, the training system and the training method are suitable for performing effective training for users, as well as for giving clear support to the activity of instructors.

Advantageously, both the user and the instructor obtain complete data regarding a training session. Advantageously, the user, but also the same instructor, both obtain a complete comparison with the virtual training model.

Advantageously, the strengths and weaknesses of the user are highlighted during an entire training session.

Advantageously, the training system has also predictive character, predicting the results that are expected from a user compared to a desired training situation, for example simulating a certain type of mission.

Advantageously, the training system has also certifying character: a training session is classifiable as “passed”/“not passed” in function of the results achieved by the user with respect to the expected results.

Advantageously, the virtual training model is suitable for predicting a series of user/firearm/training region situations.

Advantageously, the training system recognizes and manages different situations.

Advantageously, the training system is settable in function of the features, such as the age or experience, of the user.

Advantageously, the training system is settable in function of the features, such as the type or caliber, of the firearm.

Advantageously, the training system is settable in function of the features, such as the temperature or wind, of the training region.

Advantageously, the training region may be small in size and may also be performed indoors.

Advantageously, the training is virtualizable.

Advantageously, the training is performable in groups, presenting real people in the same training region or presenting within a virtual reality people using, remotely, different training systems.

It is clear that a person skilled in the art, in order to satisfy contingent needs, could make modifications to the training system described above, all being contained within the scope of protection as defined in the following claims.