Featured are impact detection sensors that include a nonconductive layer disposed between two conductive layers. Each conductive layer includes an electrical circuit configured to generate a signal in response to an impact. Also featured are devices (e.g., a wearable device or device configured for use with a piece of equipment, such as a vehicle) including a plurality of impact detection sensors. The device having sensors can receive and process data from the sensors and provide situational awareness for users in adverse conditions, such as during combat or wartime. The wearable device may further include one or more inflatable bladders configured to apply pressure to a wound site for treatment.

A tactical torso tool carrier and networked control and communication system is disclosed, wherein a tactical torso tool carrier device includes a main unit configured to be worn on the torso of a person, the main unit including a housing having a docking station configured to securely attach a removable tool module, wherein the tactical torso tool carrier device facilitates communications between personnel and systems operably associated with the tactical toro tool carrier and networked control and communication system.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a body-worn safety device includes a body-worn camera configured to capture a video of an incident from a perspective of a wearer of the body-worn camera. In this embodiment, a microphone is configured to capture audio concurrently with the video. A processing unit includes an artificial intelligence module in this embodiment. The artificial intelligence module is configured to respond to a voice command from the wearer by analyzing the captured audio, the captured video, and/or an external data of the artificial intelligence module. In another embodiment, a method of a wearable safety system provides an audible answer and a guidance in real-time using the natural language queries; and outputting an audio response and an alert to the wearer.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a body-worn safety device includes a body-worn camera configured to capture a video of an incident from a perspective of a wearer of the body-worn camera. In this embodiment, a microphone is configured to capture audio concurrently with the video. A processing unit includes an artificial intelligence module in this embodiment. The artificial intelligence module is configured to respond to a voice command from the wearer by analyzing the captured audio, the captured video, and/or an external data of the artificial intelligence module. In another embodiment, a method of a wearable safety system provides an audible answer and a guidance in real-time using the natural language queries; and outputting an audio response and an alert to the wearer.

Disclosed are a method, system, and apparatus of a rapidly expandable anti-drone mesh system and method. In one aspect, a system includes a protected asset, an armored mesh on the protected asset, a sensor system to detect a hostile drone to imminently collide with the protected asset in an impact zone, and a responsive mechanism of the protected asset to cause a high-pressure gas to rapidly expand the armored mesh in the impact zone of the protected asset when the sensor system detects the hostile drone to imminently collide with the protected asset. The armored mesh preserves the protected asset by causing the hostile drone to impact the armored mesh instead of directly the protected asset. The protected asset may be a building, a vehicle, a wearer of a tactical gear, a civilian, a soldier, a boat, a tank, a weaponry, an airplane and/or an infrastructure.

Disclosed are a method, system, and apparatus of a rapidly expandable anti-drone mesh system and method. In one aspect, a system includes a protected asset, an armored mesh on the protected asset, a sensor system to detect a hostile drone to imminently collide with the protected asset in an impact zone, and a responsive mechanism of the protected asset to cause a high-pressure gas to rapidly expand the armored mesh in the impact zone of the protected asset when the sensor system detects the hostile drone to imminently collide with the protected asset. The armored mesh preserves the protected asset by causing the hostile drone to impact the armored mesh instead of directly the protected asset. The protected asset may be a building, a vehicle, a wearer of a tactical gear, a civilian, a soldier, a boat, a tank, a weaponry, an airplane and/or an infrastructure.

A method, apparatus, and system of automated code enforcement monitoring using geospatially tagged video capture is disclosed. In one embodiment, a data acquisition device is provided comprising at least one of a body-worn camera worn by a code enforcement officer, a vehicle-mounted camera, or a drone deployed from the vehicle. The device captures video data of real property together with geospatial coordinates and timestamps within a jurisdictional boundary. An evidence management server is communicatively coupled to the device through a network to store the video data, coordinates, and timestamps. The server identifies a parcel number associated with the captured property based on geospatial coordinates. A violation detection module compares timestamped video data of the parcel with previously captured data to determine modifications to a physical structure or landscaping, thereby identifying potential violations of jurisdictional codes.

A method, apparatus, and system of automated code enforcement monitoring using geospatially tagged video capture is disclosed. In one embodiment, a data acquisition device is provided comprising at least one of a body-worn camera worn by a code enforcement officer, a vehicle-mounted camera, or a drone deployed from the vehicle. The device captures video data of real property together with geospatial coordinates and timestamps within a jurisdictional boundary. An evidence management server is communicatively coupled to the device through a network to store the video data, coordinates, and timestamps. The server identifies a parcel number associated with the captured property based on geospatial coordinates. A violation detection module compares timestamped video data of the parcel with previously captured data to determine modifications to a physical structure or landscaping, thereby identifying potential violations of jurisdictional codes.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a cloud-based data management system includes an artificial intelligence module to generate a transcript of an incident using data captured from a body-worn safety device (e.g., audio or video data) that captures an incident surrounding a first responder. The transcription is highly accurate, even in noisy environments, and discerns different speakers, making it valuable for documenting interactions and statements. The transcribed text is analyzed with a private Large Language Model (LLM), to interpret at least one of the audio and video data, and to provide insights, summaries, and/or flag potential areas of concern based on the context and content of a conversation during the incident. An incident report based on the recorded and analyzed data.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a cloud-based data management system includes an artificial intelligence module to generate a transcript of an incident using data captured from a body-worn safety device (e.g., audio or video data) that captures an incident surrounding a first responder. The transcription is highly accurate, even in noisy environments, and discerns different speakers, making it valuable for documenting interactions and statements. The transcribed text is analyzed with a private Large Language Model (LLM), to interpret at least one of the audio and video data, and to provide insights, summaries, and/or flag potential areas of concern based on the context and content of a conversation during the incident. An incident report based on the recorded and analyzed data.