Abstract: A keepout zone detection system and method for a hazardous area, such as the workspace around a power tool, that includes a safety device that controls a safety event, such as disabling the power tool, and a keepout zone detection device that includes at least one imager for imaging a workspace associated with the power tool. A processor dynamically determines the keepout zone specific to the power tool based on information received from the imager(s) or by referring to a predetermined keepout zone stored in a memory, where the processor includes a pre-trained neural network that processes the received images to further identify the presence of human appendage(s) within the keepout zone; and a communication device that communicates with the safety device to control the safety event, such as disabling the power tool, based on the determination of the presence of the human appendage within the keepout zone.

Abstract: An active safety device and method for fastener tools that includes a first sensor that detects a hazardous target; a first driver that drives the fastener a minimal distance into the material, the fastener being conductive and acting as a second sensor; a second driver that drives the fastener into the material; a processor that receives information from the first sensor and determines if a hazardous target is detected by said first sensor and engages a trigger interlock if the hazardous target is not detected the first driver is engaged and said second sensor provides information on the composition of the material to the processor; wherein the processor engages the trigger interlock when it determines if the composition of the material is classified as a hazardous target and otherwise initiates the second driver to drive the fastener into the material.

Abstract: A dynamic rollover zone detection system that is easily attached to mobile machinery and dynamically detects a rollover zone around the mobile machinery. The system detects humans and objects within the rollover zone using one or more pre-trained machine learning models in order to prevent accidents and potential collisions between machinery and people or objects in the rollover zone. If a human or obstacle is identified in the rollover zone, an alert or warning is provided and/or the mobile machinery is disabled.

Abstract: A method and system using sensors and a deep neural network to control a treadmill based on user movement on the treadmill. Training data is collected to improve performance in a variety of typical as well as atypical treadmill activities to provide data with which to train a neural network for the task of controlling the treadmill. The method and system includes one or more sensors that obtain user movement and position data while the user is on the treadmill. A command unit (CU) stores the pre-trained neural network and receives the user movement and position data obtained by the one or more sensors. The CU determines the motion commands to provide to the treadmill based on the real-time data received from the sensor(s) and processed through the pre-trained neural network.

Abstract: A keepout zone detection system and method for a hazardous area, such as the workspace around a power tool, that includes a safety device that controls a safety event, such as disabling the power tool, and a keepout zone detection device that includes at least one imager for imaging a workspace associated with the power tool. A processor dynamically determines the keepout zone specific to the power tool based on information received from the imager(s) or by referring to a predetermined keepout zone stored in a memory, where the processor includes a pre-trained neural network that processes the received images to further identify the presence of human appendage(s) within the keepout zone; and a communication device that communicates with the safety device to control the safety event, such as disabling the power tool, based on the determination of the presence of the human appendage within the keepout zone.

Abstract: A removably mountable abnormal motion detection device for abnormal motion detection of a power tool that includes a reactive device and a control device. The control device includes an abnormal detection system that utilizes a processor, one or more sensors; and a machine learning model trained to determine abnormal motion for the power tool. When it is determined by the control device that an abnormal motion has occurred or is about to occur in the near future, the control device communicates with the reactive device to engage a safety feature of the power tool.

Abstract: A lockout device that can be used independently and/or through a connected system that provides information and warnings of conventional lockout tag, while also able to actively provide information to a user and any other individual or device through a network in the system. The lockout device is paired with a user and enables audible and/or visual warnings to ensure notice of the lockout device as well as warnings to the user through the network of possible tampering.

Abstract: A high-precision, low false alarm rate detection apparatus, system, and method for rapid reaction to sudden kinetic impulses caused by abnormal motion, such as kickback, of a power tool. The apparatus, system and method includes local measurement of sensing modalities with local real-time processing and advanced digital signal processing to measure the total kinematic motion of any power tool. A machine learning model pre-trained for the power tool to predict an occurrence of an abnormal motion such as a kickback event of the power tool, the machine learning model using data received from sensing modalities to predict an occurrence of the kickback event, and a reactive device of the power tool is activated upon prediction of the kickback event.

Abstract: A lockout device that can be used independently and/or through a connected system that provides information and warnings of conventional lockout tag, while also able to actively provide information to a user and any other individual or device through a network in the system. The lockout device is paired with a user and enables audible and/or visual warnings to ensure notice of the lockout device as well as warnings to the user through the network of possible tampering.

Abstract: A dynamic rollover zone detection system that is easily attached to mobile machinery and dynamically detects a rollover zone around the mobile machinery. The system detects humans and objects within the rollover zone using one or more pre-trained machine learning models in order to prevent accidents and potential collisions between machinery and people or objects in the rollover zone. If a human or obstacle is identified in the rollover zone, an alert or warning is provided and/or the mobile machinery is disabled.