Methods and apparatus for automating various aspects relating to the design and/or deployment of monitoring systems, e.g., systems which can monitor for fire, break-ins and/or other conditions are described. In various embodiments a customer provides location and customer premises layout information as well as an indication of what is to be monitored. A rules database is accessed and a plan that is compliant with local rules is automatically generated. A user is walked through deployment of components, e.g., on his cell phone or other handheld communications device, in an easy to follow manner with monitoring device position, wireless communications ability, and function being checked automatically as part of the process as each sensor is deployed. If wireless connectivity is a problem suggestions are presented to the user for moving a sensor, e.g., camera. At the end of the process the system is activated and monitoring of the premises initiated.

Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Described is a security sensor comprising two or more sub-sensors for use in a variety of installations where different magnetic fields may be experienced by the security sensor as a result of the variety of installations. One of the sub-sensors may have a low magnetic sensitivity while the other sub-sensor may have a much higher sensitivity to magnetic fields. In operation, one or both sub-sensors are used to determine if a door or a window has been opened.

Described is a security sensor comprising two or more sub-sensors for use in a variety of installations where different magnetic fields may be experienced by the security sensor as a result of the variety of installations. One of the sub-sensors may have a low magnetic sensitivity while the other sub-sensor may have a much higher sensitivity to magnetic fields. In operation, one or both sub-sensors are used to determine if a door or a window has been opened.

Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Described is a security sensor comprising two or more sub-sensors for use in a variety of installations where different magnetic fields may be experienced by the security sensor as a result of the variety of installations. One of the sub-sensors may have a low magnetic sensitivity while the other sub-sensor may have a much higher sensitivity to magnetic fields. In operation, one or both sub-sensors are used to determine if a door or a window has been opened.

Described is a security sensor comprising two or more sub-sensors for use in a variety of installations where different magnetic fields may be experienced by the security sensor as a result of the variety of installations. One of the sub-sensors may have a low magnetic sensitivity while the other sub-sensor may have a much higher sensitivity to magnetic fields. In operation, one or both sub-sensors are used to determine if a door or a window has been opened.

A method, system and computer program product for emulating depth data of a three-dimensional camera device is disclosed. The method includes concurrently operating the radar device and the 3D camera device to generate training radar data and training depth data respectively. Each of the radar device and the 3D camera device has a respective field of view. The field of view of the radar device overlaps the field of view of the 3D camera device. The method also includes inputting the training radar and depth data to the neural network. The method also includes employing the training radar and depth data to train the neural network. Once trained, the neural network is configured to receive real radar data as input and to output substitute depth data.