Abstract: The invention pertains to systems and non-transitory computer-readable media for extrinsic monitoring and interoperability assistance of automation systems. An audiovisual capture apparatus, data stores, anomaly manager, logic engine, and alert module cooperate to process nominal operation data, train an unsupervised machine learning model, monitor real-time image, audio, audiovisual, or energy-consumption data, and detect expectation violations. Responsive to an expectation violation, contrastive media retrieved from the data stores is generated and transmitted to an end user. Additional embodiments provide XR-viewable spatial representations, configuration and integration annotations, proxy communication channels between non-integrated systems, and autonomous or user-authorized remedial actions. The disclosed embodiments support visual, audible, audiovisual, energy-monitoring, and system-to-system interoperability workflows for diagnosing automation anomalies.

Abstract: The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques.

Abstract: The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques.

Abstract: The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques.

Abstract: The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques.

Abstract: An anatomical feature simulation unit is a physical device designed to help simulate an anatomical feature (e.g., a wound) on an object (e.g., a human being or human surrogate such as a medical manikin) for instructing a trainee to learn or practice treatment skills. For the trainee, the simulation looks like a real body part when viewed using an Augmented Reality (AR) system. Responsive to a change in the anatomic state of the object (e.g., bending a knee or raising of an arm) not only the spatial location and orientation of the anatomical feature stays locked on the object in the AR system, but the characteristics of the anatomical feature change based on the physiologic logic of changing said anatomical state (e.g., greater or less blood flow, opening or closing of a wound).

Abstract: An anatomical feature simulation unit is a physical device designed to help simulate an anatomical feature (e.g., a wound) on an object (e.g., a human being or human surrogate such as a medical manikin) for instructing a trainee to learn or practice treatment skills. For the trainee, the simulation looks like a real body part when viewed using an Augmented Reality (AR) system. Responsive to a change in the anatomic state of the object (e.g., bending a knee or raising of an arm) not only the spatial location and orientation of the anatomical feature stays locked on the object in the AR system, but the characteristics of the anatomical feature change based on the physiologic logic of changing said anatomical state (e.g., greater or less blood flow, opening or closing of a wound).

Abstract: A wound simulation unit is a physical device designed to help simulate a wound on an object (e.g., a human being or human surrogate such as a medical manikin) for instructing a trainee to learn or practice wound-related treatment skills. For the trainee, the simulation looks like a real wound when viewed using an Augmented Reality (AR) system. Responsive to a change in the anatomic state of the object (e.g., bending a knee or raising of an arm) not only the spatial location and orientation of the wound stays locked on the object in the AR system, but the characteristics of the wound change based on the physiologic logic of changing said anatomical state (e.g., greater or less blood flow, opening or closing of the wound).

Abstract: A Tactile-Visual Wound (TVW) simulation unit is a physical device designed to help simulate a wound on a human being or human surrogate (e.g., a medical manikin) for instructing a trainee to learn or practice wound-related treatment skills. For the trainee, the TVW would feel (to the touch) like a real wound, look like a real wound when viewed using an Augmented Reality (AR) system, and appear to behave like a real wound when manipulated.

Abstract: A Tactile-Visual Wound (TVW) simulation unit is a physical device designed to help simulate a wound on a human being or human surrogate (e.g., a medical manikin) for instructing a trainee to learn or practice wound-related treatment skills. For the trainee, the TVW would feel (to the touch) like a real wound, look like a real wound when viewed using an Augmented Reality (AR) system, and appear to behave like a real wound when manipulated.