Apparatuses, systems and methods of providing a personal air quality monitor. The personal air quality monitor may include a housing suitable for physical association with a person; an air inlet capable of providing air flow from an environment outside the housing to a chamber within the housing; a plurality of sensor resident within the chamber, suitable for receiving the airflow and measuring air quality factors associated with the airflow; and a communication system suitable for processing the air quality factor measurements and communicating the processed air quality factor measurements to a user interface cognizable to the person.

The disclosed haptic vibrotactile actuators on inflatable bladders may include an inflatable bladder and a flexible haptic vibrotactile actuator positioned on or in the inflatable bladder such that inflation and deflation of the inflatable bladder alters a vibrotactile sensation induced by the flexible haptic vibrotactile actuator in response to activation of the flexible haptic vibrotactile actuator. Various other related methods and systems are also disclosed.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for pathogen detection. One of the methods includes providing, to a classifier, sensor data for a physical area, at a property, to cause the classifier to generate output data using the sensor data; receiving, from the classifier, the output data that indicates whether a pathogen was likely detected; detecting, using the output data, a likely pathogen in the physical area; determining whether to provide an alert given the detection of the likely pathogen; and in response to determining whether to provide the alert given the detection of the likely pathogen, selectively providing, to a device, the alert to cause the device to present the alert or determining to skip providing the alert.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for pathogen detection. One of the methods includes providing, to a classifier, sensor data for a physical area, at a property, to cause the classifier to generate output data using the sensor data; receiving, from the classifier, the output data that indicates whether a pathogen was likely detected; detecting, using the output data, a likely pathogen in the physical area; determining whether to provide an alert given the detection of the likely pathogen; and in response to determining whether to provide the alert given the detection of the likely pathogen, selectively providing, to a device, the alert to cause the device to present the alert or determining to skip providing the alert.

A monitoring system for monitoring, measuring and detecting analytes in an environment or in test samples from an environment. The monitoring system generates data in response to the presence of at least one analyte. The monitoring system is configured to communicate the data to an associated receiver such as personal communication device or the like for processing. The monitoring system can be in the form of a selectively attachable component.

A monitoring system for monitoring, measuring and detecting analytes in an environment or in test samples from an environment. The monitoring system generates data in response to the presence of at least one analyte. The monitoring system is configured to communicate the data to an associated receiver such as personal communication device or the like for processing. The monitoring system can be in the form of a selectively attachable component.

A detection system for measuring one or more conditions within an area. At least one fiber optic cable transmits light wherein the at least one fiber optic cable defines a plurality of nodes arranged to measure the one or more conditions. A control system communicates with the at least one fiber optic cable such that scattered light and a time of flight record is transmitted from the at least one fiber optic cable to the control system. The control system includes a detection algorithm operable to identify a portion of the scattered light associated with each of the plurality of nodes. When determining an alert, the control system transmits data associated with a presence and magnitude of the one or more conditions at each of the plurality of nodes to a cloud computing environment and, in return, receives a notification based on the data transmitted.

The present invention is directed to a device consisting of a portable and multi-band UV light device that uses a combination of UVA, UVB, and UVC wavelength band of UV emitters. For detection, UV emitters are energized to a particulate collector that will fluoresce and glow when there is the presence of a wide range of different harmful aerosol droplet particles in the air that may be collected. This instant and positive visual detection with an available audio alarm alert indicates the presence of harmful aerosol droplet particles in the vicinity of this Instant Particulate Detector or IPD device, allowing the user to take immediate and corrective action. The user can also subsequently select disinfection utilizing UVC wavelength light to sterilize the particulate collector and Instant Particulate Detector or IPD device.

The present invention is directed to a device consisting of a portable and multi-band UV light device that uses a combination of UVA, UVB, and UVC wavelength band of UV emitters. For detection, UV emitters are energized to a particulate collector that will fluoresce and glow when there is the presence of a wide range of different harmful aerosol droplet particles in the air that may be collected. This instant and positive visual detection with an available audio alarm alert indicates the presence of harmful aerosol droplet particles in the vicinity of this Instant Particulate Detector or IPD device, allowing the user to take immediate and corrective action. The user can also subsequently select disinfection utilizing UVC wavelength light to sterilize the particulate collector and Instant Particulate Detector or IPD device.

A method for predicting air quality with the aid of machine learning models includes: (A) providing air pollution data to perform an eXtreme Gradient Boosting (XGBoost) regression algorithm for obtaining a XGBoost prediction value; (B) providing the air pollution data to perform a Long Short-Term Memory (LSTM) algorithm for obtaining an LSTM prediction value; (C) combining the air pollution data, the XGBoost prediction value and the LSTM prediction value to generate air pollution combination data; (D) performing an XGBoost classification algorithm to obtain a suggestion for whether to issue an air pollution alert; and (E) performing the XGBoost regression algorithm on the air pollution combination data to obtain an air pollution prediction value. Two layers of machine learning models are built, and a situation where prediction results are too conservative when a single model does not have enough data can be improved.