Abstract: Systems and methods for transferring learning in sensor devices. Historical time-series measurement samples of one or more parameters associated with a biological function being monitored by the sensor device are received and assigned to clusters. Feature data extracted from the historical time-series measurement samples are used to generate cluster-specific source-domain classifiers for each cluster. Unlabeled time-series measurement samples of the one or more parameters associated with the biological function are received. A cluster-identifier is assigned to each unlabeled target-domain sample, the cluster-identifier including information identifying a cluster-specific source-domain classifier associated with the unlabeled target-domain sample.

Abstract: Augmented reality-based training systems are described that dynamically construct and render AR content based on physical attributes of a user to train the user to correctly fit one or more articles of a personal protective equipment (PPE) onto the user's body. In some examples, an AR system includes a display and a computing device having a memory and one or more processors. The memory includes instructions that when executed by the processors simulate a fitting of a PPE to a worker by capturing at least a first image of the worker; selecting a digital model of the PPE; determining an alignment of the digital model of the PPE to the first image of the worker; and outputting for display augmented reality content comprising a composite of a least a second image of the worker overlaid with the digital model of the PPE in accordance with the determined alignment.

Abstract: Methods and systems for automated detection of carton damage are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.

Abstract: A method and system of determining a filter life are disclosed. In examples, a method comprises capturing aerosol particles on a filter and measuring absorbance spectra of the aerosol particles captured on the filter. The method further comprises identifying one or more aerosol particle types based on the measured absorbance spectra and determining a mass accumulation of each of the one or more aerosol particle types based on the measured absorbance spectra and the aerosol particle type. The method further comprises determining a median particle size of each of the one or more aerosol particle types based on the measured absorbance spectra and the aerosol particle type and determining a filter life based on the determined mass accumulation and the determined median particle size of each of the one or more aerosol particle types.

Abstract: A method and system of determining air quality are disclosed. In examples, a method comprises identifying one or more aerosol particle types based on an absorbance spectra of aerosol particles captured on a filter and determining a mass concentration of each of the one or more aerosol particle types based on the absorbance spectra and the aerosol particle type. The method further comprises detecting a median particle size of each of the one or more aerosol particle types based on a rate of change of the absorbance spectra and the aerosol particle type. The method further comprises determining an air quality metric based on the identified one or more aerosol particle types, the determined mass concentration of each of the one or more aerosol particle types, and the determined median particle size of each of the one or more aerosol particle types.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: Methods and systems for automated detection of carton defects are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.

Abstract: Systems and methods for monitoring the condition of an air filter installed in an HVAC system and for monitoring the condition of the HVAC system. The monitoring system includes a processing unit configured to receive data representative of at least a first temporal parameter of the HVAC system. The processing unit can process the data to obtain an indication of the condition of the air filter and can also process the data to obtain an indication of the condition of the HVAC system.

Abstract: Methods and systems for automated detection of carton damage are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.

Abstract: Methods for automatically removing collisions between digital mesh objects and moving digital mesh objects between spatial arrangements. The collisions are removed from a set of digital mesh objects using a perturbation method or a mesh deformation method. After removing the collisions, the digital mesh objects are output in a state without collisions between them. The digital mesh objects can be moved between initial and final states based upon motion constraints of the mesh objects and interpolated states of them between the initial and final states. Based upon the constraints and interpolated states, a number of states for movement of the set of digital mesh objects is determined either collectively for the set or individually for each mesh object. The states can be used as digital setups for dental or orthodontic treatment planning.

Abstract: An article includes a substrate having a physical surface, a multi-dimensional machine-readable code embodied on the physical surface, wherein the multi-dimensional machine-readable optical code includes a static data (SD) optical element set and a dynamic lookup data (DLD) optical element set. Each of the SD and the DLD sets are embodied on the physical surface. The DLD optical element set encodes a look-up value that references dynamically changeable data, wherein the SD optical element set encodes static data that does not reference other data. The DLD optical element set is not decodable at the distance greater than the threshold distance.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: Systems and methods for detecting the quality of signals captured by a sensor device monitoring a biological function. Sensor data associated with the sensor device is received, the sensor data representing time-series measurement samples of one or more parameters associated with the biological function, the sensor data including usable and unusable samples of the time-series measurements. Data representing two or more features of samples of the time-series measurements is extracted and filtered to reduce outliers in the extracted data based on an expected outlier ratio. A machine learning algorithm is then trained to identify events based on the filtered extracted data.

Abstract: In an example, a system comprises at least one body-worn tracking device (BWTD) configured to transmit location data that indicates a location of the BWTD and a computing system configured to communicate with the at least one BWTD. The computing system is configured to receive the location data from the at least one BWTD, determine, based on a set of weighted filters associated with at least one monitoring attribute, at least one gradient value corresponding to the location data, where the at least one gradient value represents a level of the at least one monitoring attribute, and perform at least one operation based on the at least one gradient value.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.

Abstract: In an example, a system comprises at least one body-worn tracking device (BWTD) configured to transmit location data that indicates a location of the BWTD and a computing system configured to communicate with the at least one BWTD. The computing system is configured to receive the location data from the at least one BWTD, determine, based on a set of weighted filters associated with at least one monitoring attribute, at least one gradient value corresponding to the location data, where the at least one gradient value represents a level of the at least one monitoring attribute, and perform at least one operation based on the at least one gradient value.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: In some examples, an article includes a substrate and a plurality of optical element sets embodied on the substrate, wherein each optical element set includes a plurality of optical elements, wherein each respective optical element represents an encoded value in a set of encoded values, wherein the set of encoded values are differentiable based on visual differentiability of the respective optical elements, wherein each respective optical element set represents at least a portion of a message or error correction data to decode the message if one or more of the plurality of optical element sets are visually occluded, and wherein the optical element sets for the message and error correction data are spatially configured at the physical surface in a matrix such that the message is decodable from the substrate without optical elements positioned within at least one complete edge of the matrix that is visually occluded.