Abstract: Apparatus and methods for error modeling and correction in one or both of (i) a partially or fully implanted or non-implanted medicant delivery mechanism (such as a pump), and (ii) implanted physiologic parameter sensor. In one exemplary embodiment, the apparatus and methods employ a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the medicant dose calculation process, and (ii) generation of a medicant delivery operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation of the sensor and pump system. This enhances accuracy of medicant delivery, such as over the lifetime of an implanted pump at a single implantation site, or during multiple relocations of a transcutaneously implanted pump), and enables “personalization” of the pump to each user.

Abstract: Apparatus and methods for response modeling and correction of signals associated with a parameter sensor. In one exemplary embodiment, the parameter sensor is configured to measure a physiologic analyte of a living being (e.g., blood glucose), and the apparatus and methods employ a mathematical transformation of two or more sensing elements (electrodes) of the sensor in order to compensate for temporal response differences or “mismatch.” This compensation enables the calculated blood analyte level, which results from processing of the signals of the two or more sensing electrodes, to be more accurate than calculations made without such compensation. In one variant, the parameter signals are generated, and compensation processing conducted, autonomously via a common implanted sensor platform.

Abstract: Apparatus and methods for error modeling and correction in one or both of (i) a partially or fully implanted or non-implanted medicant delivery mechanism (such as a pump), and (ii) implanted physiologic parameter sensor. In one exemplary embodiment, the apparatus and methods employ a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the medicant dose calculation process, and (ii) generation of a medicant delivery operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation of the sensor and pump system. This enhances accuracy of medicant delivery, such as over the lifetime of an implanted pump at a single implantation site, or during multiple relocations of a transcutaneously implanted pump), and enables “personalization” of the pump to each user.

Abstract: Apparatus and methods for response modeling and correction of signals associated with a parameter sensor. In one exemplary embodiment, the parameter sensor is configured to measure a physiologic analyte of a living being (e.g., blood glucose), and the apparatus and methods employ a mathematical transformation of two or more sensing elements (electrodes) of the sensor in order to compensate for temporal response differences or “mismatch.” This compensation enables the calculated blood analyte level, which results from processing of the signals of the two or more sensing electrodes, to be more accurate than calculations made without such compensation. In one variant, the parameter signals are generated, and compensation processing conducted, autonomously via a common implanted sensor platform.

Abstract: Enzymatic and non-enzymatic detectors and associated membrane apparatus, and methods of use, such as within a fully implantable sensor apparatus. In one embodiment, detector performance is controlled through selective use of membrane configurations and enzyme region shapes, which enable accurate detection of blood glucose level within the solid tissue of the living host for extended periods of time. Isolation between the host's tissue and the underlying enzymes and reaction byproducts used in the detectors is also advantageously maintained in one embodiment via use of a non-enzyme containing permeable membrane formed of e.g., a biocompatible crosslinked protein-based material. Control of response range and/or rate in some embodiments also permits customization of sensor elements. In one variant, heterogeneous detector elements are used to, e.g., accommodate a wider range of blood glucose concentration within the host.

Abstract: Implantable sensor apparatus and methods of implantation. In one embodiment, a fully implantable, biocompatible sensor is disposed within a cavity or pocket formed within a living being, such that the sensor remains in a desired orientation and placement so as to enhance the performance of the sensor, and mitigate the effects of one or more factors potentially deleterious to the operation of the sensor and the host being. In one implementation, the sensor comprises an implantable biocompatible oxygen-based glucose sensor which is implanted deep within the being's torso tissue proximate the extant fascia, and oriented such that an active detector aspect of the device faces away from the being's skin surface. In one variant, the deep placement, orientation, and construction of the sensor itself cooperate to enhance the performance of the sensor, especially over extended periods of time, with little need for external calibration.

Abstract: Biocompatible implantable sensor apparatus and methods of implantation and use. In one embodiment, the sensor apparatus is an oxygen-based glucose sensor having biocompatibility features that mitigate the host tissue response. In one variant, these features include use of a non-enzymatic membrane over each of the individual analyte detectors so as to preclude contact of the surrounding tissue with the underlying enzyme or other matrix, and mitigate vascularization, and insulation of the various electrodes and associated electrolytic processes of the sensor from the surrounding tissue. In one implementation, the sensor region of the implanted apparatus is configured to interlock or imprint the surrounding tissue so as to promote a high degree of glucose molecule diffusion into the individual detectors, and a constant and predictable sensor to blood vessel interface, yet preclude the tissue from bonding to the sensor, especially over extended periods of implant.

Abstract: Apparatus and methods for error modeling and correction in a blood analyte sensor or system. In one exemplary embodiment, the apparatus employs: (i) a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation and collection of blood analyte data. This enhances device signal stability and accuracy over extended periods, thereby enabling among other things extended periods of blood analyte sensor implantation, and “personalization” of the sensor apparatus to each user receiving an implant. In one variant, the blood analyte is glucose, and the implanted sensor utilizes an oxygen-based molecular measurement principle.

Abstract: Enzymatic and non-enzymatic detectors and associated membrane apparatus, and methods of use, such as within a fully implantable sensor apparatus. In one embodiment, detector performance is controlled through selective use of membrane configurations and enzyme region shapes, which enable accurate detection of blood glucose level within the solid tissue of the living host for extended periods of time. Isolation between the host's tissue and the underlying enzymes and reaction byproducts used in the detectors is also advantageously maintained in one embodiment via use of a non-enzyme containing permeable membrane formed of e.g., a biocompatible crosslinked protein-based material. Control of response range and/or rate in some embodiments also permits customization of sensor elements. In one variant, heterogeneous detector elements are used to, e.g., accommodate a wider range of blood glucose concentration within the host.

Abstract: Implantable sensor apparatus and methods of implantation. In one embodiment, a fully implantable, biocompatible sensor is disposed within a cavity or pocket formed within a living being, such that the sensor remains in a desired orientation and placement so as to enhance the performance of the sensor, and mitigate the effects of one or more factors potentially deleterious to the operation of the sensor and the host being. In one implementation, the sensor comprises an implantable biocompatible oxygen-based glucose sensor which is implanted deep within the being's torso tissue proximate the extant fascia, and oriented such that an active detector aspect of the device faces away from the being's skin surface. In one variant, the deep placement, orientation, and construction of the sensor itself cooperate to enhance the performance of the sensor, especially over extended periods of time, with little need for external calibration.

Abstract: Apparatus and methods for error modeling and correction in a blood analyte sensor or system. In one exemplary embodiment, the apparatus employs: (i) a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation and collection of blood analyte data. This enhances device signal stability and accuracy over extended periods, thereby enabling among other things extended periods of blood analyte sensor implantation, and “personalization” of the sensor apparatus to each user receiving an implant. In one variant, the blood analyte is glucose, and the implanted sensor utilizes an oxygen-based molecular measurement principle.

Abstract: Biocompatible implantable sensor apparatus and methods of implantation and use. In one embodiment, the sensor apparatus is an oxygen-based glucose sensor having biocompatibility features that mitigate the host tissue response. In one variant, these features include use of a non-enzymatic membrane over each of the individual analyte detectors so as to preclude contact of the surrounding tissue with the underlying enzyme or other matrix, and mitigate vascularization, and insulation of the various electrodes and associated electrolytic processes of the sensor from the surrounding tissue. In one implementation, the sensor region of the implanted apparatus is configured to interlock or imprint the surrounding tissue so as to promote a high degree of glucose molecule diffusion into the individual detectors, and a constant and predictable sensor to blood vessel interface, yet preclude the tissue from bonding to the sensor, especially over extended periods of implant.

Abstract: Enzymatic and non-enzymatic detectors and associated membrane apparatus, and methods of use, such as within a fully implantable sensor apparatus. In one embodiment, detector performance is controlled through selective use of membrane configurations and enzyme region shapes, which enable accurate detection of blood glucose level within the solid tissue of the living host for extended periods of time. Isolation between the host's tissue and the underlying enzymes and reaction byproducts used in the detectors is also advantageously maintained in one embodiment via use of a non-enzyme containing permeable membrane formed of e.g., a biocompatible crosslinked protein-based material. Control of response range and/or rate in some embodiments also permits customization of sensor elements. In one variant, heterogeneous detector elements are used to, e.g., accommodate a wider range of blood glucose concentration within the host.

Abstract: Apparatus and methods for reducing error due to spatial arrangement of sensor elements in a parameter sensor such as a physiologic analyte sensor. In one exemplary embodiment, the analyte sensor is configured to measure an analyte of a living being (e.g., blood glucose), and the apparatus and methods employ determination of a blood analyte concentration based on a prescribed relationship of N1/N2—i.e., N1 analyte modulated sensing elements (e.g., glucose electrodes) associated with and proximate to N2 background sensing elements of the sensor—in order to compensate for response differences due to spatial arrangement of the sensor elements or “spatial mismatch.” This configuration of sensor elements and method of determining blood analyte concentration (based on multiple background signal electrodes) enables increased accuracy of the sensor.

Abstract: Apparatus and methods for response modeling and correction of signals associated with a parameter sensor. In one exemplary embodiment, the parameter sensor is configured to measure a physiologic analyte of a living being (e.g., blood glucose), and the apparatus and methods employ a mathematical transformation of two or more sensing elements (electrodes) of the sensor in order to compensate for temporal response differences or “mismatch.” This compensation enables the calculated blood analyte level, which results from processing of the signals of the two or more sensing electrodes, to be more accurate than calculations made without such compensation. In one variant, the parameter signals are generated, and compensation processing conducted, autonomously via a common implanted sensor platform.

Abstract: Apparatus and methods for error modeling and correction in one or both of (i) a partially or fully implanted or non-implanted medicant delivery mechanism (such as a pump), and (ii) implanted physiologic parameter sensor. In one exemplary embodiment, the apparatus and methods employ a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the medicant dose calculation process, and (ii) generation of a medicant delivery operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation of the sensor and pump system. This enhances accuracy of medicant delivery, such as over the lifetime of an implanted pump at a single implantation site, or during multiple relocations of a transcutaneously implanted pump), and enables “personalization” of the pump to each user.

Abstract: Receiver apparatus for use with an analyte sensor, and methods of operation and manufacturing. In one embodiment, the analyte sensor is an implanted/implantable blood glucose sensor, including oxygen-based detector elements. The receiver apparatus is a wireless-enabled small form-factor device with limited functionality that can be easily worn or kept with the user on a continual basis, thereby obviating the need for a more fully featured receiver or smartphone for extended periods of time (e.g., one week). The exemplary oxygen based analyte sensor, with high degree of stability over time, enables the user to divorce themselves from the more fully functioned receiver or smartphone, since no external calibration of the sensor is required during the extended period. In one variant, the device is a lightweight wristband. Other variants include e.g., pendants, finger-worn rings, arm or head bands, skin patches, and even dental, subcutaneous, or prosthetic implants.

Abstract: Apparatus and methods for error modeling and correction in a blood analyte sensor or system. In one exemplary embodiment, the apparatus employs: (i) a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation and collection of blood analyte data. This enhances device signal stability and accuracy over extended periods, thereby enabling among other things extended periods of blood analyte sensor implantation, and “personalization” of the sensor apparatus to each user receiving an implant. In one variant, the blood analyte is glucose, and the implanted sensor utilizes an oxygen-based molecular measurement principle.

Abstract: Enzymatic and non-enzymatic detectors and associated membrane apparatus, and methods of use, such as within a fully implantable sensor apparatus. In one embodiment, detector performance is controlled through selective use of membrane configurations and enzyme region shapes, which enable accurate detection of blood glucose level within the solid tissue of the living host for extended periods of time. Isolation between the host's tissue and the underlying enzymes and reaction byproducts used in the detectors is also advantageously maintained in one embodiment via use of a non-enzyme containing permeable membrane formed of e.g., a biocompatible crosslinked protein-based material. Control of response range and/or rate in some embodiments also permits customization of sensor elements. In one variant, heterogeneous detector elements are used to, e.g., accommodate a wider range of blood glucose concentration within the host.

Abstract: User interface (UI) apparatus and associated logic and methods for use with a blood analyte sensor. In one embodiment, the blood analyte sensor comprises an implantable blood glucose sensor, and the UI and associated logic are configured to receive and store user-specified parameters for operation of the blood analyte sensor and/or associated receiver. In one exemplary implementation, the UI and associated logic are configure to selectively implement various user interface regimes, including display of various analyses of current blood analyte level data and/or historical blood analyte level data, as well as prompting the user to take actions. The exemplary implementation is also optionally configured to identify and mitigate data errors.