Abstract: Wearable pulse pressure wave sensing devices are presented that generally provide a non-intrusive way to measure a pulse pressure wave travelling through an artery using a wearable device. In one implementation, the device includes an array of pressure sensors disposed on a mounting structure which is attachable to a user on an area proximate to an underlying artery. Each of the pressure sensors is capable of being mechanically coupled to the skin of the user proximate to the underlying artery. In addition, there are one or more arterial location sensors disposed on the mounting structure which identify a location on the user's skin likely overlying the artery. A pulse pressure wave is then measured using the pressure sensor of the array closest to the identified location.

Abstract: Wearable pulse pressure wave sensing devices are presented that generally provide a non-intrusive way to measure a pulse pressure wave travelling through an artery using a wearable device. In one implementation, the device includes an array of pressure sensors disposed on a mounting structure which is attachable to a user on an area proximate to an underlying artery. Each of the pressure sensors is capable of being mechanically coupled to the skin of the user proximate to the underlying artery. In addition, there are one or more arterial location sensors disposed on the mounting structure which identify a location on the user's skin likely overlying the artery. A pulse pressure wave is then measured using the pressure sensor of the array closest to the identified location.

Abstract: Wearable pulse pressure wave sensing devices are presented that generally provide a non-intrusive way to measure a pulse pressure wave travelling through an artery using a wearable device. In one implementation, the device includes an array of pressure sensors disposed on a mounting structure which is attachable to a user on an area proximate to an underlying artery. Each of the pressure sensors is capable of being mechanically coupled to the skin of the user proximate to the underlying artery. In addition, there are one or more arterial location sensors disposed on the mounting structure which identify a location on the user's skin likely overlying the artery. A pulse pressure wave is then measured using the pressure sensor of the array closest to the identified location.

Abstract: A wearable sensing band is presented that generally provides a non-intrusive way to measure a person's cardiovascular vital signs including pulse transit time and pulse wave velocity. The band includes a strap with one or more primary electrocardiography (ECG) electrodes which are in contact with a first portion of the user's body, one or more secondary ECG electrodes, and one or more pulse pressure wave arrival (PPWA) sensors. The primary and secondary ECG electrodes detect an ECG signal whenever the secondary ECG electrodes make electrical contact with the second portion of the user's body, and the PPWA sensors sense an arrival of a pulse pressure wave to the first portion of the user's body from the user's heart. The ECG signal and PPWA sensor(s) readings are used to compute at least one of a pulse transit time (PTT) or a pulse wave velocity (PWV) of the user.

Abstract: Electrical battery apparatus embodiments are presented that generally involve incorporating sensing, computing, and communication capabilities into the one common component that a vast number of electronic devices employ—namely batteries. By integrating these capabilities into disposable and/or rechargeable batteries, new functionality and intelligence can be provided to otherwise stand-alone devices.

Abstract: An aggregation framework system and method that automatic configures, aggregates, disaggregates, manages, and optimizes components of a consolidated system of devices, modules, and sensors. Embodiments of the system and method include a low-power alert sensor, a data aggregator module, and an interpreter module. The low-power alert sensor is a sensor that is continuously on and continuously monitoring its environment. The low-power alert sensor acts as a watchdog and triggers other sensors to awaken them from a power-conservation state when there is a change or event that occurs in an environment. The data aggregator module manages the set of sensors within the system and aggregates sensor data obtained from the sensors. The interpreter module then translates the physical data collected by sensors into logical information. Together the data aggregator module and the interpreter module present a unified logical view of the capabilities of the sensors under their control.

Abstract: Wearable pulse pressure wave sensing devices are presented that generally provide a non-intrusive way to measure a pulse pressure wave travelling through an artery using a wearable device. In one implementation, the device includes an array of pressure sensors disposed on a mounting structure which is attachable to a user on an area proximate to an underlying artery. Each of the pressure sensors is capable of being mechanically coupled to the skin of the user proximate to the underlying artery. In addition, there are one or more arterial location sensors disposed on the mounting structure which identify a location on the user's skin likely overlying the artery. A pulse pressure wave is then measured using the pressure sensor of the array closest to the identified location.

Abstract: The cardiovascular vital signs of a user are measured. One or more user activity metrics is received from one or more user activity sensors. A type of activity the user is currently engaged in is inferred from the received user activity metrics. Additional context that is associated with the inferred type of activity may also be identified. A determination is made as to if it is time to measure the cardiovascular vital signs of the user, where this determination is based on the inferred type of activity and may also be based on the identified additional context. Whenever it is determined to be time to measure the cardiovascular vital signs of the user, this measurement is made.

Abstract: A wearable sensing band is presented that generally provides a non-intrusive way to measure a person's cardiovascular vital signs including pulse transit time and pulse wave velocity. The band includes a strap with one or more primary electrocardiography (ECG) electrodes which are in contact with a first portion of the user's body, one or more secondary ECG electrodes, and one or more pulse pressure wave arrival (PPWA) sensors. The primary and secondary ECG electrodes detect an ECG signal whenever the secondary ECG electrodes make electrical contact with the second portion of the user's body, and the PPWA sensors sense an arrival of a pulse pressure wave to the first portion of the user's body from the user's heart. The ECG signal and PPWA sensor(s) readings are used to compute at least one of a pulse transit time (PTT) or a pulse wave velocity (PWV) of the user.

Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCI) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.

Abstract: A machine learning model is trained by instructing a user to perform various predefined gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model.

Abstract: Electrical battery apparatus embodiments are presented that generally involve incorporating sensing, computing, and communication capabilities into the one common component that a vast number of electronic devices employ—namely batteries. By integrating these capabilities into disposable and/or rechargeable batteries, new functionality and intelligence can be provided to otherwise stand-alone devices.

Abstract: A “Logical Sensor Server” or “LSS” acts as a smart hub between related or unrelated sensors, devices, or other systems by translating, morphing, or forwarding signals or events published by various input sources into signals or higher-order events that can be consumed or used by other subscribing sensors, devices, or systems. More specifically, the LSS acts alone or in combination with a Logical Sensor Platform (LSP) to enable various techniques that allow messages received from different input sources to be authored, transformed and made available to one or more subscribers in a manner that allows intelligent event-driven behavior to emerge from a collection of relatively simple input sources. Any combination of automatic configuration or user input is used to define the format of transformed inputs to be received by particular subscribers relative to one or more publications. Subscribers receiving transformed events control their own actions based on those events.

Abstract: An aggregation framework system and method that automatic configures, aggregates, disaggregates, manages, and optimizes components of a consolidated system of devices, modules, and sensors. Embodiments of the system and method include a low-power alert sensor, a data aggregator module, and an interpreter module. The low-power alert sensor is a sensor that is continuously on and continuously monitoring its environment. The low-power alert sensor acts as a watchdog and triggers other sensors to awaken them from a power-conservation state when there is a change or event that occurs in an environment. The data aggregator module manages the set of sensors within the system and aggregates sensor data obtained from the sensors. The interpreter module then translates the physical data collected by sensors into logical information. Together the data aggregator module and the interpreter module present a unified logical view of the capabilities of the sensors under their control.

Abstract: A charging station wirelessly transmits power to mobile electronic devices (MEDs) each having a planar-shaped receiver coil (RC) and a capacitor connected in parallel across the RC. The station includes a planar charging surface, a number of series-interconnected bank A source coils (SCs), a number of series-interconnected bank B SCs, and electronics for energizing the SCs. Each SC generates a flux field perpendicular to the charging surface. The bank A and bank B SCs are interleaved and alternately energized in a repeating duty cycle. The coils in each bank are also alternately wound in a different direction so that the fields cancel each other out in a far-field environment. Whenever an MED is placed in close proximity to the charging surface, the fields wirelessly induce power in the RC. The MEDs can have any two-dimensional orientation with respect to the charging surface.

Abstract: A machine learning model is trained by instructing a user to perform various predefined gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model.

Abstract: A machine learning model is trained by instructing a user to perform proscribed gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model.

Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.

Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.

Abstract: A charging station wirelessly transmits power to mobile electronic devices (MEDs) each having a planar-shaped receiver coil (RC) and a capacitor connected in parallel across the RC. The station includes a planar charging surface, a number of series-interconnected bank A source coils (SCs), a number of series-interconnected bank B SCs, and electronics for energizing the SCs. Each SC generates a flux field perpendicular to the charging surface. The bank A and bank B SCs are interleaved and alternately energized in a repeating duty cycle. The coils in each bank are also alternately wound in a different direction so that the fields cancel each other out in a far-field environment. Whenever an MED is placed in close proximity to the charging surface, the fields wirelessly induce power in the RC. The MEDs can have any two-dimensional orientation with respect to the charging surface.