Patent application number | Description | Published |
20090062679 | CATEGORIZING PERCEPTUAL STIMULI BY DETECTING SUBCONCIOUS RESPONSES - A perceptual stimulus categorization technique is presented which identifies the stimuli category of a perceptual stimulus that has been presented to a person whose brain activity is being monitored. This generally accomplished by first training a detection module to recognize the part of the brain activity generated in response to the presentation of a stimulus belonging to each of one or more stimuli categories using brain activity information. Once the detection module is trained, a subsequent instance of a stimulus belonging to a trained stimuli category being presented to the person is detected, and this detection is used to identify the trained stimuli category to which the presented stimulus belongs. | 03-05-2009 |
20090326406 | WEARABLE ELECTROMYOGRAPHY-BASED CONTROLLERS FOR HUMAN-COMPUTER INTERFACE - 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. Examples of Wearable Electromyography-Based Controllers include articles of manufacture, such as an armband, wristwatch, or article of clothing having a plurality of integrated EMG-based sensor nodes and associated electronics. | 12-31-2009 |
20090327171 | RECOGNIZING GESTURES FROM FOREARM EMG SIGNALS - 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. | 12-31-2009 |
20100241596 | INTERACTIVE VISUALIZATION FOR GENERATING ENSEMBLE CLASSIFIERS - A real-time visual feedback ensemble classifier generator and method for interactively generating an optimal ensemble classifier using a user interface. Embodiments of the real-time visual feedback ensemble classifier generator and method use a weight adjustment operation and a partitioning operation in the interactive generation process. In addition, the generator and method include a user interface that provides real-time visual feedback to a user so that the user can see how the weight adjustment and partitioning operation affect the overall accuracy of the ensemble classifier. Using the user interface and the interactive controls available on the user interface, a user can iteratively use one or both of the weigh adjustment operation and partitioning operation to generate an optimized ensemble classifier. | 09-23-2010 |
20100244767 | MAGNETIC INDUCTIVE CHARGING WITH LOW FAR FIELDS - 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. | 09-30-2010 |
20120188158 | WEARABLE ELECTROMYOGRAPHY-BASED HUMAN-COMPUTER INTERFACE - 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. Examples of Wearable Electromyography-Based Controllers include articles of manufacture, such as an armband, wristwatch, or article of clothing having a plurality of integrated EMG-based sensor nodes and associated electronics. | 07-26-2012 |
20130232095 | RECOGNIZING FINGER GESTURES FROM FOREARM EMG SIGNALS - 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. | 09-05-2013 |
20140128994 | LOGICAL SENSOR SERVER FOR LOGICAL SENSOR PLATFORMS - 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. | 05-08-2014 |
20140129162 | BATTERY WITH COMPUTING, SENSING AND COMMUNICATION CAPABILTIES - 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. | 05-08-2014 |
20140129866 | AGGREGATION FRAMEWORK USING LOW-POWER ALERT SENSOR - 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. | 05-08-2014 |