Microsoft Technology Licensing, LLC

Dan Morris

Desney S Tan

T Scott Saponas

Shwetak N Patel

Nicolas Villar

Greg R Smith

Jim Turner

Ravin Balakrishnan

Sumit Basu

Sidhant Gupta

Scott Saponas

Ian Simon

Orestis Vardoulis

Meredith June Morris

Christopher Harrison

Rebecca Fiebrink

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.

Wearable pulse pressure wave sensing device

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.

Wearable sensing band

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. 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.

Wearable electromyography-based human-computer interface

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.

Recognizing finger gestures from forearm EMG signals

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.

Magnetic inductive charging with low far fields

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.

Recognizing gestures from forearm EMG signals

Described is using the human body as an input mechanism to a computing device. A sensor set is coupled to part of a human body. The sensor set detects mechanical (e.g., bio-acoustic) energy transmitted through the body as a result of an action/performed by the body, such as a user finger tap or flick. The sensor output data (e.g., signals) are processed to determine what action was taken. For example, the gesture may be a finger tap, and the output data may indicate which finger was tapped, what surface the finger was tapped on, or where on the body the finger was tapped.

Sensing mechanical energy to appropriate the body for data input

Physical controls on a physical controller device (PCD) are dynamically mapped to application controls for an application being executed on a computer having a touch-sensitive display surface. The computer identifies a PCD which has been placed by a user on the display surface and displays a mapping aura for the PCD. When the user touches an activate direct-touch button displayed within the mapping aura, the computer activates a mapping procedure for the PCD and displays a highlighted direct-touch button over each application control which is available to be mapped to the physical controls on the PCD. When the user selects a particular application control which is available to be mapped by touching the highlighted button residing over the control, the computer creates a dynamic mapping between the selected application control and a user-selected physical control on the PCD.

Mapping of physical controls for surface computing

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.

Wearable electromyography-based controllers for human-computer interface

A graphical user interface for facilitating generation of an accompaniment for a recorded audio melody is described. A Hidden Markov Model, trained with blended chord transition matrices and melody observation matrices, is used for providing the accompaniment for the recorded audio melody. The recorded audio melody includes segments. Frequency analysis of the recorded audio melody is performed. The total duration of each fundamental frequency within a segment of the melody is summed. Based on the summing, a probability for each possible chord for each segment is computed. Based on the computed probabilities, a set of chords are selected for the segments. The chords are displayed on a chord chart of the graphical user interface. The graphical user interface facilitates various manipulations using the chords and/or controls, and generation of a new accompaniment for a recorded audio melody based on the manipulations.

Automatic accompaniment for vocal melodies

A method for generating an accompaniment for a recorded audio melody includes providing a recorded audio melody that includes segments; performing a frequency analysis of the recorded audio melody; summing the total duration of each fundamental frequency within a segment of the melody; based on the summing, computing a probability for each possible chord for each segment; based on the computed probabilities, selecting a set of chords for the segments; and outputting the set of chords as an accompaniment for the recorded audio melody. Various other methods, devices, systems, etc. are also disclosed.

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Bellevue, WA, United States of America

Dan Morris