Microsoft Technology Licensing, LLC

Microsoft Technology Licnesing, LLC

T Scott Saponas

Desney S Tan

Daniel Morris

Dan Morris

Shwetak N Patel

Nicolas Villar

Sidhant Gupta

Gregory Ray Smith

Dennis W Tom

Farah Shariff

Greg R Smith

Ilya Kelner

Siddharth Khullar

Andrew Guillory

Neel Suresh Joshi

Sumit Basu

Gabriel Adam Cohn

Sailaja Malladi

Ronald E Paulsen

Jim Turner

Orestis Vardoulis

Oscar Beijbom

Alice Jane Bernheim Brush

Ravin Balakrishnan

Jonathan Bernard Lester

David Charles Kale

Jeremiah Wander

Morgan Dixon

Harshvardhan Vathsangam

Jason Wiese

Dennis Tom

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 first data window of a pulse waveform signal comprising a first number of samples is analyzed to determine a level of confidence that a pulse sensing device is placed correctly. If an initial level of confidence is met, the user is given positive feedback, and a second data window of a pulse waveform signal comprising a second, larger number of samples is analyzed. If an increased level of confidence is met, the user is given increased positive feedback. If a level of confidence is not met, the user is given negative feedback. If a final level of confidence is met, the user is given feedback that the pulse sensing device is placed correctly.

Wearable pulse sensing device signal quality estimation

A wrist-worn device heart-monitoring device is presented. The wrist-worn heart-monitoring device includes a radial tonometer configured to output a pressure signal indicating a pulse pressure wave at a user's wrist, two or more electrodes configured to output an electrical signal indicating a user's heart has been commanded to contract, and a microphone configured to output an audio signal indicating a closing of a user's aortic valve. The wrist-worn heart-monitoring device further includes a pulse transit time monitor configured to calculate a pre-ejection period of the user's heart based on at least the pressure, electrical, and audio signals, and calculate a pulse transit time based on at least the pre-ejection period, the pressure signal, and the electrical signal.

Wrist-worn pulse transit time sensor

A wrist-worn pressure sensing device includes a pressure sensor. The wrist-worn pressure sensing device also includes a first strap that sets the position of the pressure sensor on a wearer's wrist and a second strap that engages with the first strap to adjust the overall length of the strap without moving the set position of the pressure sensor on the wearer's wrist.

Sizable wrist-worn pressure sensing device

A 'Food Logger' provides various approaches for learning or training one or more image-based models (referred to herein as 'meal models') of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with &#x201c;meal features&#x201d; that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Food logging from images

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

Restaurant-specific food logging from images

A system and method to determine pulse transit time using a handheld device. The method includes generating an electrocardiogram (EKG) for a user of the handheld device. Two portions of the user's body are in contact with two contact points of the handheld device. The method also includes de-noising the EKG to identify a start time when a blood pulse leaves a heart of the user. The method further includes de-noising a plurality of video images of the user to identify a pressure wave indicating an arterial site and a time when the pressure wave appears. Additionally, the method includes determining the PTT based on the de-noised EKG and the de-noised video images.

Determining pulse transit time non-invasively using handheld devices

A 'Placement Detector' enables handheld or mobile electronic devices such as phones, media players, tablets, etc., to infer their current position or placement. Placement inference is performed by evaluating one or more sensors associated with the device relative to one or more trained probabilistic models to infer device relative to a user. Example placement inferences include, but are not limited to, inferring whether the device is currently in a user's pocket, in a user's purse (or other carrying bag or backpack), in a closed area such as a drawer or box, in an open area such as on a table, indoors, outdoors, etc. These types of placement inferences facilitate a wide range of automated user-device interactions, including, but not limited to, placement-dependent notifications, placement-dependent responses to various inputs, prevention of inadvertent 'pocket dialing,' prevention of inadvertent power cycling of devices, lost or misplaced device location assistance, etc.

Inferring placement of mobile electronic devices

A physical activity monitoring device includes a sensor array with one or more sensors configured to measure physical activity attributes of a user. A controller automatically determines time intervals where the user is actively engaged in a physical activity based on the physical activity attributes. The controller also automatically determines a type of physical activity the user in actively engaged in during the determined time intervals based on the physical activity attributes. A reporter outputs information regarding the type of physical activity to the user.

Automatic exercise segmentation and recognition

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 physical activity monitoring device receives an indication of one or more physical activities to be performed as an extension of a game being played on a game system and measures physical activity attributes of a user wearing the physical activity monitoring device. The physical activity monitoring device determines the user's progress towards completion of the one or more physical activities based on the physical activity attributes and outputs to the game device an indication of the user's progress towards completion of the one or more physical activities.

Extending gameplay with physical activity monitoring device

A physical activity monitoring device receives a workout regimen including a plurality of exercises. For each of the plurality of exercises, the physical activity monitoring device indicates that exercise to a user and measures physical activity attributes of the user. The physical activity monitoring device outputs information regarding the user's progress towards completion of that exercise based on the physical activity attributes.

Personal training with physical activity monitoring device

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.

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Woodinville, WA, United States of America

T Scott Saponas