Verily Life Sciences LLC

The Procter Gamble Company

Anupam J Pathak

Dylan Owens

Ali Shoeb

Michael R Allen

Shannon Colleen Fong

Emre Demiralp

Timothy John Prachar

John A Redmond

Blanca Arizti

Sarel Kobus Jooste

Mark Murphy

Tushar Anil Parlikar

Scott Brian Kennedy

David He

Marty Gardner

Nikhil Bikhchandani

Hongsheng Wang

Michael Allen

Michael M Allen

A disclosed method includes receiving at least an indication of audio data generated based on sound captured by a microphone of a monitoring device. A set of audio event labels are generated by processing the received audio data with a model that includes audio recognition patterns. Each audio event label is generated by matching an audio pattern in the received audio data with an audio pattern of the audio recognition patterns. The method further includes identifying a hazard event type occurring within a time window of the time period by processing the set of audio event labels with a hazard detection model. In response to detecting the hazard event type, a hazard alert is generated and at least an indication of the hazard alert is communicated to an electronic device other than the monitoring device, which is authorized to receive communications from the monitoring device.

Recognizing hazard events based on sounds of in-home activities

A handheld system includes a housing, a motion-generating mechanism disposed within the housing, an attachment arm extending from the housing and having a first end coupled to the motion-generating mechanism and a second end configured to receive a user assistive device, a first sensor mounted to measure a first motion of the handheld system and generate a first signal indicative of the first motion, and a control system coupled to the motion-generating mechanism and the first sensor. The control system is configured to use the first signal to stabilize the user-assistive device and compensate for human tremors imparted to the housing.

System and method for stabilizing unintentional muscle movements

Systems and methods for tracking unintentional high amplitude muscle movements of a user and stabilizing a handheld tool are described. The method may include detecting motion of a housing of the handheld tool when manipulated by a user while the user is performing a task with a user-assistive device attached to an attachment arm of the handheld tool, and storing the detected motion in a memory of the handheld tool as motion data. Furthermore, the method may include controlling a first motion generating mechanism and a second motion generating mechanism by generating a first motion signal and a second motion signal that respectively drive the first motion generating mechanism in a first degree of freedom and the second motion generating mechanism in a second degree of freedom to stabilize motion of the user-assistive device attached to the attachment arm of the handheld tool.

High amplitude tremor stabilization by a handheld tool

A handheld tool includes a handle; an implement mount configured to detachably accept and to rigidly hold a user-assistive implement; an actuator assembly mounted to the handle to physically manipulate the implement mount relative to the handle; a first sensor disposed to sense an orientation of the handle; a second sensor disposed to sense an orientation of the user-assistive implement; a controller disposed in or on the handle and coupled to the actuator assembly and the first and second sensors; and memory coupled to the controller. The memory stores instructions for identifying a type of the user-assistive implement attached to the implement mount, selecting a behavior routine based upon the type of the user-assistive implement identified, and manipulating the user-assistive implement relative to the handle according to the behavior routine to aid performance of a task with the handheld tool.

Handheld articulated user-assistive device with behavior control modes

Various examples are described for using a movement sensor to detecting an activity of an infant. In an example, an activity classification system includes a sensor configured to measure the activity of an infant and an external monitor. The monitor receives, from a sensor, a time series of data comprising an inertial measurement for each a time period. The monitor determines, from the time series and by using a predictive model, an activity from a list of identified activities. Examples of identified activities are deep sleep, light sleep, sitting, awake, nursing, or bottle feeding.

Sensing and activity classification for infants

A method of tremor reduction in a handheld device includes measuring a tremor motion and a tilt motion with a motion tracking module ('MTM') disposed in a housing of the handheld device. In response to measuring the tremor motion, an attachment arm is moved with at least one motion generating mechanism to reduce the tremor motion in the attachment arm. Additionally, in response to measuring the tilt motion, the attachment arm is moved with the at least one motion generating mechanism to resist the attachment arm hitting a hard stop in the handheld device.

Tilt compensation for tremor cancellation device

Systems and methods for tracking unintentional muscle movements of a user and stabilizing a handheld tool while it is being used are described. The method may include measuring, with a first inertial measuring unit ('IMU'), at least an orientation of a housing of the handheld tool, and measuring, with a second IMU, at least an orientation or an attachment arm extending from the housing of the handheld tool. The method may also include storing the orientation of the housing and the orientation of the attachment arm in a memory. Furthermore, the method may include controlling, with a processing logic, a first motion generating mechanism and a second motion generating mechanism to move the attachment arm relative to the housing based on the measured orientation of the housing and the measured orientation of the attachment arm to stabilize motion of a user-assistive device attached to a distal end of the attachment arm.

Method and system for a feedback controller for a handheld tool

Systems and methods for tracking unintentional muscle movements of a user and stabilizing a handheld tool while it is being used by the user are described. The method may include detecting motion of a handle of the handheld tool manipulated by a user while the user is performing a task with a user-assistive device attached to an attachment arm of the handheld tool. Furthermore, the method may include storing the detected motion in a memory of the handheld tool as motion data. The method may also include controlling, based on the motion data, a motion-generating mechanism of the handheld tool that moves the attachment arm relative to the handle in a degree of freedom.

Motion stabilization by a handheld tool

A method predicts the fall risk of a user based on a machine learning model. The model is trained using data about the user, which may be from wearable sensors and depth sensors, manually input by the user, and received from other types of sources. Data about a population of users and data from structured tests completed by the user can also be used to train the model. The model uses features and motifs discovered based on the data that correlate to fall risk events to update fall risk scores and predictions. The user is provided a recommendation describing how the user can reduce a predicted fall risk for the user.

Adaptive model-based system to automatically quantify fall risk

Embodiments are disclosed of an adapter for coupling an implement to a tremor cancellation platform. The adapter includes a first section attached to the tremor cancellation platform, the first section comprising an elongated tapered key having a base and a tip, the base having a greater transverse dimension than the tip and the base being attached to the tremor cancellation platform. The adapter also includes a second section coupled to the implement, the second section including a tapered cavity sized and shaped to receive the first section, and a latch to lock the second section onto the first section.

Adapter to attach implements to an actively controlled human tremor cancellation platform

A handheld tool includes a handle for holding by a user, an attachment arm extending from the handle that is configured to connect to a user-assistive device, a first inertial measurement unit ('IMU') mounted to the attachment arm to acquire measurements of one or more of a motion or an orientation of the user-assistive device and to generate feedback data indicative of the measurements, an actuator assembly coupled to manipulate the user-assistive device via the attachment arm in at least two orthogonal dimensions, and a motion control system coupled to receive the feedback data from the first IMU and coupled to provide commands to the actuator assembly to provide auto-leveling of the user-assistive device to a frame of reference while the user manipulates the handheld tool.

Handheld tool for leveling uncoordinated motion

Embodiments regard nutrition assessment using a handheld device. An embodiment of an apparatus includes a handle with a controller within the handle, an attachment arm extending from the handle, and a user-assistive device coupled with an end of the attachment arm, wherein the apparatus is to determine a mass held by the user-assistive device, the determination being made during a task by a user of the handheld tool including manipulation of the handheld tool.

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Mountain View, CA, United States of America

Anupam J Pathak