Motor control system based upon movements inherent to self-propulsion
Inventors
Robinson, Mark Ries • Allen, Elena A
Assignees
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Publication Number
US-10427765-B2
Publication Date
2019-10-01
Expiration Date
Abstract
The systems and methods described herein provide hands free motor control mechanisms based on the natural and inherent movements associated with an activity of interest, and can be combined with gesture communication based upon defined movements by the participant.
Core Innovation
The invention provides hands free operation of a motor control system for cadence based self-propulsion activities by using activity associated movement signals inherent to the activity. A participant-attached sensor system measures one or more movements of the participant, and an analysis system determines an activity state and a measure of activity cadence from the acquired activity associated movement signals.
The determined activity state and the measure of activity cadence are used to determine an amount of motor assistance. A control system then provides motor assistance according to the amount of motor assistance determined from the activity state and the activity cadence measure, thereby controlling an amount of motor assistance during the participant’s current activity.
For activity state and cadence determination, the acquisition uses motion information concerning motion of the participant, including sensor outputs from an accelerometer, a gyroscope, and an inertia measurement device, and/or motion information from an image capture system including video or 3D camera data. The document further characterizes reducing non-indicative environmental information through frequency-based processing and common noise reduction/common-mode cancellation, and it includes activity recognition refinements such as dynamic time warping for speed-invariant recognition and magnetometer-based direction and stage refinement.
The document describes embodiments that optionally include gesture control signals and voice commands with a wake word to further influence the determination of the motor assistance amount. Example system architectures include a participant-attached sensor system communicating wirelessly, safety stop behavior based on loss of communications and sensing conditions, and camera-based activity monitoring using optical and image analysis to support activity type/state detection and cadence measurement.
Claims Coverage
The document provides four independent claims that collectively cover methods and systems for hands free motor control of cadence-based self-propulsion activities, with inventive features focused on acquiring activity associated movement signals, determining an activity state and activity cadence, and mapping these to a motor assistance amount, optionally using additional input modalities such as gesture recognition and voice command recognition.
Cadence-based motor assistance from activity type and cadence
Acquiring motion information concerning motion of the participant; determining a type of activity from the acquired information; determining a measure of activity cadence from the acquired information; determining an amount of motor assistance to provide from the type of activity and from the measure of activity cadence; and providing motor assistance according to the amount of motor assistance determined.
Participant-attached sensor system for current activity and cadence motor control
A participant-attached sensor system configured to measure one or more movements of the participant; a system configured to determine activity associated movement signals; and a system configured to determine a current activity of the participant and a cadence of a propulsion motion of the participant from the activity associated movement signals, and to control the amount of motor control provided based on the determines current activity of the participant and cadence of a propulsion motion of the participant.
Hands free cadence analysis to motor control signal
A sensor system attached to the participant, the apparatus, or a combination thereof; an analysis system configured to determine a cadence of the participant's self-propulsion activity from the output of the sensor system; and a control system configured to determine a motor control signal based upon the determined cadence.
Motor assistance from activity state and activity cadence measure
Acquiring activity associated movement signals from motions of the participant that are inherent to the activity; determining an activity state from the acquired activity associated movement signals; determining a measure of activity cadence from the acquired activity associated movement signals; determining the amount of motor assistance to provide from the activity state and from the measure of activity cadence; and providing motor assistance according to the amount of motor assistance determined.
Across the independent claims, the core coverage is the use of inherent activity associated movement signals to determine an activity state or type and a cadence measure, and then to determine and provide an amount of motor assistance or a motor control signal based on those determinations. Additional claim coverage includes participant-attached sensing and may include gesture recognition and voice command recognition to influence motor control amount determination.
Stated Advantages
Hands free operation of a motor control system for cadence-based self-propulsion activities.
Controls an amount of motor assistance based on the participant’s type/current activity and the measure of activity cadence.
Determines a motor control signal based upon a determined cadence from sensor outputs.
Documented Applications
Cadence-based self-propulsion activities such as surfing, where inherent participant motion is used for hands-free motor assistance based on activity cadence.
Demonstrations include wrist IMU cadence estimation and pop-up signatures, and vision-based activity recognition using a GoPro with edge/black-and-white processing and optical flow for paddling cadence timing and surfing state detection.
Systems are described for example architectures including wrist/board sensors with wireless communication and camera-mounted board sensing, with safety stop behavior based on loss of communications and sensing conditions.
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