US 12,295,724 B2
Systems and methods for automated tremor management, tracking and recommendations
Joon Faii Ong, Hertfordshire (GB); Benjamin T. J. Koh, Hertfordshire (GB); Youssef Ibrahim, Hertfordshire (GB); Elliott Baxter, Hertfordshire (GB); Gordon McCabe, Hertfordshire (GB); Paul de Panisse Passis, Hertfordshire (GB); and Jon Barrenetxea Carrasco, Hertfordshire (GB)
Assigned to GyroGear Limited, Hertfordshire (GB)
Filed by GyroGear Limited, Hertfordshire (GB)
Filed on Aug. 3, 2021, as Appl. No. 17/393,136.
Application 17/393,136 is a continuation in part of application No. PCT/GB2021/051983, filed on Jul. 30, 2021.
Claims priority of provisional application 63/060,622, filed on Aug. 3, 2020.
Prior Publication US 2022/0031194 A1, Feb. 3, 2022
Int. Cl. A61B 5/00 (2006.01); A61B 5/11 (2006.01)
CPC A61B 5/1101 (2013.01) [A61B 5/0015 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 5/742 (2013.01); A61B 5/746 (2013.01); A61B 2562/0219 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a processor, an accelerometer data signal from a sensor device associated with a user, the accelerometer data signal having a time-varying frequency and a time-varying amplitude of motion detected by the sensor device;
wherein the sensor device is associated with a wearable tremor management device configured to be worn by the user;
wherein the tremor management device comprises at least one actuator configured to provide a tremor mitigation effect comprising at least one of:
a counteracting force or
motion resistance
inputting, by the processor, the time-varying frequency and the time-varying amplitude of motion detected by the sensor device into a tremor severity model to output a tremor severity pattern prediction based at least in part on trained tremor severity model parameters;
wherein the trained tremor severity model comprises at least one of:
at least one artificial intelligence model, or
at least one machine learning model;
wherein the trained tremor severity model parameters are trained based on:
inputting at least one historical tremor property associated with at least one historical tremor into the tremor severity model to output a historical tremor severity pattern prediction; and
updating the trained tremor severity model parameters based at least in part on the historical tremor severity pattern prediction and a historical tremor severity;
generating, by the processor, at least one device instruction feedback to modify device control of the tremor management device based at least in part on the tremor severity pattern prediction;
determining, by the processor, a tremor in the accelerometer data signal based at least in part on the time-varying frequency and a tremor frequency range indicative of movement associated with tremors;
determining, by the processor, a tremor frequency and a tremor amplitude of the tremor based on the accelerometer data signal during a period of time associated with the tremor;
generating, by the processor, based on the at least one device instruction feedback, a mechanical dampening control signal to the tremor management device to control the at least one actuator of the tremor management device to create the tremor mitigation effect on the user in response to the tremor frequency and the tremor amplitude so as to counteract the tremor without impeding intentional motion.