US 11,890,468 B1
Neurostimulation systems with event pattern detection and classification
Jai Y. Yu, Burlingame, CA (US)
Assigned to Cala Health, Inc., San Mateo, CA (US)
Filed by Cala Health, Inc., Burlingame, CA (US)
Filed on Oct. 1, 2020, as Appl. No. 17/061,231.
Claims priority of provisional application 62/933,816, filed on Nov. 11, 2019.
Claims priority of provisional application 62/910,260, filed on Oct. 3, 2019.
Int. Cl. A61N 1/36 (2006.01); G16H 20/30 (2018.01); A61N 1/04 (2006.01); A61N 1/02 (2006.01); G16H 50/20 (2018.01); A61N 1/08 (2006.01)
CPC A61N 1/36031 (2017.08) [A61N 1/025 (2013.01); A61N 1/0456 (2013.01); A61N 1/0476 (2013.01); A61N 1/08 (2013.01); G16H 20/30 (2018.01); G16H 50/20 (2018.01)] 12 Claims
OG exemplary drawing
 
1. A wearable neurostimulation device for transcutaneously stimulating one or more peripheral nerves of a user, the device comprising:
one or more electrodes configured to generate electric stimulation signals;
one or more sensors configured to detect motion signals, wherein the one or more sensors are operably connected to the wearable neurostimulation device; and
one or more hardware processors configured to:
receive raw signals relating to device interaction events;
store the device interaction events into a data log;
perform an anomalous sequence detection analysis on entries of the data log;
perform an event sequence classification on entries of the data log;
determine at least one of an anomaly type and/or an anomaly score;
determine anomalous device function patterns or device usage patterns, wherein the anomalous device function patterns relate to determining or predicting a malfunction of the wearable neurostimulation device, and wherein the device usage patterns relate to abnormal usage of the wearable neurostimulation device by the user;
communicate information related to the anomalous device function patterns or the device usage patterns to the user or a third party;
wherein the device is non implantable, and
wherein the anomalous sequence detection analysis comprises utilizing Markov chains.