US 2018/0008190 A1Published application

Systems, methods, and devices for determining endpoints of a rest period using motion data

AssigneeKoninklijke Philips N.V.

InventorsBarry PETERSON, Arnaud MOREAU, Peter ANDERER, Marco ROSS

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Abstract

Systems, methods, and devices for determining a temporal duration of a rest period using motion data are described herein. In one exemplary embodiment, one or more data filters are applied to received motion data to generate one or more data sets of the motion data. The motion data represents an amount of activity experienced by an individual over the course of a period of time, such as one day. An iterative process is performed to identify a starting point and an ending point of a rest period using the generated data set(s). After the starting and ending points are identified, a temporal difference between the starting and ending points is calculated, and a total temporal duration of the rest period is determined.

Claims20

Dependent claims

A method for identifying a rest period of an individual, the method comprising:

setting a threshold activity level for the rest period;
receiving motion data;
applying a first data filter to the motion data;
generating a first data set of the motion data;
determining a minimum point within the first data set;
determining a minimum time associated with the minimum point;
determining a first upper intersection point where the first data set intersects with the threshold activity level, wherein the first upper intersection point occurs after the minimum time;
determining a first upper time associated with the first upper intersection point;
determining a first position on the motion data corresponding to the first upper time;
determining a raw data upper intersection point where the motion data intersects with the threshold activity level;
determining a raw data upper time associated with the raw data upper intersection point, wherein the raw data upper time occurs after to the first upper time within the rest period;
setting the raw data upper time as an upper boundary of the rest period;
determining a first lower intersection point where the first data set intersects with the threshold activity level, wherein the first lower intersection point occurs prior to the minimum time;
determining a first lower time associated with the first lower intersection point;
determining a second position on the motion data corresponding to the first lower time;
determining a raw data lower bound where the motion data intersects with the threshold activity level;
determining a raw data lower time associated with the raw data lower bound;
setting the raw data lower time as a lower boundary of the rest period;
and
determining an amount of time of the rest period by calculating a temporal difference between the raw data upper time and the raw data lower time.

17 more elements

A system comprising:

a wearable motion tracker comprising at least one motion sensor;
a motion analysis device comprising:
communications circuitry that receives motion data from the wearable motion tracker;
memory that stores the motion data;
and
at least one processor configured to:
set a threshold activity level for a rest period;
apply a first data filter to the motion data;
generate a first data set of the motion data;
determine a minimum point within the first data set;
determine a minimum time associated with the minimum point;
determine a first upper intersection point where the first data set intersects with the threshold activity level, wherein the first upper intersection point occurs after the minimum time;
determine a first upper time associated with the first upper intersection point;
determine a first position on the motion data corresponding to the first upper time;
determine a raw data upper intersection point where the motion data intersects with the threshold activity level;
determine a raw data upper time associated with the raw data upper intersection point, wherein the raw data upper time occurs after the first upper time within the rest period;
assign the raw data upper time as being an upper boundary of the rest period;
determine a first lower intersection point where the first data set intersects with the threshold activity level, wherein the first lower intersection point occurs prior to the minimum time;
determine a first lower time associated with the first lower intersection point;
determine a second position on the motion data corresponding to the first lower time;
determine a raw data lower bound where the motion data intersects with the threshold activity level;
determine a raw data lower time associated with the raw data lower bound;
assign the raw data lower time as being a lower boundary of the rest period;
and
determine an amount of time of the rest period by calculating a temporal difference between the raw data upper time and the raw data lower time.

22 more elements

A wearable motion tracker 0-54, comprising:

at least one motion sensor that captures motion data representative of an amount of activity detected during each epoch within a time period;
memory that stores the motion data;
and
at least one processor configured to:
set a threshold activity level for a rest period;
apply a first data filter to the motion data;
generate a first data set of the motion data;
determine a minimum point within the first data set;
determine a minimum time associated with the minimum point;
determine a first upper intersection point where the first data set intersects with the threshold activity level, wherein the first upper intersection point occurs after the minimum time;
determine a first upper time associated with the first upper intersection point;
determine a first position on the motion data corresponding to the first upper time;
determine a raw data upper intersection point where the motion data intersects with the threshold activity level;
determine a raw data upper time associated with the raw data upper intersection point, wherein the raw data upper time occurs after the first upper time within the rest period;
assign the raw data upper time as being an upper boundary of the rest period;
determine a first lower intersection point where the first data set intersects with the threshold activity level, wherein the first lower intersection point occurs prior to the minimum time;
determine a first lower time associated with the first lower intersection point;
determine a second position on the motion data corresponding to the first lower time;
determine a raw data lower bound where the motion data intersects with the threshold activity level;
determine a raw data lower time associated with the raw data lower bound;
assign the raw data lower time as being a lower boundary of the rest period;
and
determine an amount of time of the rest period by calculating a temporal difference between the raw data upper time and the raw data lower time.

20 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/544,144Filed 4 Mar 2016
Priority: 6 Mar 2015
Published: 11 Jan 2018
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G16H 20/30
ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient…:…
A61B 5/1118
Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb: determining activity level
A61B 5/4809
Sleep evaluation: sleep detection, i.e. determining whether a subject is asleep or not
G06V 40/20
Recognition of biometric, human-related or animal-related patterns in image or video data: movements or behaviour, e.g. gesture recognition