System and method for determining the activity of a mobile element

Patent metadata

Field	Value
Publication number	US-9489600-B2
Application number	US-201013266094-A
Country	US
Kind code	B2
Filing date	Apr 26, 2010
Priority date	Apr 24, 2009
Publication date	Nov 8, 2016
Grant date	Nov 8, 2016

Abstract

A system for determining activity of a mobile element, includes at least one motion sensor having a measurement axis, and which is securely connected to the mobile element. A filter for selects, for each measurement axis of the motion sensor, high frequencies above a first threshold, and processing equipment for determines a unidimensional high-frequency component (y(n)) equal to the square of the Euclidean norm of the high frequencies along the measurement axes of the motion sensor. A calculator calculates, for each state, probability density functions (Py,i) of the high-frequency component, the probability density function corresponding to each state according to a Chi-square law with a degree of freedom equal to the number of measurement axes of the motion sensor. An analyzer defines states of the mobile element, utilizing the probability density function of the high-frequency component for each state, together with the probabilities of transitions between two successive states.

First claim

The invention claimed is:

1. A method for determining activity of a user having a mobile element coupled thereto, the mobile element comprising at least one motion sensor comprising an accelerometer having at least two measurement axes, the method comprising:
using the mobile element to measure movement of the user along the at least two axes to generate a corresponding signal S(n) by the motion sensor at discrete times from zero to N when the mobile element is in a state E(n) characterizing a posture of the user;
filtering the signal S(n) to obtain a first component y(n) of high-frequency portions of the signal S(n), said high frequency portions being above a first frequency threshold on each axis of the motion sensor, and a second component x(n) of low frequency portions of the signal S(n), said low frequency portions being below a second frequency threshold lower than the first frequency threshold on at least one axis of the motion sensor;
calculating by a processor a combination θ(n) of a norm of the first component and a contribution of the second component;
determining by the processor a list of the most likely states E(k i ) characterizing the postures of the user during a period between time zero and N from a calculation of:
a probability function of obtaining θ(n) from the signal S(n) when the mobile element is in the E(n) state;
a probability function of a transition from the E(n−1) state to the E(n) state; and
probability functions of each state of the mobile element at time zero; and
outputting a most likely activity of the user as a sequence of most likely postures.

2. The method of claim 1 , wherein the first component y(n) is a unidimensional component equal to a square of a norm of said high frequencies portions of the signal S(n) which are taken into account from k measurement axes of the motion sensor, and the probability function of said first component is a probability density function which is defined by a Chi-square law with a degree of freedom equal to k.

3. The method of claim 2 , wherein the probability density function (P y ) is defined, when the mobile element is in the state i, by the following expression:

P
y

⁢

,
i

⁢

(

y
⁡

(
n
)

)

=

1

2
k

⁢

σ

y
,
i

k

⁢

Γ
⁡

(

k
2

)

⁢

y
⁡

(
n
)

k
2

-
1

⁢

ⅇ

-

y
⁡

(
n
)

2
⁢

σ

y
,
i

2

in which:
σ y,i is a quantity proportional to a time average of the first component y(n) in the state i; and
Γ is the gamma function satisfying

Γ
⁡

(

1
2

)

=

π

,

Γ
⁡

(
1
)

=

1
⁢

⁢
and
⁢

⁢

Γ
⁡

(

z
+
1

)

=

z
⁢

⁢

Γ
⁡

(
z
)

 for real z.

4. The method of claim 1 , wherein the low-frequency portions are selected for each measurement axis of the motion sensor and the second component x(n) is of a dimension equal to the number of measurement axes of the motion sensor.

5. The method of claim 1 , wherein the low frequency portions are selected for each measurement axis of the motion sensor and the second component x(n) is unidimensional.

6. The method of claim 1 , wherein a probability density function (P(x(n),y(n))) for obtaining a pair of values ((x(n), y(n)) is calculated for the second component x(n) and the first component y(n), P(x(n),y(n)) being equal to a product of a probability density function of the second component x(n) and the probability density function of the fi

Assignees

• Jallon Pierre
• Commissariat Energie Atomique
• Movea

Inventors

• Jallon Pierre

Classifications

• G06F18/295

  Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models · CPC title

• A61B5/11 Primary

  Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)} · CPC title

• A61B5/1101 Primary

  Detecting tremor · CPC title

• A61B2562/0219

  Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches · CPC title

• G06K9/6297 Primary

  Physics · mapped topic