US 8,977,522 B2 · System and method for observing a person's walking activity

A system for observing a walking activity of a person, comprising:

a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer, for delivering at output, for a footstep of the person:
a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat,
a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and
an angular speed of the corresponding tibia at the instant at which the foot is laid flat;
said system further comprising an analysis module communicatively coupled to the device, the analysis module comprising a processor configured to analyze signals delivered by the device and to determine, only from an analysis of these signals, a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking;
wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the analysis module is further configured to determine the type of walking of the user from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for walking on the flat surface, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−1.2;
−1], μ2 ε[0.25;
0.35], and μ3 ε[−0.3;
−0.2], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
510−2┘, Σ2 ε└10−4;
510−3┘, and Σ3 ε└10−4;
510−2┘.

60 more elements

A system for observing a walking activity of a person, comprising:

a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer, for delivering at output, for a footstep of the person:
a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat,
a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and
an angular speed of the corresponding tibia at the instant at which the foot is laid flat;
said system further comprising an analysis module communicatively coupled to the device, the analysis module comprising a processor configured to analyze signals delivered by the device and to determine, only from an analysis of these signals, a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking;
wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the analysis module is further configured to determine the type of walking of the user from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for walking down a descent, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−1.4;
−1], μ2 ε[0.05;
0.25], and μ3 ε[−0.7;
−0.2], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
510−2┘, Σ2 ε└10−3;
10−2┘, and Σ3 ε└10−4;
510−2┘.

60 more elements

A system for observing a walking activity of a person, comprising:

a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer, for delivering at output, for a footstep of the person:
a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat,
a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and
an angular speed of the corresponding tibia at the instant at which the foot is laid flat;
said system further comprising an analysis module communicatively coupled to the device, the analysis module comprising a processor configured to analyze signals delivered by the device and to determine, only from an analysis of these signals, a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking;
wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the analysis module is further configured to determine the type of walking of the user from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for walking up a climb, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−1.1;
−0.2], μ2 ε[0.1;
0.2], and μ3 ε[−1.2;
−0.6], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
510−2┘, Σ2 ε└10−3;
10−2┘, and Σ3 ε└10−3;
510−2┘.

60 more elements

A system for observing a walking activity of a person, comprising:

a device comprising one or more of a magnetometer, an accelerometer, and a pyrometer, for delivering at output, for a footstep of the person:
a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat,
a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and
an angular speed of the corresponding tibia at the instant at which the foot is laid flat;
said system further comprising an analysis module communicatively coupled to the device, the analysis module comprising a processor configured to analyse-analyze signals delivered by the device and to determine, only from an analysis of these signals, a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking;
wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

Σ

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
Σ
-
1
⁡
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the analysis module is further configured to determine the type of walking of the user from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for climbing a staircase, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−0.4;
−0.15], μ2 ε[0;
0.2], and μ3 ε[−0.7;
−0.4], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−2;
10−1┘, Σ2 ε└510−3;
510−2┘, and Σ3 ε└10−2;
10−1┘.

60 more elements

A system for observing a walking activity of a person, comprising:

a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer, for delivering at output, for a footstep of the person:
a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat,
a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and
an angular speed of the corresponding tibia at the instant at which the foot is laid flat;
said system further comprising an analysis module communicatively coupled to the device, the analysis module comprising a processor configured to analyze signals delivered by the device and to determine, only from an analysis of these signals, a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking;
wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the analysis module is further configured to determine the type of walking of the user from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for descending a staircase, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−0.8;
−0.6], μ2 ε[−0.3;
−0.1], and μ3 ε[−0.4;
−0.25], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
10−2┘, Σ2 ε└10−4;
10−3┘, and Σ3 ε└10−4;
10−3┘.

60 more elements

A method for observing a walking activity of a person, based on, during a footstep, signals representing a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat, a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and an angular speed of the corresponding tibia at the instant at which the foot is laid flat, said signals being provided as an output of a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer,

wherein a computing device is used to analyse said signals to determine a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking, and wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the type of walking of the user is determined from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for walking on the flat surface, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−1.2;
−1], μ2 ε[0.25;
0.35], and μ3 ε[−0.3;
−0.2], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
510−2┘, Σ2 ε└10−4;
510−3┘, and Σ3 ε└10−4;
510−2┘.

55 more elements

A method for observing a walking activity of a person, based on, during a footstep, signals representing a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat, a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and an angular speed of the corresponding tibia at the instant at which the foot is laid flat, said signals being provided as an output of a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer,

wherein a computing device is used to analyse said signals to determine a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking, and wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the type of walking of the user is determined from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for walking down a descent, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−1.4;
−1], μ2 ε[0.05;
0.25], and μ3 ε[−0.7;
−0.2], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
510−2┘, Σ2 ε└10−3;
10−2┘, and Σ3 ε└10−4;
510−2┘.

55 more elements

A method for observing a walking activity of a person, based on, during a footstep, signals representing a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat, a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and an angular speed of the corresponding tibia at the instant at which the foot is laid flat, said signals being provided as an output of a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer,

wherein a computing device is used to analyse said signals to determine a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking, and wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the type of walking of the user is determined from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for walking up a climb, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−1.1;
−0.2], μ2 ε[0.1;
0.2], and μ3 ε[−1.2;
−0.6], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
510−2┘, Σ2 ε└10−3;
10−2┘, and Σ3 ε└10−3;
510−2┘.

55 more elements

A method for observing a walking activity of a person, based on, during a footstep, signals representing a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat, a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and an angular speed of the corresponding tibia at the instant at which the foot is laid flat, said signals being provided as an output of a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer,

wherein a computing device is used to analyse said signals to determine a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking, and wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the type of walking of the user is determined from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for climbing a staircase, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−0.4;
−0.15], μ2 ε[0;
0.2], and μ3 ε[−0.7;
−0.4], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−2;
10−1┘, Σ2 ε└510−3;
510−2┘, and Σ3 ε└10−2;
10−1┘.

55 more elements

A method for observing a walking activity of a person, based on, during a footstep, signals representing a first difference of angular speeds of a corresponding tibia between an instant at which a heel of a foot is planted and an instant at which the foot is laid flat, a second difference of angular speeds of the corresponding tibia between the instant at which the heel of the foot is planted and an instant at which a last toe of the foot is lifted, and an angular speed of the corresponding tibia at the instant at which the foot is laid flat, said signals being provided as an output of a device comprising one or more of a magnetometer, an accelerometer, and a gyrometer,

wherein a computing device is used to analyse said signals to determine a type of walking of the user as a function of time by using a hidden Markov model with N states corresponding respectively to N types of walking, and wherein:
a probability density px(x(n)) of correspondence between the signals delivered by the device and a state of the hidden Markov model representing a type of walking is defined by the following first expression:
1
2
⁢
⁢
π
⁢

∑

·
ⅇ
-
(
x
_
⁡
(
n
)
-
μ
_
)
T
⁢
∑
-
1
⁢
(
x
_
⁡
(
n
)
-
μ
_
)
2
in which:
x(n) represents a column vector with components of three signals transmitted by the device;
μ represents a column vector with three components μ1, μ2, μ3, representative of the state of the hidden Markov model corresponding to said type of walking;
and
|Σ| represents an absolute value of a determinant of a diagonal matrix Σ of dimension 3 representative of the state of the hidden Markov model corresponding to said type of walking;
the probability density of correspondence between the signals delivered by the device and the state of the hidden Markov model representing the type of walking is defined by a linear combination of said first expressions for probability densities of the set of types of walking, a sum of the coefficients of said linear combination being equal to 1;
the type of walking of the user is determined from among a set of at least two types of walking comprising walking on a flat surface, walking down a descent, walking up a climb, climbing a staircase, and descending a staircase;
and
for descending a staircase, the three components μ1, μ2, μ3 of the column vector μ are such that μ1 ε[−0.8;
−0.6], μ2 ε[−0.3;
−0.1], and μ3 ε[−0.4;
−0.25], and three diagonal components Σ1, Σ2, Σ3 of the diagonal matrix Σ are such that Σ1 ε└10−3;
10−2┘, Σ2 ε└10−4;
10−3┘, and Σ3 ε└10−4;
10−3┘.

55 more elements

System and method for observing a person's walking activity

Life of this patent

System and method for observing a person's walking activity

Life of this patent

Abstract

Claims25