Detecting frailty and foot at risk using lower extremity motor performance screening

Patent metadata

Field	Value
Publication number	US-12402809-B2
Application number	US-201917052611-A
Country	US
Kind code	B2
Filing date	May 2, 2019
Priority date	May 4, 2018
Publication date	Sep 2, 2025
Grant date	Sep 2, 2025

Abstract

Lower extremity motion data may be received from one or more sensors. The lower extremity motion data may be used to quantify propulsion performance, which is used to calculate one or more gait characteristics of the lower extremity. A risk level of the lower extremity may be determined based, at least in part, on the calculated gait characteristics.

First claim

What is claimed is:

1. A method, comprising:
receiving, wirelessly over a network by a processor from one or more sensors of a first sensing device worn on a first lower extremity of a user and from one or more sensors of a second sensing device worn on a second lower extremity of the user, motion data regarding motion of the first lower extremity and the second lower extremity over a period of time, wherein:
the first sensing device is one of: (i) integrated into a first article of clothing worn on a first leg, ankle, or foot of the first lower extremity, (ii) attached directly to a skin of the first leg, ankle, or foot of the first lower extremity via adhesive or a strap, or (iii) implanted in the first leg, ankle, or foot of the first lower extremity;

quantifying, by the processor, a propulsion phase of a gait cycle of the lower extremity based, at least in part, on the received motion data to obtain propulsion phase characteristics of the lower extremity over the period of time;
calculating, by the processor, one or more gait characteristics based, at least in part on, the propulsion phase characteristics, the one or more gait characteristics including a propulsion efficiency of the user during the propulsion phase;
determining, by the processor, a risk level of the first or second lower extremity for one or more medical conditions based, at least in part, on:
a ratio of the propulsion efficiency to a maximum pressure of a forefoot of the first or second lower extremity during the propulsion phase, or
a ratio of the propulsion efficiency to a time integral of a pressure of the forefoot during the propulsion phase; and

providing, by the processor, one or more treatment options to reduce the risk level.

2. The method of claim 1 , wherein the motion data regarding motion of the first lower extremity and the second lower extremity comprises angular velocity data of the first leg of the first lower extremity or the second leg of the second lower extremity, and wherein quantifying the propulsion phase includes:
determining a stance phase of the gait cycle based, at least in part, on the motion data; and
determining an initiation of the propulsion phase based on an angular velocity of the first or second leg exceeding a predetermined angular velocity following a midpoint of the stance phase and preceding a termination of the propulsion phase.

3. The method of claim 1 , wherein the step of calculating the one or more gait characteristics comprises identifying the propulsion phase for a movement of the first or second lower extremity during a continuous walking bout of the user.

4. The method of claim 3 , wherein quantifying the propulsion phase comprises determining a duration of the propulsion phase.

5. The method of the claim 1 , wherein the one or more sensors of the first sensing device and the one or more sensors of the second sensing device are each integrated into a respective at least one of a shoe, an ankle foot orthosis, a sock, a sandal, an insole, an offloading, or a skin of a user.

6. The method of claim 1 , wherein determining a risk level of the first or second lower extremity comprises determining whether the first or second lower extremity is non-frail, pre-frail, or frail.

7. The method of claim 1 , wherein the one or more gait characteristics comprise at least one of:
a propulsion duration;
a propulsion acceleration;
a mid-stance speed;
a speed norm;
a toe-off speed; and
a mid-swing speed.

8. A system, comprising:
a first sensing device wearable on a first lower extremity of a user, the first sensing device comprising:
a first sensor configured to collect first motion data regarding motion of the first lower extremity over a period of time;
a first memory to store the first motion data collected by the first sensor; and
a first wireless transmitter configured to wirelessly transmit the first motion data stored in the first memory for risk level analysis of the first motion data,
wherein the first sensing device is configured to be one of: (i) integrated into a first article of clothing worn on a first leg, ankle, or foot of the first lower extremity, (ii) attached directly to a skin of the first leg, ankle, or foot of the first lower extremity via adhesive or a strap, or (iii) implanted in the first leg, ankle, or foot of the first lower extremity;

a second sensing device wearable on a second lower extremity of a user, the second sensing device comprising:
a second sensor configured to collect second motion data regarding motion of the second lower extremity over a period of time;
a second memory to store the second motion data collected by the second sensor; and
a second wireless transmitter configured to wirelessly transmit the second motion data stored in the second memory for risk level analysis of the second motion data; and

a processing station coupled to the first and second sensing devices, the processing station comprising a processor configured to perform operations comprising:
receiving, wirelessly over a network, the first and second motion data from the first and second wireless transmitters;
quantifying a propulsion phase based on the received first and second motion data to obtain propulsion phase characteristics of the first lower extremity and the second lower extremity over the period of time;
calculating one or more gait characteristics based, at least in part, from the propulsion phase characteristics, the one or more gait characteristics including a propulsion efficiency of the user during the propulsion phase;
determining a risk level of the first or second lower extremity for one or more medical conditions based, at least in part, on:
a ratio of the propulsion efficiency to a maximum pressure of a forefoot of the first or second lower extremity during the propulsion phase, or
a ratio of the propulsion efficiency to a time integral of a pressure of the forefoot during the propulsion phase; and

providing one or more treatment options to reduce the risk level.

9. The system of claim 8 , wherein the first and second motion data regarding motion of the first lower extremity and the second lower extremity of the user comprises kinematic data of the first leg of the first lower extremity and the second leg of the second lower extremity.

10. The system of claim 8 , wherein the step of calculating the one or more gait characteristics comprises identifying the propulsion phase for a movement of the first or second lower extremity during a continuous walking bout of the user.

11. The system of claim 10 , wherein quantifying the propulsion phase comprises determining a duration of the propulsion phase.

12. The system of the claim 8 , wherein each of the first sensing device and the second sensing device is integrated into a respective at least one of a shoe, an ankle foot orthoses, a sock, an insole, a sandal, an offloading, or a skin of the user.

13. The system of claim 8 , wherein determining a risk level of the first lower extremity or the second lower extremity comprises determining whether the first lower extremity or the second lower extremity is non-frail, pre-frail, or frail.

14. The system of claim 8 , wherein the one or more gait characteristics comprise at least one of:
a propulsion duration;
a propulsion acceleration;
a mid-stance speed;
a speed norm;
a toe-off speed; and
a mid-swing speed.

15. A computer program product comprising:
a non-transitory computer readable medium comprising instructions executable by a processor to perform steps comprising:
receiving, from one or more sensors of a first sensing device worn on a

Assignees

• Baylor College Medicine

Inventors

• Najafi Bijan
• Rahemi Hadi
• Lee Hyoki
• Nguyen Hung

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/008

  based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour · CPC title

• A61B5/7275

  Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor · CPC title