Fall probability estimation device, fall probability estimation system, fall probability estimation method, and recording medium

Patent metadata

Field	Value
Publication number	US-2025032000-A1
Application number	US-202118716597-A
Country	US
Kind code	A1
Filing date	Dec 27, 2021
Priority date	Dec 27, 2021
Publication date	Jan 30, 2025
Grant date	—

Abstract

Provided is a fall probability estimation device that includes a data acquisition unit that acquires feature amount data including a feature amount extracted from sensor data regarding a motion of a foot of a user and used for estimation of fall probability of the user, a storage unit that stores an estimation model that outputs a fall probability index according to an input of the feature amount data, an estimation unit that inputs the acquired feature amount data into the estimation model and estimates the fall probability of the user according to the fall probability index output from the estimation model, and an output unit that outputs information regarding the estimated fall probability of the user.

First claim

What is claimed is:

1 . A fall probability estimation device comprising:
a storage configured to store an estimation model that outputs a fall probability index according to an input of feature amount data used for estimating fall probability; a memory storing instructions; and a processor connected to the memory and configured to execute the instructions to: acquire feature amount data including a feature amount extracted from sensor data regarding a motion of a foot of a user and used for estimation of fall probability of the user; input the acquired feature amount data into the estimation model and estimate the fall probability of the user according to the fall probability index output from the estimation model; and output information regarding the estimated fall probability of the user.

2 . The fall probability estimation device according to claim 1 , wherein
the storage stores the estimation model that outputs a fall probability score according to an input of the feature amount data, and the processor is configured to execute the instructions to acquire the feature amount data including a feature amount extracted from gait waveform data generated using time-series data of the sensor data regarding a motion of a foot and used to estimate the fall probability score as the fall probability index, input the acquired feature amount data to the estimation model, and estimate the fall probability of the user according to the fall probability score output from the estimation model.

3 . The fall probability estimation device according to claim 2 , wherein
the storage stores the estimation model generated by machine learning using training data regarding a plurality of subjects in which a feature amount used for estimation of the fall probability index is an explanatory variable and the fall probability index of the plurality of subjects is an objective variable, and the processor is configured to execute the instructions to input the feature amount data acquired regarding the user to the estimation model, and estimate the fall probability of the user according to the fall probability index of the user output from the estimation model.

4 . The fall probability estimation device according to claim 3 , wherein
the storage stores the estimation model machine-learned using an explanatory variable including attribute data of the plurality of subjects, and the processor is configured to execute the instructions to input the feature amount data and the attribute data related to the user to the estimation model, and estimate the fall probability of the user according to the fall probability index of the user output from the estimation model.

5 . The fall probability estimation device according to claim 3 , wherein
the storage stores a first estimation model generated by machine learning using training data regarding the gait waveform data of the plurality of subjects in which feature amounts regarding activities of quadriceps femoris, hamstrings, tibialis anterior muscle, tibialis posterior muscle, and gluteus medius muscle extracted from a first section from immediately before heel contact to immediately after landing of a sole, feature amounts regarding activities of iliopsoas muscle, quadriceps femoris, adductor longus muscle, gracilis muscle, sartorius muscle, and tibialis anterior muscle extracted from a second section from a pre-swing period to an initial swing period, and feature amounts regarding activities of hamstrings, tibialis anterior muscle, and gracilis muscle extracted from a third section before and after movement of minimum toe clearance in a mid-swing period are explanatory variables, and the fall probability index of the plurality of subjects is an objective variable, and the processor is configured to execute the instructions to input the feature amount data acquired according to gait of the user to the first estimation model, and estimate the fall probability of the user according to the fall probability index of the user output from the first estimation model.

6 . The fall probability estimation device according to claim 5 , wherein
the storage stores the first estimation model generated by machine learning using training data regarding the gait waveform data of the plurality of subjects in which at least one feature amount included in a first feature amount group including at least one feature amount extracted from the first section, a second feature amount group including at least one feature amount extracted from the second section, and a third feature amount group including at least one feature amount extracted from the third section is an explanatory variable and the fall probability index of the plurality of subjects is an objective variable, the processor is configured to execute the instructions to acquire the feature amount data including the at least one feature amount included in the first feature amount group, the second feature amount group, and the third feature amount group extracted according to the gait of the user, input the acquired feature amount data to the first estimation model, and estimate the fall probability of the user according to the fall probability index of the user output from the first estimation model.

7 . The fall probability estimation device according to claim 6 , wherein
the storage stores the first estimation model generated by machine learning using training data regarding the gait waveform data of the plurality of subjects in which at least one feature amount extracted from a gait phase common to five items among gait phases from which feature amounts regarding each of the five items are extracted: a total muscular strength of a whole body; a dynamic balance; a lower-limb muscular strength; a mobility; and a static balance is an explanatory variable and the fall probability index of the plurality of subjects is an objective variable, the processor is configured to execute the instructions to acquire the feature amount data including the at least one feature amount included in the first feature amount group, the second feature amount group, and the third feature amount group extracted according to the gait of the user, input the acquired feature amount data to the first estimation model, and estimate the fall probability of the user according to the fall probability index of the user output from the first estimation model.

8 . The fall probability estimation device according to claim 3 , wherein
the storage stores a second estimation model generated by machine learning using training data in which a score of at least one of five items: a total muscular strength of a whole body; a dynamic balance; a lower-limb muscular strength; a mobility; and a static balance estimated using the gait waveform data of the plurality of subjects is an explanatory variable and the fall probability index of the plurality of subjects is an objective variable, the processor is configured to execute the instructions to acquire at least one of the scores regarding the five items estimated according to gait of the user, input the acquired score to the second estimation model, and estimate the fall probability of the user according to the fall probability index of the user output from the second estimation model.

9 . The fall probability estimation device according to claim 8 , wherein
the storage stores a pre-estimation model generated by machine learning using training data regarding the gait waveform data of the plurality of subjects in which a feature amount related to at least one of the five items is an explanatory variable and the scores of the five items related to the feature amount used as the explanatory variable are objective variabl

Assignees

• Nec Corp

Inventors

• Huang Chenhui
• Nihey Fumiyuki
• Wang Zhenwei
• Kajitani Hiroshi
• Nozaki Yoshitaka
• Fukushi Kenichiro

Classifications

• A61B5/1117 Primary

  Fall detection · 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

• A61B5/7267

  involving training the classification device · CPC title

• A61B5/112 Primary

  Gait analysis · CPC title

• A61B5/11

  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