“Device And Method For Sequential Posture Identification And Autonomic Function Information Acquisition” in Patent Application Approval Process (USPTO 20210307699): Nippon Telegraph and Telephone Corporation

2021 OCT 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors HORIKAWA, Keitaro (Musashino-shi, Japan); NAKAMURA, Yoshitaka (Musashino-shi, Japan); SAWADA, Masato (Musashino-shi, Japan); TSUKADA, Shingo (Atsugi-shi, Japan); YAMADA, Toshiya (Yokohama-shi, Japan); YAMANAKA, Akihiro (Musashino-shi, Japan), filed on June 21, 2021, was made available online on October 7, 2021, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to Nippon Telegraph and Telephone Corporation (Chiyoda-ku, Japan).

The following quote was obtained by the news editors from the background information supplied by the inventors: “In recent years, utilization of so-called wearable devices, i.e., information processing terminals which can be attached to human bodies (hereinafter, wearable devices) has expanded. Because it can be worn and carried by a user on a daily basis, a wearable device can be utilized in continuous and long-term monitoring of health conditions and lifestyle habits of the user. Further, technology for collecting, on a large scale, information related to users’ health conditions and lifestyle habits collected by wearable devices is being popularized. Examples of such information on user’s health conditions and lifestyle habits collectable by utilization of a wearable device include times of the user’s sleep, exercise, work, commuting, meals, and the like.

“Along with aging of society, countermeasures for increase of chronic diseases, such as high blood pressure and diabetes, and dealing promptly with heart attacks and cerebrovascular diseases (apoplexy) have become social challenges. Since these diseases are closely related to lifestyle habits, such as exercise and diet, there is a demand for prevention of these diseases and prevention of increase of these diseases by appropriate health management according to individual lifestyle habits.

“Action estimation technology may be utilized for lifestyle habits, action patterns, and the like of an individual to be perceived. For example, according to an action estimation technique described in Non-Patent Literature 1, a single triaxial acceleration sensor is attached to a subject and acceleration data are acquired. Based on the acquired acceleration data, types of activity of the subject are estimated.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “SUMMARY:

“Technical Problem

“However, when action estimation for a subject is performed by the conventional technique, in-depth estimation of posture and states of activity is difficult. For example, when an acceleration sensor is attached to the arm, movement of the torso and movement of the arm are not necessarily the same, and thus the posture of the subject may not be detected accurately. Further, just performing action estimation of a subject by use of an acceleration sensor may not directly lead to health management and disease prevention for the subject.

“Embodiments disclosed herein have been made in view of the above, and aim to provide a technique for detecting a posture and a state of activity of a subject and enabling health management and disease prevention.

“Solution to Problem

“Sequential posture identification device, method, and program disclosed herein receive respectively, from an acceleration information measurement unit and a biological signal information measurement unit that are provided in a wearable device, acceleration information on motion of a subject whom the wearable device is attached to, and biological signal information of the subject. Further, the sequential posture identification device, method, and program extract, from the acceleration information and the biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period. Further, the sequential posture identification device, method, and program generate, by machine learning based on the first feature data, a dynamic/static activity identification model for identification of whether the subject is involved in a dynamic activity or in a static activity. The sequential posture identification device, method, and program generate, by machine learning based on the first feature data, a dynamic-activity identification model for identification of plural dynamic-activity patterns. The sequential posture identification device, method, and program generate, by machine learning based on the first feature data, a static-activity identification model for identification of plural static-activity patterns. The sequential posture identification device, method, and program determine, based on the dynamic/static activity identification model and the second feature data, whether the subject is involved in a dynamic activity or in a static activity in the second predetermined period. The sequential posture identification device, method, and program determine, based on the dynamic-activity identification model and the second feature data, a dynamic-activity pattern of the subject in the second predetermined period. The sequential posture identification device, method, and program determine, based on the static-activity identification model and the second feature data, a static-activity pattern of the subject in the second predetermined period. The sequential posture identification device, method, and program identify a posture and an activity of the subject in the second predetermined period, by combining results of the determination by first, second, and third determination units. The sequential posture identification device, method, and program generate correspondence information associating between the posture and the activity identified by an identification unit and biological signal information of the subject in the second predetermined period.

“Further, autonomic function information acquisition device, method, and program disclosed herein receive respectively, from an acceleration information measurement unit and a biological signal information measurement unit that are provided in a wearable device, acceleration information on motion of a subject whom the wearable device is attached to, and biological signal information of the subject. By executing sequential machine learning for acceleration information and biological signal information in a first predetermined period, the autonomic function information acquisition device, method, and program identify a posture and an activity of the subject in a second predetermined period. The autonomic function information acquisition device, method, and program extract biological signal information corresponding to a combination of the same posture and activity that have been identified. The autonomic function information acquisition device, method, and program calculate a parameter of autonomic function evaluation, from the extracted biological signal information corresponding to the combination of the same posture and activity.

“Advantageous Effects of Invention

“The disclosed sequential posture identification device, sequential posture identification method, sequential posture identification program, autonomic function information acquisition device, autonomic function information acquisition method, and autonomic function information acquisition program achieve an effect of detecting a posture and a state of activity of a subject and enabling health management and disease prevention.”

The claims supplied by the inventors are:

“1. An autonomic function information acquisition device, comprising: an acceleration information measurement unit that is provided in a wearable device, and measures acceleration information of motion of a subject whom the wearable device is attached to; a biological signal information measurement unit that is provided in the wearable device, and measures biological signal information of the subject; an identification unit that identifies, by executing sequential machine learning for the acceleration information and the biological signal information in a first predetermined period, a posture and an activity of the subject in a second predetermined period; an extraction unit that extracts biological signal information corresponding to a combination of the same posture and activity identified by the identification unit; and a calculation unit that calculates a parameter of autonomic function evaluation from the biological signal information extracted by the extraction unit and corresponding to the combination of the same posture and activity.

“2. The autonomic function information acquisition device according to claim 1, wherein the biological signal information measurement unit measures, as the biological signal information, heart rate data of the subject, the extraction unit extracts heart rate data corresponding to the combination of the same posture and activity, and the calculation unit calculates, as the parameter, at least one of an average value, a variance value, and a median point, of the heart rate data extracted by the extraction unit.

“3. The autonomic function information acquisition device according to claim 2, further comprising: a connection unit that connects together the heart rate data extracted by the extraction unit and corresponding to the combination of the same posture and activity, into a group of data, wherein when a difference between values of connected portions of the connected heart rate data is less than a predetermined threshold, the connection unit statistically calculates an estimated value that corrects the difference, and connects together the heart rate data that have been corrected by the estimated value.

“4. The autonomic function information acquisition device according to claim 2, further comprising: a connection unit that connects together the heart rate data extracted by the extraction unit and corresponding to the combination of the same posture and activity, into a group of data, wherein when a difference between values of connected portions of the connected heart rate data is equal to or larger than a predetermined threshold, the connection unit deletes a value exceeding the predetermined threshold and connects the heart rate data together.

“5. The autonomic function information acquisition device according to claim 1, wherein the biological signal information measurement unit measures, as the biological signal information, heart rate data of the subject, the extraction unit extracts plural sets of heart rate data corresponding to periods corresponding to the same consecutive changes in posture and activity, and the calculation unit synchronously adds together the plural sets of heart rate data by synchronizing starting time points or ending time points of a change in the posture or activity in the plural sets of heart rate data extracted by the extraction unit, and calculates the parameter from the synchronously added data.

“6. The autonomic function information acquisition device according to claim 5, wherein the extraction unit extracts the plural sets of heart rate data corresponding to the periods corresponding to the same consecutive changes in posture and activity, each of the periods being a period, in which the subject changes a posture, and in which a body of the subject is static before and after a time point of the change of the posture, and the calculation unit calculates, as the parameter, at least one of: a difference between average heartbeat intervals before and after the time point of the change of the posture; a maximum inclination of heartbeat intervals in an initial response; and a maximum inclination of heartbeat intervals in a late response.

“7. The autonomic function information acquisition device according to claim 5, wherein the extraction unit extracts the plural sets of heart rate data corresponding to the periods corresponding to the same consecutive changes in posture and activity, each of the periods being a period, in which the subject changes from a static state to a dynamic state and thereafter returns to the static state again, and during which a posture of the subject does not change, and the calculation unit synchronously adds together the plural sets of heart rate data by synchronizing starting time points of the dynamic state, and calculates, as the parameter, at least one of: a maximum inclination of a rising phase of heart rate; an average heart rate after the rise; and a difference between average heart rates or average heartbeat intervals before start of the dynamic state and during the dynamic state.

“8. The autonomic function information acquisition device according to claim 5, wherein the extraction unit extracts plural sets of heart rate data corresponding to the periods corresponding to the same consecutive changes in posture and activity, each of the periods being a period, in which the subject changes from a static state to a dynamic state and thereafter returns to a static state again, and during which the posture of the subject does not change, and the calculation unit synchronously adds together the plural sets of heart rate data by synchronizing ending time points of the dynamic state, and calculates, as the parameter, at least one of: a maximum inclination of a heart rate falling phase; an average heart rate after the fall; and a difference between average heart rates or average heartbeat intervals during the dynamic state and after ending of the dynamic state.

“9. The autonomic function information acquisition device according to claim 1, further comprising: a learning unit that executes machine learning based on correspondence between the posture and activity identified by the identification unit and the biological signal information; and an abnormality detection unit that detects, based on a result of the machine learning by the learning unit, an abnormality in the subject from the acceleration information and the biological signal information measured by the acceleration information measurement unit and the biological signal information measurement unit.

“10. The autonomic function information acquisition device according to claim 1, wherein the first predetermined period and the second predetermined period overlap each other at least partially.

“11. An autonomic function information acquisition method, comprising: a reception step of receiving respectively, from an acceleration information measurement unit and a biological signal information measurement unit that are provided in a wearable device, acceleration information on motion of a subject whom the wearable device is attached to, and biological signal information of the subject; an identification step of identifying, by executing sequential machine learning for the acceleration information and the biological signal information in a first predetermined period, a posture and a motion of the subject in a second predetermined period; an extraction step of extracting biological signal information corresponding to a combination of the same posture and activity identified in the identification step; and a calculation step of calculating a parameter of autonomic function evaluation from the biological signal information extracted in the extraction step and corresponding to the combination of the same posture and activity.

“12. A non-transitory computer-readable recording medium having stored therein an autonomic function information acquisition program that causes a computer to execute: a reception process of receiving respectively, from an acceleration information measurement unit and a biological signal information measurement unit that are provided in a wearable device, acceleration information on motion of a subject whom the wearable device is attached to, and biological signal information of the subject; an identification process of identifying, by executing sequential machine learning for the acceleration information and the biological signal information in a first predetermined period, a posture and an activity of the subject in a second predetermined period; an extraction process of extracting biological signal information corresponding to a combination of the same posture and activity identified in the identification process; and a calculation process of calculating a parameter of autonomic function evaluation from the biological signal information extracted in the extraction process and corresponding to the combination of the same posture and activity.”

URL and more information on this patent application, see: HORIKAWA, Keitaro; NAKAMURA, Yoshitaka; SAWADA, Masato; TSUKADA, Shingo; YAMADA, Toshiya; YAMANAKA, Akihiro. Device And Method For Sequential Posture Identification And Autonomic Function Information Acquisition. Filed June 21, 2021 and posted October 7, 2021. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220210307699%22.PGNR.&OS=DN/20210307699&RS=DN/20210307699

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