US-20260127483-A1 - COMPUTER-IMPLEMENTED METHOD FOR TRAINING OF A MACHINE LEARNING MODEL IN FALL ASSESSMENT

US20260127483A1US 20260127483 A1US20260127483 A1US 20260127483A1US-20260127483-A1

Abstract

A computer-implemented method for training of a machine learning model in fall assessment comprising fall detection, for a fall assessment training environment; comprising implementing the machine learning model, and preparing training data. Preparing training data comprises: obtaining and automatically annotating sensor data from sensors, collecting data from subjects, and coupling values of each subject to their sensor data, respectively, Using the sensor data for generating a time series that comprises data points. Automatically annotating the data points such that each data point is annotated reflecting the corresponding sensor data, respectively. Using the data points, dynamically segmenting said time series into at least event segments associated with fall events and event segments that are not associated with fall events. Using the event segments to construct time windows along the time series such that the time series is discretized, each time window comprising a plurality of time steps.

Inventors

Jawwad Ahmed

Assignees

AUTOLIV DEVELOPMENT AB

Dates

Publication Date: 20260507
Application Date: 20221028
Priority Date: 20211029

Claims (16)

1 . A computer-implemented method for training of a machine learning model in fall assessment comprising fall detection, for a fall assessment training environment; the method comprising implementing the machine learning model, and preparing training data, where preparing-training data comprises obtaining and automatically annotating ( sensor data generated from sensors collecting data from subject, and coupling values of each subject to their specifically obtained, and annotated, sensor data, respectively, using the sensor data for generating a time series that comprises data points, automatically annotating the data points such that each data point is annotated reflecting the corresponding sensor data, respectively, using the annotated data points, dynamically segmenting said time series into at least event segments associated with fall events and event segments that are not associated with fall events, using the event segments to construct time windows along the time series such that the time series is discretized, each time window comprising a plurality of time steps, and automatically annotating the constructed time windows, where the automatic annotation of the time windows comprises information that identifies if the data points associated with the time steps in each time window belong to a fall event or to a non-fall event.
2 . The computer-implemented method according to claim 1 , wherein the automatic annotation of the time windows comprises information that identifies the position of the time-steps belonging to the fall segment in each time window, and information regarding an assigned weight to each time window, where the assigned weight has been determined in dependence of said position.
3 . The computer-implemented method according to claim 1 , further comprising communicating information comprising the prepared training data to the machine learning model and training the machine learning model using the training data comprising the prepared training data in fall assessment.
4 . The computer-implemented method according to claim 1 , wherein the obtained, and annotated, sensor data comprise sensor data generated during doings of the trainer subjects, said doings comprising activities of daily living (ADLs), including real fall events, and/or simulated falls.
5 . The computer-implemented method according to claim 1 , wherein the sensors comprise at least one of motion sensors, 3D sensors, accelerometers, gyroscopes and/or cameras.
6 . The computer-implemented method according to claim 1 , wherein the preparation of training data further comprises setting fall threshold values for sensor data and assignment of fall event segments if relevant sensor data are more than said fall threshold values.
7 . The computer-implemented method according to claim 1 , wherein the preparation of training data further comprises utilization of a majority voting scheme for determining said corresponding sensor data to each annotated data point and for determining said dynamic segmentations into event segments.
8 . The computer-implemented method according to claim 1 , wherein profile properties of the trainer subject profiles comprise profile properties, such as, height, weight and/or Body Mass Index (BMI), and wherein the values of the profile properties enable correlation of trainer subject profiles to corresponding user profiles, and more precise dynamic segmentation.
9 . A computer program comprising computer readable instructions for applying the computer-implemented method, and/or the preparation of training data, according to claim 1 , and/or a computer readable medium comprising said computer program.
10 . A computer program comprising computer readable instructions for a machine learning model according to claim 1 , and/or a computer readable medium comprising said computer program.
11 . A control unit arrangement, for training of a machine learning model, adapted to control at least, the implementing the machine learning model according to claim 1 , and/or the preparation of training data.
12 . A system for training of a machine learning model in fall assessment, for communication of information, for enabling implementing the machine learning model according to claim 1 , and for enabling the preparation of the training data wherein the fall assessment comprises fall detection; the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information; wherein the system utilizes a computer program comprising computer readable instructions for applying the computer-implemented method, and/or the preparation of training data, and/or a computer readable medium comprising said computer program.
13 . A computer-implemented method for fall assessment, and for communication of information from the fall assessment, for a fall assessment environment; wherein the fall assessment comprises fall detection; the fall assessment environment comprises a machine learning model, comprising the trained machine learning model being trained in accordance with the computer-implemented method, and/or being trained in accordance with a computer-implemented method comprising the preparation of training data, of claim 1 , a user, sensors collecting data from the user, means for obtaining data, means for processing data and means for communication of information; wherein the computer-implemented method comprises implementing the machine learning model comprising obtaining, and annotating, user sensor data generated from sensors collecting data from a user, wherein the user has a user profile, and the computer-implemented method further comprises obtaining values of the user profile properties, coupling to the user sensor data, and processing the coupled user sensor data by means of the machine learning model comprising the trained machine learning model, and/or being trained, and wherein the computer-implemented method comprises the fall assessment and the communication of information from the fall assessment, wherein the fall assessment comprises fall detection.
14 . A computer program comprising computer readable instructions for applying the computer-implemented method according to claim 13 , and/or a computer readable medium comprising said computer program.
15 . A control unit arrangement-for fall assessment, adapted to control at least, enablement of the computer-implemented method for fall assessment according to claim 13 , and/or the implementing of the machine learning model, comprising the trained machine learning model.
16 . A system for training of a machine learning model in fall assessment, for communication of information, for enabling implementing the machine learning model according to claim 1 , and for enabling the preparation of training data; wherein the fall assessment comprises fall detection; the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information; wherein the system utilizes a computer program comprising computer readable instructions for the machine learning model, and/or a computer readable medium comprising said computer program.

Description

DESCRIPTION OF THE DISCLOSURE The present disclosure relates to an improved computer-implemented method for training of a machine learning model in fall assessment, a computer program comprising computer readable instructions for applying the computer-implemented method and/or preparation of training data, a computer program comprising computer readable instructions for a machine learning model, computer readable mediums comprising the computer programs, as well as, a control unit arrangement, a device, and a system, for training of a machine learning model. Further, the present disclosure also relates to a computer-implemented method for fall assessment, and computer programs, computer readable mediums, control unit arrangements, devices, and systems, therefore. Falls are one leading cause of human injury and injury-related deaths. Fall detections and fall predictions are together with machine learning approaches of increased usefulness. Machine learning approaches are, for example, used today to distinguish fall and non-fall activities. At a high level, these approaches may include approaches involving vision-based arrangements which in real-time can classify falls based on live feed video. Vision-based arrangements may, in some instances, be less practical, e.g. in an outside environment, and may also come with potentially privacy issues. Further, there is also machine learning approaches focusing on more pervasive solutions being based on wearable device sensors which are non-intrusive and pervasive. In this area, see for example “A Cascade-Classifier Approach for fall detection, putra et al, MOBIHEALTH 2015, Oct. 14-16, London, Great Britain, January 2015, DOI: 10.4108/eai.14-10-2015.2261619” that proposes a cascade-classifier approach for this. Other examples include the M. Musci, D. D. Martini, N. Blago, T. Facchinetti, and M. Piastra, “Fall Detection using Recurrent Neural Networks,” p. 7 as well was the F. J. González-Cañete and E. Casilari, “A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems,” Sensors, vol. 21, no. 6, p. 2254, Mar. 2021, doi: 10.3390/s21062254. However, there is still room for improvements in this area. In particular, regarding the requirements on availability of suitably annotated data that is used by fall detection-based algorithms for learning of various fall and non-fall scenarios. However, creating annotated training data for data-driven machine learning algorithms, i.e. for data-driven machine learning computer programs, is not a trivial task and usually requires manual effort of careful analysis and manual effort which can be time consuming and costly particularly in real-time settings where new data are always streaming in. The falls may for example occur for a person walking in a home environment, or outside the home. In this context, falls also comprise falls occurring when riding a bicycle, scooter, motorcycle and the like, for example due to skidding, slipping, losing control of vehicle, crashing with another vehicle or object etc. Falls related to a running vehicle are often more abrupt, and require a faster handling than a fall occurring for a walking person that for example trips or slips. This is achieved by means of a computer-implemented method for training of a machine learning model in fall assessment comprising fall detection, for a fall assessment training environment. The method comprises implementing the machine learning model, and preparing training data. Preparing training data comprises obtaining, and automatically annotating sensor data generated from sensors collecting data from the subjects, and coupling values of each subject to their specifically obtained, and annotated, sensor data, respectively, and using the sensor data for generating a time series that comprises data points. Preparing training data further comprises automatically annotating the data points such that each data point is annotated reflecting the corresponding sensor data, respectively, and using the annotated data points, dynamically segmenting said time series into at least event segments associated with fall events and event segments that are not associated with fall events. Preparing training data also comprises using the event segments to construct time windows along the time series such that the time series is discretized, each time window comprising a plurality of time steps, and automatically annotating the constructed time windows, where the automatic annotation of the time windows comprises information that identifies if the data points associated with the time steps in each window belong to a fall event or to a non-fall event. This allows more accurate automatic identification of fall segments in a fall session using available information from the fallers/subjects and/or type of falls. This leads to more accurate annotation quality of time windows since the quality of the time window annotation is dependent on the quality of Segmentation ann