US-20260128150-A1 - HEALTHCARE RECOMMENDATION METHOD AND SYSTEM USING REHABILITATION DEVICE

US20260128150A1US 20260128150 A1US20260128150 A1US 20260128150A1US-20260128150-A1

Abstract

The present disclosure provides a healthcare recommendation method and system using rehabilitation device, wherein the healthcare recommendation method, performed by a processing device, includes: obtaining raw sensing data from sensing devices, labeling one of the raw sensing data according to personalized feature labels to generate labeled data, inputting one of the raw sensing data to a pre-trained predication model to generate predicted data, performing data fusion on the labeled data and the predicted data to generate fusion data, extracting multi-dimensional feature data from the raw sensing data, inputting the fusion data and the multi-dimensional feature data into a pre-trained inference model to generate an exercise effectiveness indicator, and generating a healthcare plan based on the exercise effectiveness indicator.

Inventors

Chih Lin WU
Yun Cheng JHONG
Zhi Ying CHEN
Chien Der Lin

Assignees

INSTITUTE FOR INFORMATION INDUSTRY

Dates

Publication Date: 20260507
Application Date: 20241125
Priority Date: 20241106

Claims (16)

1 . A healthcare recommendation method, performed by a processing device, comprising: obtaining a plurality of raw sensing data from a plurality of sensing devices; labeling one of the plurality of raw sensing data according to a plurality of personalized feature labels to generate labeled data; inputting one of the plurality of raw sensing data into a pre-trained prediction model to generate predicted data; performing data fusion on the labeled data and the predicted data to generated fused data; extracting multi-dimensional feature data from the plurality of raw sensing data; inputting the fused data and the multi-dimensional feature data into a pre-trained inference model to generate an exercise effectiveness indicator; and generating a healthcare plan based on the exercise effectiveness indicator.
2 . The healthcare recommendation method according to claim 1 , wherein the plurality of raw sensing data comprises exercise time and a motion trajectory, and the healthcare recommendation method further comprises: utilizing the exercise time and the motion trajectory to calculate an acceleration change; and utilizing the acceleration change to perform a movement smoothness analysis to generate smoothness data.
3 . The healthcare recommendation method according to claim 2 , wherein performing the data fusion on the labeled data and the predicted data to generated the fused data further comprises: performing the data fusion on the labeled data, the predicted data and the smoothness data to generate the fused data.
4 . The healthcare recommendation method according to claim 2 , wherein using the acceleration change to perform the movement smoothness analysis to generate the smoothness data comprises: obtaining a motion type corresponding to the motion trajectory; and selecting one of a plurality of movement smoothness analysis algorithms according to the motion type to perform the movement smoothness analysis.
5 . The healthcare recommendation method according to claim 1 , further comprising: obtaining user feedback data; and adjusting a threshold setting of the labeling according to the plurality of personalized feature labels according to the user feedback data.
6 . The healthcare recommendation method according to claim 1 , further comprising: obtaining first historical sensing data and user feedback data corresponding to the first historical sensing data; labeling the first historical sensing data according to a threshold setting and a plurality of default movement features to generate first historical labeled data; adjusting the threshold setting according to the user feedback data and the first historical labeled data; labeling a plurality of second historical data according to the threshold setting being adjusted and the plurality of default movement features to generate a plurality of pieces of second historical labeled data; and utilizing the plurality of second historical labeled data to generate a normal model, wherein the normal model indicates the plurality of personalized feature labels.
7 . The healthcare recommendation method according to claim 1 , further comprising: obtaining a plurality of historical sensing data from the plurality of sensing devices; utilizing the plurality of historical sensing data to generate a plurality of pieces of training data; utilizing the plurality of training data to perform training to generate a multi-dimensional inference model; and performing an approximate estimation and compression on the multi-dimensional inference model to generate the pre-trained inference model.
8 . The healthcare recommendation method according to claim 1 , further comprising: obtaining a plurality of historical sensing data from the plurality of sensing devices; labeling one of the plurality of historical sensing data according to the plurality of personalized feature labels to generate historical labeled data; inputting one of the plurality of historical sensing data to the pre-trained prediction model to generate historical predicted data; performing data fusion on the historical labeled data and the historical predicted data to generate historical fused data; extracting historical multi-dimensional feature data from the plurality of pieces of historical sensing data; utilizing the historical fused data and the historical multi-dimensional feature data as one of a plurality of training data; and utilizing the plurality of training data to generate the pre-trained inference model.
9 . A healthcare recommendation system, comprising: a plurality of sensing devices configured to obtain a plurality of raw sensing data; and a processing device connected to the plurality of sensing devices, and configured to label one of the plurality of raw sensing data according to a plurality of personalized feature labels to generate labeled data, input one of the plurality of raw sensing data into a pre-trained prediction model to generate predicted data, perform data fusion on the labeled data and the predicted data to generated fused data, extract multi-dimensional feature data from the plurality of raw sensing data, input the fused data and the multi-dimensional feature data into a pre-trained inference model to generate an exercise effectiveness indicator, and generate a healthcare plan based on the exercise effectiveness indicator.
10 . The healthcare recommendation system according to claim 9 , wherein the plurality of raw sensing data comprises exercise time and a motion trajectory, and the processing device is further configured to utilize the exercise time and the motion trajectory to calculate an acceleration change, and utilize the acceleration change to perform a movement smoothness analysis to generate smoothness data.
11 . The healthcare recommendation system according to claim 10 , wherein the processing device is further configured to perform the data fusion on the labeled data, the predicted data and the smoothness data to generate the fused data.
12 . The healthcare recommendation system according to claim 10 , wherein the processing device is further configured to obtain a motion type corresponding to the motion trajectory, and select one of a plurality of movement smoothness analysis algorithms according to the motion type to perform the movement smoothness analysis.
13 . The healthcare recommendation system according to claim 9 , wherein the processing device is further configured to obtain user feedback data, and adjust a threshold setting of the labeling according to the plurality of personalized feature labels according to the user feedback data.
14 . The healthcare recommendation system according to claim 9 , wherein the processing device is further configured to: obtain first historical sensing data and user feedback data corresponding to the first historical sensing data; label the first historical sensing data according to a threshold setting and a plurality of default movement features to generate first historical labeled data; adjust the threshold setting according to the user feedback data and the first historical labeled data; label a plurality of second historical data according to the threshold setting being adjusted and the plurality of default movement features to generate a plurality of second historical labeled data; and utilize the plurality of second historical labeled data to generate a normal model, wherein the normal model indicates the plurality of personalized feature labels.
15 . The healthcare recommendation system according to claim 9 , wherein the processing device is further configured to: obtain a plurality of historical sensing data from the plurality of sensing devices; utilize the plurality of historical sensing data to generate a plurality of training data; utilize the plurality of training data to perform training to generate a multi-dimensional inference model; and perform an approximate estimation and compression on the multi-dimensional inference model to generate the pre-trained inference model.
16 . The healthcare recommendation system according to claim 9 , wherein the processing device is further configured to: obtain a plurality of historical sensing data from the plurality of sensing devices; label one of the plurality of historical sensing data according to the plurality of personalized feature labels to generate historical labeled data; input one of the plurality of historical sensing data to the pre-trained prediction model to generate historical predicted data; perform data fusion on the historical labeled data and the historical predicted data to generate historical fused data; extract historical multi-dimensional feature data from the plurality of historical sensing data; utilize the historical fused data and the historical multi-dimensional feature data as one of a plurality of training data; and utilize the plurality of training data to generate the pre-trained inference model.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This non-provisional application claims priority under 35 U.S.C. § 119(a) on Patent Application No(s). 113142537 filed in Republic of China (Taiwan) on Nov. 6, 2024, the entire contents of which are hereby incorporated by reference. BACKGROUND 1. Technical Field This disclosure relates to a healthcare recommendation method and system. 2. Related Art Population aging has become a global trend and is progressing rapidly. Therefore, how to effectively care for the aging population in the future will be a significant challenge for society. In response to the aging society, caregiving institutions need to integrate and utilize information and communication technology products and services to address the issue of manpower shortages, while also improving the quality of care for the elderly. In the current rehabilitation care process, the assessment of operating rehabilitation device is primarily based on the number of operations and duration or the use of general standard models for quantitative evaluation. However, these methods often overlook an individual's physical condition, the operation process, and personal health historical data, making it impossible to conduct personalized effectiveness evaluation based on multi-dimensional data, which limits the accuracy and specificity of rehabilitation outcomes. SUMMARY Accordingly, this disclosure provides a healthcare recommendation method and system. According to one or more embodiment of this disclosure, a healthcare recommendation method, performed by a processing device, includes: obtaining a plurality of raw sensing data from a plurality of sensing devices; labeling one of the raw sensing data according to a plurality of personalized feature labels to generate labeled data; inputting one of the raw sensing data into a pre-trained prediction model to generate predicted data; performing data fusion on the labeled data and the predicted data to generated fused data; extracting multi-dimensional feature data from the raw sensing data; inputting the fused data and the multi-dimensional feature data into a pre-trained inference model to generate an exercise effectiveness indicator; and generating a healthcare plan based on the exercise effectiveness indicator. According to one or more embodiment of this disclosure, a healthcare recommendation system includes: a plurality of sensing devices and a processing device. The sensing devices are configured to obtain a plurality of raw sensing data. The processing device is connected to the sensing devices. The processing device is configured to label one of the raw sensing data according to a plurality of personalized feature labels to generate labeled data, input one of the raw sensing data into a pre-trained prediction model to generate predicted data, perform data fusion on the labeled data and the predicted data to generated fused data, extract multi-dimensional feature data from the raw sensing data, input the fused data and the multi-dimensional feature data into a pre-trained inference model to generate an exercise effectiveness indicator, and generate a healthcare plan based on the exercise effectiveness indicator. In view of the above, the healthcare recommendation method and system according to an embodiment of the present disclosure may perform personalized labeling on sensing data, feature extraction on multi-dimensional data and processing multiple model data, perform assessment of personalized exercise effectiveness indicator by using the above data, and provide the healthcare plan to the user accordingly. Therefore, the issue of staff shortages in care institutions may be resolved, and the quality of care may be improved. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure will become more fully understood from the detailed description given herein below and the accompanying drawings which are given by way of illustration only and thus are not limitative of the present disclosure and wherein: FIG. 1 is a block diagram illustrating a healthcare recommendation system according to an embodiment of the present disclosure; FIG. 2 is a flow chart illustrating a healthcare recommendation method according to an embodiment of the present disclosure; FIG. 3 is a flow chart illustrating a movement smoothness analysis in the healthcare recommendation method according to an embodiment of the present disclosure; FIG. 4 is a flow chart illustrating building personalized features in the healthcare recommendation method according to an embodiment of the present disclosure; FIG. 5 is a flow chart illustrating obtaining a pre-trained inference model in the healthcare recommendation method according to an embodiment of the present disclosure; and FIG. 6 is a flow chart illustrating obtaining the pre-trained inference model in the healthcare recommendation method according to another embodiment of the present disclosure. DETAILED DESCRIPTION In the following detailed description, for pu