Abstract
The Human Activity Recognition (HAR) system allows various accessible entries for the early diagnosis of Diabetes as one of the nescient applications domains for the HAR. Long Short-Term Memory (LSTM) was applied and recognized 13 activities that resemble diabetes symptoms. Afterward, risk factor assessment for an experimental subject identified similar activity pattern attributes between diabetic patients and the experimental subject. Because of this, a trained LSTM model was deployed to monitor the average time length for every activity performed by the experimental subject for 30 consecutive days. Concurrently, the symptomatic diabetes activity patterns of diabetic patients were explored. The cosine similarity of activity patterns of the experimental subject and diabetic patients measured 57.39%, putting the experimental subject into moderate risk factor class. The experimental subject was clinically tested for risk factors using the diabetic clinical diagnosis process, known as the A1C. The A1C level was 6.1%, recognizing the experimental subject as a patient suffering from Diabetes. Thus, the proposed novel approach remarkably classifies the risk factor level based on activity patterns.
Highlights
The primary source of human nutrition is blood glucose
One of the unattended applications of Human Activity Recognition (HAR) is the diagnoses of diseases, such as Diabetes, mental disorders, cancer, insomnia, cardiovascular diseases, among others that are directly correlated with the pattern of daily activities [7,8]
The risk factor for effective diabetes classification was measured using similar qualities characterizing the similarity of the activity patterns among diabetic patients and the experimental subject
Summary
The primary source of human nutrition is blood glucose. Insulin, a pancreatic hormone, allows glucose to reach human cells for energy intake. Physical activities significantly stimulate the regulation of the blood glucose in Diabetes mellitus, enhance cardiovascular health conditions, increase the tendency of losing more weight, and enhance the possibly the achieving best-being. One of the unattended applications of HAR is the diagnoses of diseases, such as Diabetes, mental disorders, cancer, insomnia, cardiovascular diseases, among others that are directly correlated with the pattern of daily activities [7,8]. The risk factor for effective diabetes classification was measured using similar qualities characterizing the similarity of the activity patterns among diabetic patients and the experimental subject. We considered the activity patterns and six progressively physical properties, i.e., height, weight, blood pressure, evidence of diabetic patients in first degree relatives, age, and gender. The level of the A1C assay was 6.1%, which recognized the experimental subject as a patient suffering from Diabetes, which confirms the conclusion determined by our suppositional scrutinization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
