Active Sense: Early Staging of Non-Insulin Dependent Diabetes Mellitus (NIDDM) Hinges upon Recognizing Daily Activity Pattern

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.

The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

“…Human activity recognition (HAR) is the process of enabling computers to recognize human activities by analyzing patterns in different data types, including sensor data, images, and videos. Research on HAR is important as it is the principal method for accomplishing applications, such as identifying risk factors regarding depression [ 1 ], diabetes [ 2 ], health condition surveillance [ 3 , 4 ], eldercare [ 5 ], sports performance analysis [ 6 ], and abnormal activity identification [ 7 ]. Since HAR is the primary foundation for the successful implementation of many applications, researchers are trying to overcome the challenges which cause inaccuracy in HAR.…”