Mohammad Anisur Rahaman’s scientific contributions

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (1)


Figure 13. A graphical representation of the comparison result between docto-bot and conventional medical equipment
Multipurpose medical assistant robot (Docto-Bot) based on internet of things
  • Article
  • Full-text available

December 2021

·

294 Reads

·

8 Citations

International Journal of Electrical and Computer Engineering (IJECE)

·

·

Mohammad Anisur Rahaman

span>The world's population is growing every day, and so is the number of patients. People's life expectancy is increasing due to technology's welfare, but the problem is that the health sector has always faced a shortage of inadequate doctors. This research main objective was to design and implement a biomedical-based medical assistant robot named "Docto-Bot" to deal with this problem. This research concerns this medical assistant robot's design and development for the disabled and the patients in need. Such a robot's prime utilization is to minimize person-to-person contact and ensure the cleaning, sterilization, and support in hospitals and similar facilities such as quarantine. This prototype robot consists of a medicine reminding and medicine providing system, Automatic hand sanitizer and IoT based physiological monitoring system (body temperature, pulse rate, ECG, Oxygen saturation level). A direct one-to-one server-based communication method and user-end android app maintaining system designed. It also included the controlling part, which control automatically and manually by users. Docto-Bot will play a very significant factor in bio-medical robot applications. Though the achievements described in the paper look fruitful and advanced, shortcomings still exist.</span

Download

Citations (1)


... At present, the traditional machine learning models are commonly applied for ECG prediction. These models make forecasting based on historical data, such as classification model and regression model [10][11][12][13]. For example, Liu et al. [14] developed a cardiac arrest classification model utilizing wavelet transform and the AdaBoost algorithm. ...

Reference:

Early prediction of sudden cardiac death risk with Nested LSTM based on electrocardiogram sequential features
Multipurpose medical assistant robot (Docto-Bot) based on internet of things

International Journal of Electrical and Computer Engineering (IJECE)