RESUMEN
Heart attack is a life-threatening condition which is mostly caused due to coronary disease resulting in death in human beings. Detecting the risk of heart diseases is one of the most important problems in medical science that can be prevented and treated with early detection and appropriate medical management; it can also help to predict a large number of medical needs and reduce expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various data sources including electronic health records and clinical diagnosis reports from hospital clinics. ML is used as a process in which computers learn from data in order to make predictions about new datasets. The algorithms created for predictive data analysis are often used for commercial purposes. This paper presents an overview to forecast the likelihood of a heart attack for which many ML methodologies and techniques are applied. In order to improve medical diagnosis, the paper compares various algorithms such as Random Forest, Regression models, K-nearest neighbour imputation (KNN), Naïve Bayes algorithm etc. It is found that the Random Forest algorithm provides a better accuracy of 88.52% in forecasting heart attack risk, which could herald a revolution in the diagnosis and treatment of cardiovascular illnesses.
RESUMEN
Over the past few years the growth and development of exo-skeleton has dramatically raised with the development of precise control elements and actuation systems. Many exo-skeleton systems have been designed, developed and tested for performance optimization. In the recent years, the significance of exo-skeleton in medical fields have got increased and are used in providing therapy and rehabilitation to the patients. With this development there comes the importance for analysis and control of the exo-skeleton for precise functioning and to avoid malfunction of the system in the later part. Dynamic analysis of limb joints is essential to better facilitate a deeper understanding of the exo-skeleton limb during various environmental conditions like varied loading. The dynamic model so developed will assist in choosing an apt actuation system based on the torque requirement of the model.This paper focusses on the analysis of a 2DOF lower limb active control exo-skeleton system and makes a torque calculation for actuator selection for the lower limb to provide rehabilitation to the patients as wearable walking aid. The work also makes a trajectory planning for the lower limb to move in sequence for making a walking cycle with angular limitations to avoid damage to the user's limbs. The motion analysis for the developed lower limb Exoskeleton as per the analysis is 52.055 Nm at hip joint 11.677 Nm at knee joint.
Asunto(s)
Dispositivo Exoesqueleto , Fenómenos Biomecánicos , Humanos , Articulación de la Rodilla , Extremidad Inferior , Torque , CaminataRESUMEN
Visually impaired people are often subjugated under extreme circumstances even in their day-to-day life. The daily requirements of a common man appear to be an impediment in their routine life. Simplest of tasks like walking, eating, bathing, conversing and even eating is of utmost difficulty to them. Moreover, with such difficulties their only way-out seems to be dependency on the privileged lot, which further diminishes their confidence in themselves and gradually makes them even more dependent. The conventional devices that are used by visually impaired people include basic walking sticks which fail at the job in hand by not providing adequate stabilisation on rough surfaces and misguiding the users into unfavourable conditions. There is no way for the person to know what the object in front of them is without hitting it with the stick, which could also lead to accidents. To solve these problems, a smart walking stick is developed which not only recognises the object in front of it using Machine Learning (ML) models, but also gives a voice output to alert its user about the particular object thereby limiting the chance of any and all accidents. The concept is realised in hardware and integrated to the walking stick. This helps in stabilisation of phone and to produce better results in object identification. Further an application is developed to alert the user by converting the obtained image into a voice messages.