Fitness monitoring using machine learning
| dc.contributor.author | Khan, Mubashir | |
| dc.contributor.author | Shaikh, Shafaque Naushad (16CO15) | |
| dc.contributor.author | Shaikh, Altamas Shakeel (16CO11) | |
| dc.contributor.author | Ulde, Fahmi Nisar (16CO17) | |
| dc.date.accessioned | 2021-11-03T06:40:46Z | |
| dc.date.available | 2021-11-03T06:40:46Z | |
| dc.date.issued | 2020-05 | |
| dc.description.abstract | The enhanced performance of modern Artificial Intelligence algorithms has opened up limitless possibilities in the development of smart systems and devices. One of the most complex tasks for interactive devices is the analysis of human motion. However, using neural networks, movement can be classified and even understood. Our system proposes the use of an enhanced Pose Estimation algorithm[8] and Alternating Least Square(ALS)[4] for developing an application that functions as a personal exercise trainer. As the problem of staying healthy & fit is important as well as considering the use of smartphones, an easy-to-use application is a great solution. This report provide details of this application that offers a full workout program, monitors the user’s workout, and alerts the user when the move is performed incorrectly. Pose estimation detects human figures in images and videos[8].The algorithm is simply estimating where key body joints are and then joining them to know human pose. ALS is Alternating Least Square method for recommendation that we are using in diet and exercise recommendation system after categorising the user[4]. Keywords: BMI Calculation, Diet Recommendation, Exercise Recommendation, ALS, Pose estimation, Machine learning, Body detection, Schedule | en_US |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3607 | |
| dc.language.iso | en | en_US |
| dc.publisher | AIKTC | en_US |
| dc.subject | Project Report - CO | en_US |
| dc.title | Fitness monitoring using machine learning | en_US |
| dc.type | Other | en_US |