@inproceedings{6fb2fb46ddf14f638a20887084c17ddc,
title = "IMU-based solution for automatic detection and classification of exercises in the fitness scenario",
abstract = "Causal relationship between physical activity and prevention of several diseases has been known for some time. Recently, attempts to quantify dose-response relationship between physical activity and health show that automatic tracking and quantification of the exercise efforts not only help in motivating people but improve health conditions as well. However, no commercial devices are available for weight training and calisthenics. This work tries to overcome this limit, exploiting machine learning technique (particularly Linear Discriminant Analysis, LDA) for analyzing data coming from wearable inertial measurement units, (IMUs) and classifying/counting such exercises. Computational requirements are compatible with embedded implementation and reported results confirm the feasibility of the proposed approach, offering an average accuracy in the detection of exercises on the order of 85%.",
keywords = "data classification, IMU, machine learning, Mhealth, wearables",
author = "Claudio Crema and Alessandro Depari and Alessandra Flammini and Emiliano Sisinni and Thomas Haslwanter and Stefan Salzmann",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 12th IEEE Sensors Applications Symposium, SAS 2017 ; Conference date: 13-03-2017 Through 15-03-2017",
year = "2017",
month = apr,
day = "6",
doi = "10.1109/SAS.2017.7894068",
language = "English",
series = "SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings",
address = "United States",
}