Deep Learning Algorithms for Human Activity Recognition: A Comparative Analysis

Agrawal, Aaditya; Ahuja, Ravinder

doi:10.1007/978-981-33-6691-6_43

Cited by 2 publications

(1 citation statement)

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“…To model and decode time-sequential information in input sensor data, deep learning models are promising [ 18 ]. Among all the different deep models, the Deep Belief Network (DBN) was the first to be successful, as it was faster than the typical large artificial neural network [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating

Uddin

Seeberg

Kocbach

et al. 2021

Sensors

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The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.

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Section: Introductionmentioning

confidence: 99%