Tibial Accelerations During the Single-Leg Hop Test: Influence of Fixation

Tucker, Hannah W; Tobin, Emily; Moran, Mark

doi:10.1123/jsr.2020-0309

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The specifications of the IMUs are summarized in Table 2 according to [48]. These kinds of sensors have been used in several recent studies, including [49][50][51][52]. The sampling frequency that was used throughout the experimental sessions was 50 Hz, which is considered to be adequate for such kind of investigations, similarly to experimental investigations such as [21,24,52].…”

Section: Data Acquisition and Sensorsmentioning

confidence: 99%

Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture

et al. 2021

View full text Add to dashboard Cite

The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.

show abstract