Understanding the mechanism of the neural correlates during human physical activities is important for providing safety in industrial factory environments considering brain activity during lifting a weight. Moreover, different responses to the same task can be observed due to physiological and neurological differences among individuals. In this project, the change pattern in EEG will be investigated during lifting of a weight and the features in EEG data making difference during lifting a weight will be analyzed. Classification between lifting and no lifting cases will be realized by using deep learning based machine learning methods. The outcomes of the project can be applied in industrial exoskeleton applications as well as physical rehabilitation of stroke patients.


Dataset Repository (Share on AI4EU)

Conference Paper / Journal Article


Project Partners:

  • Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TUBITAK), Sencer Melih Deniz
  • German Research Centre for Artificial Intelligence (DFKI), Paul Lukowicz


Primary Contact: Sencer Melih Deniz, TUBITAK BILGEM

Main results of micro project:

The project has run for almost 50% of its allocated time and has yet to be completed. Within this time duration, the following steps were completed:
1. Experimental paradigm was designed to achieve the project goals.
2. Study preparation including hardware and software development was completed.
3. Data recording session has been started and is in progress. Data from a total of 10 people has been obtained so far. More participants will be included in data acquisition to achieve the desired result.

The dataset and results will be evaluated once the data acquisition is completed.

Contribution to the objectives of HumaneAI-net WPs

This project is also part of WP2 with task numbers T2.2, T2.3.

This project aims to contribute to WP2 and WP6 by investigating the use case of EEG signal and AI models in the detection of various aspects of physical activities during weightlifting. To investigate pattern change in EEG during weightlifting will be aimed at providing more information in prediction of intended and actual human actions during sensori-motor tasks. Doing so, a common research question is aimed to be applied to the more industrial use cases such as control of exoskeletons. Moreover, outcomes of the project can be used for contribution in increasing mobility in stroke patients and disabled people as related with healthy living and mobility.

Tangible outputs

  • Program/code: Data Acquisition Software Code – Juan Felipe Vargas Colorado