Internship and Applied Research Experience at EHR-LAB
The Engineering and Health Research Laboratory (EHR-LAB) offers undergraduate and graduate students an applied, research-oriented, and interdisciplinary internship environment at the intersection of engineering, biomedical technologies, and health sciences. The primary objective of our internship program is to enable students not only to familiarize themselves with laboratory equipment but also to experience the end-to-end research process—spanning from experimental design and data collection to signal processing, analysis, visualization, and scientific interpretation.
EHR-LAB’s research activities encompass complementary fields such as biomedical signal and image processing, biomechanics, neurophysiology, clinical decision support systems, artificial intelligence and machine learning in healthcare, wearable technologies and IoT, technology-assisted therapy and rehabilitation, exercise biomechanics and sports performance, balance and postural control, and gait analysis. Consequently, interns have the opportunity to work on real-world research problems related to biomedical engineering, human movement, neuromuscular systems, and health technologies, aligned with their academic backgrounds and the requirements of ongoing projects.
From Lab to Data: End-to-End Research
At the core of the internship approach at EHR-LAB lies the integrated workflow of “device › experiment › data › signal processing › analysis › scientific interpretation.” Interns participate—under the guidance of researchers—in stages such as understanding research protocols, setting up experimental setups, preparing participants and equipment, positioning sensors/electrodes, checking connections, recording data, evaluating signal quality, and organizing data post-experiment.
During this process, students: Interns can gain experience working with technologies such as Delsys Trigno multi-channel wireless surface electromyography (sEMG), Shimmer EMG, Bertec force platforms and CoP measurement systems, pressure measurement systems, wearable sensors (e.g., MYO Armband, LPMS-B2, Axivity AX3), the Wii Balance Board, BlazePod, stroboscopic vision systems, and other biomedical measurement technologies used in various research projects. The laboratory's existing infrastructure includes equipment for EMG, force/pressure, and wearable motion sensors, alongside a MATLAB-based analysis framework.
The 16-channel OpenBCI EEG system, recently added to the laboratory's infrastructure, represents a new area of study for the internship program, particularly regarding neurophysiology and multimodal biosignal research. Practical training covers EEG measurement preparation using a saline-based electrode system, electrode placement, channel and connection checks, signal quality monitoring, recording procedures, and fundamental EEG signal processing approaches, all tailored to specific research protocols.
Biomedical Signal and Image Processing
A key component of the EHR-LAB internship involves going beyond mere device operation to transform raw biomedical data into meaningful scientific information. The laboratory's existing software infrastructure includes tools for biomedical signal analysis.
Depending on the scope of the project, interns may participate in various stages of data processing—such as data cleaning, artifact and noise control, filtering, normalization, time and frequency domain analyses, feature extraction, data visualization, and basic statistical evaluation—using data obtained from sEMG, EEG, force platforms/CoP, pressure sensors, accelerometers, gyroscopes, and other wearable sensors. By encouraging the development of problem-specific code within the MATLAB environment, the aim is for students to evolve from mere users of off-the-shelf software into researchers capable of understanding data, developing algorithms, and critically evaluating results.
EHR-LAB’s areas of activity also include the analysis of multidimensional biomedical data—obtained through advanced studies—using machine learning and artificial intelligence methods, the integrated evaluation of data from diverse sensors, and the investigation of data-driven clinical and functional inferences. This approach aligns directly with the laboratory's research focus on artificial intelligence in healthcare, machine learning, clinical decision support systems, wearable technologies, and IoT.
Interdisciplinary Research Culture
At EHR-LAB, an internship is viewed as more than just technical laboratory training. By interacting with researchers from diverse disciplines—such as electrical-electronics engineering, biomedical engineering, physiotherapy and rehabilitation, sports sciences, and health sciences—students gain firsthand experience in how the same data can be evaluated from engineering, physiological, and clinical perspectives.
Fieldwork outside the laboratory can also be a significant component of this experience. Indeed, EHR-LAB’s collaborative activities with various universities and research groups include joint projects conducted with the Mersin University Faculty of Sports Sciences. Thus, alongside controlled laboratory experiments, students [engage in work] under real-world field conditions...
