F. Bajraktari, N. Roβkopf, and P. P. Pott, “CNN-Based Intention Recognition Using Body-Worn Inertial Measurement Units,” in
2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), Jun. 2023, pp. 760–765. doi:
10.1109/CBMS58004.2023.00315.
Abstract
This study explores the potential of using a sensor system and deep learning algorithm for automatic recognition and classification of human movement intentions to enhance the control of active orthopedic aids such as orthoses, prostheses, and exoskeletons. The sensor system consists of four inertial measurement units (IMU) attached to the thighs, lower back, and chest of a person, measuring acceleration and rotational velocity in three axes each. A dataset was generated from 20 healthy individuals performing various movements from a standing position, and preprocessed and segmented into time windows. The data was fed into a convolutional neural network (CNN) capable of classifying the time windows into seven classes. The network achieved a classification accuracy of up to 82 %. The results demonstrate the potential of the proposed system for successful application in the control of assistance systems, which can improve the operability and effectiveness of orthopedic aids.BibTeX
F. Bajraktari, K. Fleissner, and P. P. Pott, “A deep learning based instrument detection approach for automated surgical systems,” in Proceedings on Automation in Medical Engineering, 2023, vol. 2, no. 1.
BibTeX
J. Liu, F. Bajraktari, R. Rausch, and P. P. Pott, “3D Reconstruction of Forearm Veins Using NIR-Based Stereovision and Deep Learning,” in
2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), Jun. 2023, pp. 57–60. doi:
10.1109/CBMS58004.2023.00192.
Abstract
In this paper, the development of a cost-effective assistance system for venipuncture is presented. The system locates forearm veins through near-infrared imaging, depth estimation, deep learning segmentation, and 3D reconstruction. A single-board computer was integrated with two infrared cameras and two 760 nm near-infrared (NIR) LEDs to capture and process stereo images. The depth estimation was achieved through stereo triangulation. A deep learning model based on the U-Net architecture with an attention mechanism and a training dataset of 900 images from 40 participants was used for vein segmentation. Depth information and segmented veins were combined to enable a 3D visualization of the veins. The results show a Jaccard-Score of 92.80 % for vein segmentation and an average reprojection error of 0.48 pixels for the 3D reconstruction.BibTeX
F. Bajraktari, J. Liu, and P. P. Pott, “Methods of Contactless Blood Pressure Measurement,”
Current Directions in Biomedical Engineering, vol. 8, no. 2, Art. no. 2, 2022, doi:
doi:10.1515/cdbme-2022-1112.
BibTeX
F. Bajraktari, K. Fleissner, and P. P. Pott, “A comparison of two CNN-based instrument detection approaches for automated surgical assistance systems,”
Current Directions in Biomedical Engineering, vol. 9, no. 1, Art. no. 1, 2023, doi:
doi:10.1515/cdbme-2023-1150.
BibTeX
J. Liu, F. Bajraktari, Ö. Atmaca, T. J. Ly, and P. P. Pott, “Microscale Sensor Fabrication on Curved Needle Surfaces,”
Current Directions in Biomedical Engineering, vol. 8, no. 2, Art. no. 2, 2022, doi:
doi:10.1515/cdbme-2022-1161.
BibTeX
J. Mayer, F. Bajraktari, J. Liu, and P. P. Pott, “Technik für die sanfte Blutentnahme,” mt medizintechnik, no. 3, Art. no. 3, 2024.
BibTeX
F. Bajraktari and P. P. Pott, “Multi-view surgical phase recognition during laparoscopic cholecystectomy,”
Current Directions in Biomedical Engineering, vol. 10, no. 4, Art. no. 4, 2024, doi:
doi:10.1515/cdbme-2024-2011.
BibTeX
Ö. Atmaca, J. Liu, T. J. Ly, F. Bajraktari, and P. P. Pott, “Spatial sensitivity distribution assessment and Monte Carlo simulations for needle‐based bioimpedance imaging during venipuncture using the finite element method,”
International Journal for Numerical Methods in Biomedical Engineering, May 2024, doi:
10.1002/cnm.3831.
Abstract
Despite being among the most common medical procedures, needle insertions suffer from a high error rate. Impedance measurements using electrode-equipped needles offer promise for improved tissue targeting and reduced errors. Impedance visualization usually requires an extensive pre-measured impedance dataset for tissue differentiation and knowledge of the electric fields contributing to the resulting impedances. This work presents two finite element simulation approaches for both problems. The first approach describes the generation of a multitude of impedances with Monte Carlo simulations for both, homogeneous and inhomogeneous tissue to circumvent the need to rely on previously measured data. These datasets could be used for tissue discrimination. The second method describes the simulation of the spatial sensitivity distribution of an electrode layout. Two singularity analysis methods were employed to determine the bulk of the sensitivity within a finite volume, which in turn enables consistent 3D visualization. The modeled electrode layout consists of 12 electrodes radially placed around a hypodermic needle. Electrical excitation was simulated using two neighboring electrodes for current carriage and voltage pickup, which resulted in 12 distinct bipolar excitation states. Both, the impedance simulations and the respective singularity analysis methods were compared with each other. The results show that the statistical spread of impedances is highly dependent on the tissue type and its inhomogeneities. The bounded bulk of sensitivities of both methods are of similar extent and symmetry. Future models should incorporate more detailed tissue properties such as anisotropy or changing material properties due to tissue deformation to gain more accurate predictions.BibTeX
