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20 results on '"Eskofier, Bjorn"'

1. Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units

Author

Parikh, Aditya, Sadeghi, Misha, and Eskofier, Bjorn

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disorder. Traditional diagnostic methods rely on subjective assessments, necessitating objective approaches for accurate diagnosis. Our study investigates the use of facial action units (AUs) and emotions as biomarkers for depression. We analyzed facial expressions from video data of participants classified with or without depression. Our methodology involved detailed feature extraction, mean intensity comparisons of key AUs, and the application of time series classification models. Furthermore, we employed Principal Component Analysis (PCA) and various clustering algorithms to explore the variability in emotional expression patterns. Results indicate significant differences in the intensities of AUs associated with sadness and happiness between the groups, highlighting the potential of facial analysis in depression assessment.

Published
2024

2. A Wireless Joint Communication and Localization EMG-Sensing Concept for Movement Disorder Assessment

Author

Bruckner, Stefan, Kolpak, Jasmin, Michler, Fabian, Shanin, Nikita, Schober, Robert, Hagelauer, Amelie, Weigel, Robert, Gasner, Heiko, Winkler, Jurgen, Eskofier, Bjorn M., and Vossiek, Martin

Abstract

Real-time sensory recording of the musculoskeletal system function is an important tool for the diagnosis, treatment planning, and optimal treatment execution of diseases, such as Parkinson's disease and osteoarthritis. This article presents a new wireless joint communication and localization electromyography (EMG)-sensing concept. An on-body sensor beacon measures EMG signals and wirelessly transmits them. At the same time, the spatial position and movement of the beacon is determined with high precision in real time using these transmitted radio signals. The seamless integration of multiple sensors avoids the need to synchronize and individually set up multiple independently operating sensors. An outstanding feature of the radio localization approach is that it does not require proprietary ultra-wideband signals or complicated time synchronization protocols, allowing for small and energy-efficient implementation. To demonstrate this novel concept, a wireless 3D-localizable EMG sensor was developed and experimentally evaluated. This new type of sensing concept allows, for the first time, the time-synchronous measurement of muscle activity and the underlying movement of the associated body part.

Published
2023
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7. Guest Editorial Trustworthy and Collaborative AI for Personalised Healthcare Through Edge-of-Things

Author

Ren, Zhao, Schuller, Bjorn W., Eskofier, Bjorn M., Nguyen, Thanh Tam, and Nejdl, Wolfgang

Abstract

From diagnosis to therapies, the development of artificial intelligence (AI) has facilitated improvements in personalised healthcare applications. The evolution of AI in healthcare is closely related to the changes in the types and volumes of data which we need to deal with. The first generation of healthcare technologies, represented by the highly successful relational databases, are designed to handle structured data involving patient demographics, patient care, treatments, and outcomes of those treatments. Big Data platforms, which are representative of the current mainstream healthcare technologies, are built to process unstructured data from sources like electronic health records, medical imaging, genomic sequencing, and pharmaceutical research. The next generation of healthcare technologies will potentially be Edge-of-Things data, represented by massive amount of streaming data generated from Internet-of-Things frameworks, Cloud systems, and Edge computing platforms.

Published
2023
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11. Sampling rate impact on energy consumption of biomedical signal processing systems

Author

Tobola, Andreas, primary, Streit, Franz J., additional, Espig, Chris, additional, Korpok, Oliver, additional, Sauter, Christian, additional, Lang, Nadine, additional, Schmitz, Bjorn, additional, Hofmann, Christian, additional, Struck, Matthias, additional, Weigand, Christian, additional, Leutheuser, Heike, additional, Eskofier, Bjorn M., additional, and Fischer, Georg, additional

Published
2015
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19. Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease.

Author

Barth, Jens, Sunkel, Michael, Bergner, Katharina, Schickhuber, Gerald, Winkler, Jurgen, Klucken, Jochen, and Eskofier, Bjorn

Abstract

Objective and rater independent analysis of movement impairment is one of the most challenging tasks in medical engineering. Especially assessment of motor symptoms defines the clinical diagnosis in Parkinson's disease (PD). A sensor-based system to measure the movement of the upper and lower extremities would therefore complement the clinical evaluation of PD. [ABSTRACT FROM PUBLISHER]

Published
2012
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20. Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities.

Author

Barth J, Oberndorfer C, Kugler P, Schuldhaus D, Winkler J, Klucken J, and Eskofier B

Subjects
Adult, Aged, Female, Humans, Male, Middle Aged, Activities of Daily Living, Algorithms, Gait, Monitoring, Physiologic instrumentation, Monitoring, Physiologic methods, Parkinson Disease physiopathology, Shoes, Wireless Technology
Abstract

The segmentation of gait signals into single steps is an important basis for objective gait analysis. Only a precise detection of step beginning and end enables the computation of step parameters like step height, variability and duration. A special challenge for the application is the accurateness of such an algorithm when based on signals from daily live activities. In this study, gyroscopes were attached laterally to sport shoes to collect gait data. For the automated step segmentation, subsequence Dynamic Time Warping was used. 35 healthy controls and ten patients with Parkinson's disease performed a four times ten meter walk. Furthermore 4 subjects were recorded during different daily life activities. The algorithm enabled counting steps, detecting precisely step beginning and end and rejecting other movements. Results showed a recognition rate of steps during ten meter walk exercises of 97.7% and in daily life activities of 86.7%. The segmentation procedure can be used for gait analysis from daily life activities and can constitute the basis for computation of precise step parameters. The algorithm is applicable for long-term gait monitoring as well as for analyzing gait abnormalities.

Published
2013
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