A Millimeter-Wave MIMO Radar Network for Human Activity Recognition and Fall Detection - Fröhlich, Ann-Christine; Mejdani, Desar; Engel, Lukas; Bräunig, Johanna; Kammel, Christoph; Vossiek, Martin; Ullmann, Ingrid (2024 IEEE Radar Conference (RadarConf24), 2024)

Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning - Bräunig, Johanna; Mejdani, Desar; Krauß, Daniel; Grießhammer, Stefan; Richer, Robert; Schüßler, Christian; Yip, Julia; Steigleder, Tobias; Ostgathe, Christoph; Eskofier, Björn; Vossiek, Martin (2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2023)
The accurate detection and quantification of activities of daily life (ADL) are crucial for assessing the health status of palliative patients to allow an optimized treatment in the last phase of life. Current evaluation methods heavily rely on subjective self-reports or external observations by clinical staff, lacking objectivity. To address this limitation, we propose a radar-based approach for recognizing ADLs in a palliative care context. In our proof of concept study, we recorded five different ADLs relevant to palliative care, all occurring within a hospital bed, from N=14 healthy participants (57% women, aged 28.6 ± 5.3years). All movements were recorded using two frequency-modulated continuous wave radar systems measuring velocity, range, and angle. A convolutional neural network combined with long short-term memory achieved a classification accuracy of 99.8 ± 0.4% across five cross-validation folds. Furthermore, we compare our initial approach, which takes into account all dimensions of the available radar data, to a simplified version, where only velocity information over time is fed into the network. While these results demonstrate the high potential of radar-based sensing to automatically detect and quantify activities in a palliative care context, future work is still necessary to assess the applicability to real-world hospital scenarios.<br
Keywords: Hospitals; Radar measurements; Pipelines; Neural networks; Radar; Particle measurements; Frequency measurement

Radar-Based Tremor Quantification Using Deep Learning for Improved Parkinson’s and Palliative Care Assessment - Mejdani, Desar; Bräunig, Johanna; Grießhammer, Stefan; Krauß, Daniel; Steigleder, Tobias; Engel, Lukas; Jukic, Jelena; Rozhdestvenskaya, Anna; Winkler, Jürgen; Eskofier, Björn; Ostgathe, Christoph; Vossiek, Martin (IEEE Transactions on Radar Systems, 2024)
Tremor is one of the most prevalent movement disorders, which is especially observed in patients with Parkinson’s disease (PD) and other conditions common in palliative care (PC). Effective treatment and monitoring of disease progression are crucial in the context of PC for patients suffering from movement disorders. To this aim, accurate and continuous detection and assessment of tremor characteristics, such as the tremor frequency, is required. Current evaluations by clinicians conducted during sporadic consultations are subjective and intermittent. Radar sensors provide continuous, objective evaluations of tremor motion in patient monitoring, offering a contactless, light-independent, and privacy-preserving method that directly measures tremor’s radial motion through the Doppler effect. As previous radar-based research lacks continuous tremor monitoring in realistic scenarios, this study uses a frequency-modulated continuous-wave (FMCW) radar to detect subtle tremor motions and estimates their frequencies amid challenges such as large body motion interference in a clinical setting. Seventeen healthy participants were instructed to mimic tremors in their right hand while performing three diagnostics movements frequently used in tremor assessment, and two activities that were inspired by common daily tasks encountered in PC settings. Tremor detection and frequency estimation was enabled using suitable radar signal preprocessing followed by a neural network comprising convolutional and recurrent layers. Reference frequencies were obtained from an inertial measurement unit (IMU) attached to the participants’ right hands. Cross-validation revealed a mean absolute error (MAE) of 1.47 Hz in radar-based frequency estimation compared with the reference and a 90% accuracy in distinguishing the presence or absence of tremor, highlighting the proposed approach’s high potential for future tremor assessmen
Keywords: Sensors; Radar antennas; Palliative care; Frequency estimation; Doppler effect; Chirp; Kinetic theory; Doppler radar; Deep learning; Parkinson's disease; Deep learning; neural network; palliative care (PC); Parkinson’s disease (PD); radar; tremor