Efficacy of a smartphone-based reappraisal training against depression and benefits of enhancing it with facial expression - Rupp, Lydia; Capito, Klara; Gmelch, Lena Marie; Böhme, Stephanie; Richer, Robert; Sadeghi, Misha; Rahimi, Farnaz; Abel, Luca; Eskofier, Björn; Berking, Matthias (Society for Psychotherapie Research 54th International Annual Meeting, 2023)

EmpkinS-EKSpression-BioFACEBack: Ergebnisse eines randomisiert-kontrollierten Trials zu gesichtsmuskelfokussiertem Biofeedback bei depressiven Erkrankungen - Schindler-Gmelch, Lena; Rupp, Lydia; Rahimi, Farnaz; Sadeghi, Misha; Keinert, Eva Marie; Hager, Malin; Capito, Klara; Richer, Robert; Eskofier, Björn; Berking, Matthias (4. Deutscher Psychotherapie Kongress, 2025)

EmpkinS-EKSpression: Ergebnisse einer Randomisiert-kontrollierten klinischen Studie eines Emotions-gestützten Trainings zur Kognitiven Umstrukturierung - Rupp, Lydia; Schindler-Gmelch, Lena; Sadeghi, Misha; Rahimi, Farnaz; Keinert, Eva Marie; Hager, Malin; Capito, Klara; Richer, Robert; Eskofier, Björn; Berking, Matthias (4. Deutscher Psychotherapie Kongress, 2025)

Facing depression: evaluating the efficacy of the EmpkinS-EKSpression reappraisal training augmented with facial expressions – protocol of a randomized controlled trial - Keinert, Eva Marie; Schindler-Gmelch, Lena; Rupp, Lydia; Sadeghi, Misha; Capito, Klara; Hager, Malin; Rahimi, Farnaz; Richer, Robert; Egger, Bernhard; Eskofier, Björn; Berking, Matthias (BMC Psychiatry, 2024)
Background Dysfunctional depressogenic cognitions are considered a key factor in the etiology and maintenance of depression. In cognitive behavioral therapy (CBT), the current gold-standard psychotherapeutic treatment for depression, cognitive restructuring techniques are employed to address dysfunctional cognitions. However, high drop-out and non-response rates suggest a need to boost the efficacy of CBT for depression. This might be achieved by enhancing the role of emotional and kinesthetic (i.e., body movement perception) features of interventions. Therefore, we aim to evaluate the efficacy of a cognitive restructuring task augmented with the performance of anti-depressive facial expressions in individuals with and without depression. Further, we aim to investigate to what extent kinesthetic markers are intrinsically associated with and, hence, allow for the detection of, depression. Methods In a four-arm, parallel, single-blind, randomized controlled trial (RCT), we will randomize 128 individuals with depression and 128 matched controls without depression to one of four study conditions: (1) a cognitive reappraisal training (CR); (2) CR enhanced with instructions to display anti-depressive facial expressions (CR + AFE); (3) facial muscle training focusing on anti-depressive facial expressions (AFE); and (4) a sham control condition. One week after diagnostic assessment, a single intervention of 90–120-minute duration will be administered, with a subsequent follow-up two weeks later. Depressed mood will serve as primary outcome. Secondary outcomes will include current positive mood, symptoms of depression, current suicidality, dysfunctional attitudes, automatic thoughts, emotional state, kinesthesia (i.e., facial expression, facial muscle activity, body posture), psychophysiological measures (e.g., heart rate (variability), respiration rate (variability), verbal acoustics), as well as feasibility measures (i.e., treatment integrity, compliance, usability, acceptability). Outcomes will be analyzed with multiple methods, such as hierarchical and conventional linear models and machine learning. Discussion If shown to be feasible and effective, the inclusion of kinesthesia into both psychotherapeutic diagnostics and interventions may be a pivotal step towards the more prompt, efficient, and targeted treatment of individuals with depressio

Harnessing multimodal approaches for depression detection using large language models and facial expressions - Sadeghi, Misha; Richer, Robert; Egger, Bernhard; Schindler-Gmelch, Lena; Rupp, Lydia; Rahimi, Farnaz; Berking, Matthias; Eskofier, Björn (npj Mental Health Research, 2024)
Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysi
Keywords: Depression detection; mental health assessment; large language models; text analysis; E-DAIC dataset; speech quality assessment; visual features; facial expressions

Realtime Laser Beam Steering and Calibration Method for Coherent Biomedical Distance and Motion Sensing - Schmidt, Marius; Sadeghi, Misha; Rahimi, Farnaz; Eskofier, Björn; Buglagil, Arda; Schmauß, Bernhard; Carlowitz, Christian (CLEO: Science and Innovations, CLEO: S and I 2024 in Proceedings CLEO 2024, Part of Conference on Lasers and Electro-Optics, 2024)
This paper presents a depth camera based 3D position tracking and calibration method for realtime millimeter-accuracy galvanometer laser beam steering that enables high sensitivity coherent optical distance and movement sensing for biomedical applications.
CRIS-Team Scopus Importer:2024-10-04

Simultaneous Control of Human Hand Joint Positions and Grip Force via HD-EMG and Deep Learning - Rahimi, Farnaz; Badamchizadeh, Mohammad Ali; Simpetru, Raul; Ghaemi, Sehraneh; Eskofier, Björn; Del Vecchio, Alessandro (2025)
In myoelectric control, simultaneous control of multiple degrees of freedom can be challenging due to the dexterity of the human hand. Numerous studies have focused on hand functionality, however, they only focused on a few degrees of freedom. In this paper, a 3DCNN-MLP model is proposed that uses high-density sEMG signals to estimate 20 hand joint positions and grip force simultaneously. The deep learning model maps the muscle activity to the hand kinematics and kinetics. The proposed models' performance is also evaluated in estimating grip forces with real-time resolution. This paper investigated three individual dynamic hand movements (2pinch, 3pinch, and fist closing and opening) while applying forces in 10% and 30% of the maximum voluntary contraction (MVC). The results demonstrated significant accuracy in estimating kinetics and kinematics. The average Euclidean distance across all joints and subjects was 11.01 ± 2.22 mm and the mean absolute error for offline and real-time force estimation were found to be 0.8 ± 0.33 N and 2.09 ± 0.9 N respectively. The results demonstrate that by leveraging high-density sEMG and deep learning, it is possible to estimate human hand dynamics (kinematics and kinetics), which is a step forward to practical prosthetic hands.<br
https://cris.fau.de/converis/publicweb/Publication/335935342
Keywords: Deep learning; surface Electromyography; real-time; multi-DoF prediction; grip force

Simultaneous Estimation of Digit Tip Forces and Hand Postures in a Simulated Real-life Condition with High-density Electromyography and Deep Learning - Rahimi, Farnaz; Badamchizadeh, Mohammad Ali; Ghaemi, Sehraneh; Del Vecchio, Alessandro (IEEE Journal of Biomedical and Health Informatics, 2024)
In myoelectric control, continuous estimation of multiple degrees of freedom has an important role. Most studies have focused on estimating discrete postures or forces of the human hand but for a practical prosthetic system, both should be considered. In daily life activities, hand postures vary for grasping different objects and the amount of force exerted on each fingertip depends on the shape and weight of the object. This study aims to investigate the feasibility of continuous estimation of multiple degrees of freedom. We proposed a reach and grasp framework to study both absolute fingertip forces and hand movement types using deep learning techniques applied to high-density surface electromyography (HD-sEMG). Four daily life grasp types were examined and absolute fingertip forces were simultaneously estimated while grasping various objects, along with the grasp types. We showed that combining a 3-dimensional Convolutional Neural Network (3DCNN) with a Long Short-term Memory (LSTM) can reliably and continuously estimate the digit tip forces and classify different hand postures in human individuals. The mean absolute error (MAE) and Pearson correlation coefficient (PCC) results of the force estimation problem across all fingers and subjects were 0.46 ± 0.23 and 0.90 ± 0.03% respectively and for the classification problem, they were 0.04 ± 0.01 and 0.97 ± 0.02%. The results demonstrated that both absolute digit tip forces and hand postures can be successfully estimated through deep learning and HD-sEMG.
CRIS-Team Scopus Importer:2024-01-26
Keywords: 3DCNN; Biomedical measurement; Deep learning; deep learning; finger force prediction; Force; Force measurement; FSR sensors; Grasping; High-density sEMG (HD-sEMG); LSTM; motion intention recognition; Muscles; Sensors

Simultaneous Estimation of Digit Tip Forces and Hand Postures in a Simulated Real-Life Condition With High-Density Electromyography and Deep Learning - Rahimi, Farnaz; Badamchizadeh, Mohammad Ali; Ghaemi, Sehraneh; Del Vecchio, Alessandro (IEEE Journal of Biomedical and Health Informatics, 2024)
In myoelectric control, continuous estimation of multiple degrees of freedom has an important role. Most studies have focused on estimating discrete postures or forces of the human hand but for a practical prosthetic system, both should be considered. In daily life activities, hand postures vary for grasping different objects and the amount of force exerted on each fingertip depends on the shape and weight of the object. This study aims to investigate the feasibility of continuous estimation of multiple degrees of freedom. We proposed a reach and grasp framework to study both absolute fingertip forces and hand movement types using deep learning techniques applied to high-density surface electromyography (HD-sEMG). Four daily life grasp types were examined and absolute fingertip forces were simultaneously estimated while grasping various objects, along with the grasp types. We showed that combining a 3-dimensional Convolutional Neural Network (3DCNN) with a Long Short-term Memory (LSTM) can reliably and continuously estimate the digit tip forces and classify different hand postures in human individuals. The mean absolute error (MAE) and Pearson correlation coefficient (PCC) results of the force estimation problem across all fingers and subjects were 0.46 ± 0.23 and 0.90 ± 0.03% respectively and for the classification problem, they were 0.04 ± 0.01 and 0.97 ± 0.02%. The results demonstrated that both absolute digit tip forces and hand postures can be successfully estimated through deep learning and HD-sEMG.<br
Keywords: 3DCNN; deep learning; finger force prediction; FSR sensors; high-density sEMG (HD-sEMG); LSTM; motion intention recognition.