Biology subjects

Arami, A.

Publications and source records attributed to Arami, A..

6 recordsLinked to original sources

bioRxiv2025

Hierarchical Coarse-to-Fine cGAN for Subtype-Specific Freezing of Gait Signal Generation

Freezing of gait (FOG), a debilitating symptom of Parkinsons disease, manifests in subtypes as shuffling, trembling, or akinesias, with occurrence and frequency varying across patients. While deep learning (DL) models show promise in FOG detection, their robustness and generalization across subtypes are limited by data scarcity and imbalances between FOG/non-FOG classes and among subtypes. To address this, we propose a subtype-aware FOG augmentation technique enabling training of DL models to perform consistently across subtypes. Specifically, we introduce Hierarchical Coarse-to-Fine conditional Generative Adversary Network (Hi-CF cGAN), a two-stage model that generates subtype-conditioned FOG-like ankle accelerations that are realistic and diverse, as verified through visualization, UMAPs, and Maximum Mean Discrepancy comparison against real signals. We evaluate its effectiveness by training CNNs for FOG detection with both general (subtype-stratified) and personalized (subtype-variant, based on patient-specific subtype composition) augmentation via Hi-CF cGAN, benchmarking against classical augmentations and baseline (no augmentation). Compared to baseline, general augmentation with Hi-CF cGAN effectively improves average detection rates of FOG, trembling FOG, and especially the previously overlooked minor subtypes, shuffling FOG (from 66.8% to 81.6%) and akinesia FOG (from 58.7% to 77.9%). These improvements exceed those of classical augmentations, demonstrating superior realism, richness, and adaptability of Hi-CF cGAN-generated data in addressing FOG/non-FOG and subtype imbalances. Personalized augmentation further enhances accuracy on targeted subtype(s) compared to general augmentation, highlighting its potential for tailored model optimization.

bioengineering↗

bioRxiv2025

Role of Speed Regulation and Speed Modulation in Velocity-Field Based Control

The velocity vector field (flow) controller is a well-established control strategy for lower limb exoskeletons. In this paper, we analyze this controller and propose modifications to improve its performance. We demonstrate that flow control acts as a variable proportional-derivative error regulator, where the parameter {Gamma} represents the desired norm of the hip-knee joint velocity vector (path speed). Based on this, we introduce two modifications to {Gamma}: (1) a constant {Gamma} set to the mean desired path speed, and (2) a variable {Gamma} that mimics natural path speed during unassisted walking. We compared the modified flow controllers with a slow-{Gamma} version in experiments involving seven participants walking on a treadmill at 0.6m/s, 0.8m/s, and 1.0m/s. Compared to the slow-{Gamma} controller, the RMS tracking error decreased by 30.7{+/-} 11.3% and the range of motion of the knee increased by 48.2 {+/-} 5.5% for the mean-{Gamma} controller, while the variable-{Gamma} controller had 32.4 {+/-} 14.7% smaller RMS error and 50.5 {+/-} 6.5% larger range of motion of the knee. Additionally, the slow-{Gamma} controller consistently applied resistive power, whereas participants reported more comfortable and natural gait with the modified controllers. We also compared them with the original tuning of flow controller, with results indicating superior performance from the proposed modifications. These findings demonstrate effectiveness across different walking speeds and offer a tuning strategy for future flow controller use.

bioengineering↗

bioRxiv2025

Subtype-Specific Freezing of Gait Detection Strategies via Feature-Masked-Based CNN

ObjectiveFreezing of gait (FOG), a disabling symptom of Parkinsons disease, varies in manifestations and motion contexts. Its heterogeneity motivates subtype categorization such as manifestation-specific subtypes (akinesia, trembling or shuffling) or motion-specific subtypes (gait-initiation, walking or turning). Despite numerous promising deep learning FOG detection studies, few consider FOG heterogeneity. It remains unclear whether different subtypes require distinct detection strategies, and whether tailoring subtype-specific models could enhance detection generalizability across subtypes. Methods: To address these questions, we categorize FOG data into manifestation- or motion-specific subtypes and derive their corresponding detection strategies as interpretable feature masks. We then propose a feature-mask-based CNN that explicitly embeds the identified strategies. Using waist-mounted 3D accelerometer data, a general CNN and subtype-specific CNNs are trained. Results: According to feature-mask analysis, motion-specific subtypes share a common detection strategy, whereas manifestation-specific subtypes require distinct strategies. Manifestation models exhibit enhanced generalizability across subtypes compared to the general model, boosting the overall average FOG detection sensitivity by 24.95%{+/-}9.80% and specificity by 18.29%{+/-}8.71%. Conversely, motion models reduce the overall FOG sensitivity by 1.89%{+/-}8.74% and specificity by 5.17%{+/-}10.76%. Conclusions: The detection strategy is mainly driven by manifestation composition of the data. The general model favors the dominant manifestation-specific subtype group(s), a bias corrected by tailored manifestation-specific strategies. No comparable benefit arises from motion models due to their similar manifestation compositions. Significance: This study interpretably reveals the detection strategies required by different FOG subtypes and demonstrates the effectiveness of subtype-specific tailoring in improving FOG detection generalizability.

bioengineering↗

bioRxiv2023

Coordinated human-exoskeleton locomotion emerges from regulating virtual energy

Lower-limb exoskeletons showed great potential for the gait rehabilitation of individuals with motor impairments; however, maintaining human-exoskeleton coordination is still a challenge. Human-exoskeleton coordination problem, referred to as any mismatch or asynchrony between the users intended trajectories and exoskeleton desired trajectories, results in sub-optimal gait performance, particularly for the individuals with residual motor ability. Here, we investigate the virtual energy regulator (VER)s ability to generate coordinated locomotion in lower limb exoskeleton in a study on nine individuals. A comprehensive analysis of metrics obtained at three different speeds demonstrates the emergence of natural and coordinated locomotion as a result of a shared control between human and exoskeleton maintained with the VER. We also introduce a metric to measure the users contribution to gait and demonstrated that the resultant locomotion limit cycle is a linear combination of human-intended limit cycle and the VERs limit cycle. This results may have implications about how central nervous system controls our locomotion.

bioengineering↗

bioRxiv2023

Human-exoskeleton interaction force estimation in Indego exoskeleton

Accurate interaction force estimation can play an important role in optimization human-robot interaction in exoskeleton. In this work, we propose a novel approach for system identification of exoskeleton dynamics in presence of interaction forces as a whole multi-body system regardless of gait phase or any assumption on human-exoskeleton interaction. We hanged the exoskeleton through a linear spring and excited the exoskeleton joints with chirp commands while measuring the exoskeleton-environment interaction force. Several structures of neural networks have been trained to model the exoskeleton passive dynamics and estimate the interaction force. Our testing results indicated that a deep neural network with 250 neurons and 10 time delays can obtain sufficiently accurate estimation of the interaction force, resulting in 1.23 of RMSE on Z-normalized applied torques and 0.89 of adjusted R2.

bioengineering↗

bioRxiv2021

Online Reference Trajectory Adaptation: A Personalized Control Strategy for Lower Limb Exoskeletons

This paper presents a novel method for reference trajectory adaptation in lower limb rehabilitation exoskeletons during walking. Our adaptation rule is extracted from a cost function that penalizes both interaction force and trajectory modification. By adding trajectory modification term into the cost function, we restrict the boundaries of the reference trajectory adaptation according to the patients motor capacity. The performance of the proposed adaptation method is studied analytically in terms of convergence and optimality. We also developed a realistic dynamic walking simulator and utilized it in performance analysis of the presented method. The proposed trajectory adaptation technique guarantees convergence to a stable, reliable, and rhythmic reference trajectory with no prior knowledge about the human intended motion. Our simulations demonstrate the convergence of exoskeleton trajectories to those of simulated healthy subjects while the exoskeleton trajectories adapt less to the trajectories of patients with reduced motor capacity (less reliable trajectories). Furthermore, the gait stability and spatiotemporal parameters such as step time symmetry and minimum toe off clearance enhanced by the adaptation in all subjects. The presented mathematical analysis and simulation results show the applicability and effectiveness of the proposed method and its potential to be applied for trajectory adaptation in lower limb rehabilitation exoskeletons.

bioengineering↗