Neha Das

Neha Das

Doctoral student

Publications

You can also find my articles on my Google Scholar profile.

Explainable Unsupervised Anomaly Detection for Identifying Compensatory Motor Behavior in Stroke Rehabilitation

Published in TechRxiv (2025), 2025

Occurrence of compensatory motor behaviors is common after stroke and other movement disorders, and can hinder patient rehabilitation. Detection of such behaviors is essential but traditionally relies on subjective, labor-intensive manual assessment. While supervised learning has been explored for automation, its effectiveness is constrained by inter-individual variability in compensatory strategies and the difficulty of obtaining detailed labels. To address these challenges, we present an unsupervised anomaly detection framework that models healthy motion distributions using Probabilistic Movement Primitives and quantifies deviations to detect potential compensations. The transparent structure of the proposed framework enables localization of compensation sources and differentiation between compensating and inhibited degrees of freedom, thereby enhancing its explainability and providing deeper insight into its outcomes. The framework was validated on a reaching-motion dataset, and achieved high accuracy in detecting compensation among stroke patients (F1 = 0.95). Trials from the evaluation dataset were further annotated with one or more compensation sources by multiple raters, yielding a rich dataset with quantifiable label uncertainties. Evaluation indicated that the framework achieved high performance in isolating compensation sources, particularly when annotator agreement is high.

Cite with: N. Das, S. Endo, S. Patel, M. Rossini, A. De Crignis, E. Guanziroli, G. Palumbo, A. Specchia, C. Krewer and S. Hirche, "Explainable Unsupervised Anomaly Detection for Identifying Compensatory Motor Behavior in Stroke Rehabilitation," 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Heidelberg, Germany, 2024, pp. 1320-1327, doi: 10.1109/BioRob60516.2024.10719910 https://www.techrxiv.org/users/993864/articles/1355353-explainable-unsupervised-anomaly-detection-for-identifying-compensatory-motor-behavior-in-stroke-rehabilitation

Framework for Learning a Hand Intent Recognition Model from sEMG for FES-Based Control

Published in 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 2024

Stroke survivors and individuals with neuromus-cular disorders often experience motor function impairments, particularly during hand movements crucial for activities of daily living (ADL). Functional Electrical Stimulation (FES) has emerged as a potential assistive and rehabilitative technique to address these limitations. However, accurately determining user intent during FES poses a significant challenge. This work proposes a framework for rapidly learning a model of the user’s hand intent from surface electromyography (sEMG) signals, specifically for continuous FES-based control of the ipsilateral hand. The framework systematically collects data from expected volitional and FES-evoked hand motions, followed by training a logistic regression model for intent classification. The study demonstrates that the proposed model can learn from limited data and compares favorably to deep neural nets trained on the same dataset. This model is able to recognize user intent with high accuracy even during concurrent FES stimulation.

Cite with: N. Das, S. Endo, H. Kavianirad and S. Hirche, "Framework for Learning a Hand Intent Recognition Model from sEMG for FES-Based control," 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Heidelberg, Germany, 2024, pp. 1320-1327, doi: 10.1109/BioRob60516.2024.10719910 https://ieeexplore.ieee.org/abstract/document/10719910

Online detection of compensatory strategies in human movement with supervised classification: a pilot study.

Published in Frontiers in Neurorobotics, 2023

Stroke survivors often compensate for the loss of motor function in their distal joints by altered use of more proximal joints and body segments. Since this can be detrimental to the rehabilitation process in the long-term, it is imperative that such movements are indicated to the patients and their caregiver. This is a difficult task since compensation strategies are varied and multi-faceted. Recent works that have focused on supervised machine learning methods for compensation detection often require a large training dataset of motions with compensation location annotations for each time-step of the recorded motion. In contrast, this study proposed a novel approach that learned a linear classifier from energy-based features to discriminate between healthy and compensatory movements and identify the compensating joints without the need for dense and explicit annotations.

Cite with: Das, N., Endo, S., Patel, S., Krewer, C., & Hirche, S. Online detection of compensatory strategies in human movement with supervised classification: a pilot study." 2023 Frontiers in Neurorobotics, 17, 1155826 https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2023.1155826/full

Deep Learning based Uncertainty Decomposition for Real-time Control.

Published in 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023, 2023

Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty that arises from measurement noise can often be explicitly modeled given a parametric description, it can be harder to model epistemic uncertainty, which describes the presence or absence of training data. The latter can be particularly useful for implementing exploratory control strategies when system dynamics are unknown. We propose a novel method for detecting the absence of training data using deep learning, which gives a continuous valued scalar output between 0 (indicating low uncertainty) and 1 (indicating high uncertainty). We utilize this detector as a proxy for epistemic uncertainty and show its advantages over existing approaches on synthetic and real-world datasets. Our approach can be directly combined with aleatoric uncertainty estimates and allows for uncertainty estimation in real-time as the inference is sample-free unlike existing approaches for uncertainty modeling. We further demonstrate the practicality of this uncertainty estimate in deploying online data-efficient control on a simulated quadcopter acted upon by an unknown disturbance model.

Cite with: Das, N., Umlauft, J., Lederer, A., Capone, A., Beckers, T., & Hirche, S. Deep Learning based Uncertainty Decomposition for Real-time Control." 2023 IFAC-PapersOnLine, 56(2), 847-853 https://www.sciencedirect.com/science/article/pii/S2405896323020803

Learning Extended Body Schemas from Visual Keypoints for Object Manipulation.

Published in Arxiv, 2021

Humans have impressive generalization capabilities when it comes to manipulating objects and tools in completely novel environments. These capabilities are, at least partially, a result of humans having internal models of their bodies and any grasped object. How to learn such body schemas for robots remains an open problem. In this work, we develop an self-supervised approach that can extend a robot’s kinematic model when grasping an object from visual latent representations.

Cite with: S. Bechtle, N. Das and F. Meier, "Learning Extended Body Schemas from Visual Keypoints for Object Manipulation." 2020 arXiv preprint arXiv:2011.03882. https://arxiv.org/abs/2011.03882

Model-Based Inverse Reinforcement Learning from Visual Demonstrations

Published in Conference on Robot Learning (CoRL 2020), 2021

Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good dynamics models, developing algorithms that scale to high-dimensional state-spaces and being able to learn from both visual and proprioceptive demonstrations. In this work, we present a gradient-based inverse reinforcement learning framework that utilizes a pre-trained visual dynamics model to learn cost functions when given only visual human demonstrations. The learned cost functions are then used to reproduce the demonstrated behavior via visual model predictive control. We evaluate our framework on hardware on two basic object manipulation tasks.

Cite with: N. Das, S. Bechtle, T. Davchev, D. Jayaraman, A. Rai and F. Meier, "Model-Based Inverse Reinforcement Learning from Visual Demonstrations," 2020 Conference on Robot Learning (CoRL) [https://corlconf.github.io/paper_432](https://proceedings.mlr.press/v155/das21a/das21a.pdf)

Learning State-Dependent Losses for Inverse Dynamics Learning

Published in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics model’s parameters, data efficiency is crucial. Given observed data, a key element to how an optimizer updates model parameters is the loss function. In this work, we propose to apply meta-learning to learn structured, state-dependent loss functions during a meta-training phase. We then replace standard losses with our learned losses during online adaptation tasks. We evaluate our proposed approach on inverse dynamics learning tasks, both in simulation and on real hardware data. In both settings, the structured and state-dependent learned losses improve online adaptation speed, when compared to standard, state-independent loss functions.

Cite with: K. Morse, N. Das, Y. Lin, A. S. Wang, A. Rai and F. Meier, "Model-Based Inverse Reinforcement Learning from Visual Demonstrations," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) https://ieeexplore.ieee.org/document/9341701

Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations.

Published in Arxiv, 2019

Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making. However, currently prevailing methods based on latent-variable models are limited to working with low resolution images only. In this work, we show that some of the issues with using high-dimensional observations arise from the discrepancy between the dimensionality of the latent and observable space, and propose solutions to overcome them.

Cite with: N. Das, M. Karl, P. Becker-Ehmck and P. van der Smagt, "Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations." 2019 arXiv preprint arXiv:1911.00756. https://arxiv.org/abs/1911.00756