Biology subjects

Kurbis, A. G.

Publications and source records attributed to Kurbis, A. G..

4 recordsLinked to original sources

bioRxiv2025

An EMG foundation model for neural decoding

Decoding algorithms can be used to predict motor behaviour from patterns of neural activity. However, most studies rely on subject-optimized models, limiting generalization and scalability to novel subjects and tasks. Building on recent advances in deep learning and large-scale data, here we developed an EMG foundation model for neural decoding. Our model was trained on more than 197 hours of neural recordings from 1,667 subjects. We used unsupervised learning to pretrain our encoder layers on unlabeled data, followed by supervised learning on our benchmark dataset. Additionally, we performed large-scale architecture searches to develop a custom encoder-decoder model composed of convolutional and transformer layers, optimized for both scalability and performance. Our foundation model consistently outperformed the previous state-of-the-art (i.e., subject-optimized models) across both in-distribution and out-of-distribution evaluations. For in-distribution evaluation, few-shot fine-tuning yielded an average F1 score of 0.697, compared to 0.638 for subject-optimized models. For out-of-distribution evaluation on clinical and demographically-shifted subjects, we achieved an average F1 score of 0.599, compared to 0.518 for the subject-optimized baselines. Taken together, our results highlight the value of foundation models for robust and generalizable neural decoding. By publicly releasing our neural network weights and training pipeline, we aim to support future research in computational neuroscience and neural-machine interfaces.

neuroscience↗

bioRxiv2023

Sequential image classification of human-robot walking environments using temporal neural networks

Robotic prosthetic legs and exoskeletons require real-time and accurate estimation of the walking environment for smooth transitions between different locomotion mode controllers. However, previous studies have mainly been limited to static image classification, therein ignoring the temporal dynamics of human-robot locomotion. Motivated by these limitations, here we developed several state-of-the-art temporal convolutional neural networks (CNNs) to compare the performances between static vs. sequential image classification of real-world walking environments (i.e., level-ground terrain, incline stairs, and transitions to and from stairs). Using our large-scale image dataset, we trained a number of encoder networks such as VGG, MobileNetV2, ViT, and MobileViT, each coupled with a temporal long short-term memory (LSTM) backbone. We also trained MoViNet, a new video classification model designed for mobile and embedded devices, to further compare the performances between 2D and 3D temporal deep learning models. Our 3D network outperformed all the hybrid 2D encoders with LSTM backbones and the 2D CNN baseline model in terms of classification accuracy, suggesting that network architecture can play an important role in performance. However, although our 3D neural network achieved the highest classification accuracy, it had disproportionally higher computational and memory storage requirements, which can be disadvantageous for real-time control of robotic leg prostheses and exoskeletons with limited onboard resources.

bioengineering↗

bioRxiv2023

Development and Mobile Deployment of a Stair Recognition System for Human-Robot Locomotion

Environment sensing and recognition can improve the safety and autonomy of human-robot locomotion, especially during transitions between environmental states such as walking to and from stairs. However, accurate and real-time perception on edge devices with limited computational resources is an open problem. Here we present the development and mobile deployment of StairNet - a vision-based automated stair recognition system powered by deep learning. Building on ExoNet - the largest open-source dataset of egocentric images of real-world walking environments - we designed a new dataset specifically for stair recognition with over 515,000 images. We then developed a lightweight and efficient convolutional neural network for image classification, which accurately predicted complex stair environments with 98.4% accuracy. We also studied different model compression and optimization methods and deployed our system on several mobile devices running a custom-designed iOS application with onboard accelerators using CPU, GPU, and/or NPU backend computing. Of the designs that we tested, our highest performing system showed negligible reductions in classification accuracy due to the model conversion for mobile deployment and achieved an inference time of 2.75 ms on an iPhone 11. The high speed and accuracy of the StairNet system on edge devices opens new opportunities for autonomous control and planning of robotic prosthetic legs, exoskeletons, and other assistive technologies for human locomotion.

bioengineering↗

bioRxiv2022

Stair Recognition for Robotic Exoskeleton Control using Computer Vision and Deep Learning

Computer vision can be used in robotic exoskeleton control to improve transitions between different locomotion modes through the prediction of future environmental states. Here we present the development of a large-scale automated stair recognition system powered by convolutional neural networks to recognize indoor and outdoor real-world stair environments. Building on the ExoNet database - the largest and most diverse open-source dataset of wearable camera images of walking environments - we designed a new computer vision dataset, called StairNet, specifically for stair recognition with over 515,000 images. We then developed and optimized an efficient deep learning model for automatic feature engineering and image classification. Our system was able to accurately predict complex stair environments with 98.4% classification accuracy. These promising results present an opportunity to increase the autonomy and safety of human-exoskeleton locomotion for real-world community mobility. Future work will explore the mobile deployment of our automated stair recognition system for onboard real-time inference.

bioengineering↗