Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning

• URL: http://arxiv.org/abs/2412.16271v1
• Date: Fri, 20 Dec 2024 15:37:10 GMT
• Title: Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning
• Authors: Dario Di Domenico, Nicolò Boccardo, Andrea Marinelli, Michele Canepa, Emanuele Gruppioni, Matteo Laffranchi, Raffaello Camoriano,
• Abstract summary: We introduce a novel myoelectric prosthetic system integrating a high density-sEMG (HD-sEMG) setup and incremental learning methods. First, we present a newly designed, compact HD-sEMG interface equipped with 64 dry electrodes positioned over the forearm. Then, we introduce an efficient incremental learning system enabling model adaptation on a stream of data.
• Score: 1.5383266953224775
• License:
• Abstract: Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data. While promising, they often suffer from distribution shift during long-term usage, requiring costly model re-training. Moreover, most prosthetic sEMG sensors have low spatial density, which limits accuracy and the number of controllable motions. In this work, we address both challenges by introducing a novel myoelectric prosthetic system integrating a high density-sEMG (HD-sEMG) setup and incremental learning methods to accurately control 7 motions of the Hannes prosthesis. First, we present a newly designed, compact HD-sEMG interface equipped with 64 dry electrodes positioned over the forearm. Then, we introduce an efficient incremental learning system enabling model adaptation on a stream of data. We thoroughly analyze multiple learning algorithms across 7 subjects, including one with limb absence, and 6 sessions held in different days covering an extended period of several months. The size and time span of the collected data represent a relevant contribution for studying long-term myocontrol performance. Therefore, we release the DELTA dataset together with our experimental code.

Related papers

• CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention [53.539020807256904]
  We introduce a Compact for Representations of Brain Oscillations using alternating attention (CEReBrO) Our tokenization scheme represents EEG signals at a per-channel patch. We propose an alternating attention mechanism that jointly models intra-channel temporal dynamics and inter-channel spatial correlations, achieving 2x speed improvement with 6x less memory required compared to standard self-attention.
  arXiv  Detail & Related papers  (2025-01-18T21:44:38Z)
• Online Adaptation for Myographic Control of Natural Dexterous Hand and Finger Movements [0.6741087029030101]
  This work redefines the state-of-the-art in myographic decoding in terms of the reliability, responsiveness, and movement complexity available from prosthesis control systems.
  arXiv  Detail & Related papers  (2024-12-23T21:20:32Z)
• Comparison of gait phase detection using traditional machine learning and deep learning techniques [3.11526333124308]
  This study proposes a few Machine Learning (ML) based models on lower-limb EMG data for human walking. The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model.
  arXiv  Detail & Related papers  (2024-03-07T10:05:09Z)
• A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning [11.170031300110315]
  Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computers. These interfaces have a range of applications, including the control of extended reality, agile prosthetics, and exoskeletons. These interfaces have a range of applications, including the control of extended reality, agile prosthetics, and exoskeletons.
  arXiv  Detail & Related papers  (2023-09-23T05:32:33Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization [66.27399823422665]
  Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. We propose an efficient Device-cloUd collaborative parametErs generaTion framework DUET.
  arXiv  Detail & Related papers  (2022-09-12T13:26:26Z)
• An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic [91.6474995587871]
  High performance control of diverse effectors for complex tasks must be robust over time and of high decoding performance without continuous recalibration of the decoders. We developed an adaptive online tensor-based decoder: the Recursive Exponentially Weighted Markov-Switching multi- Linear Model (REW-MSLM) We demonstrated over a period of 6 months the stability of the 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar using REW-MSLM without recalibration of the decoder.
  arXiv  Detail & Related papers  (2022-01-25T16:51:29Z)
• Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification [0.2676349883103404]
  Machine learning based sEMG decoders are either trained on subject-specific data, or at least recalibrated for each user, individually. Due to the limited amount of availability of sEMG data, the deep learning models are prone to overfitting. Recently, transfer learning for domain adaptation improved generalization quality with reduced training time.
  arXiv  Detail & Related papers  (2021-12-30T11:35:53Z)
• Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
  We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics. Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
  arXiv  Detail & Related papers  (2021-04-29T00:11:32Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.