Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals

• URL: http://arxiv.org/abs/2411.16273v1
• Date: Mon, 25 Nov 2024 10:51:40 GMT
• Title: Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals
• Authors: Silas Ruhrberg Estévez, Josée Mallah, Dominika Kazieczko, Chenyu Tang, Luigi G. Occhipinti,
• Abstract summary: Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks. This paper presents a novel motion prediction framework that integrates three Inertial Measurement Units (IMUs) and eight surface Electromyography (sEMG) sensors. Our findings reveal that Convolutional Neural Networks (CNNs) slightly outperform Long Short-Term Memory (LSTM) networks on a dataset of five motion tasks.
• Score: 0.8388591755871735
• License:
• Abstract: Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks, particularly among the aging population. The efficacy of these devices relies on accurate real-time prediction of the user's intended movements through sensor-based inputs. This paper presents a novel motion prediction framework that integrates three Inertial Measurement Units (IMUs) and eight surface Electromyography (sEMG) sensors to capture both kinematic and muscular activity data. A comprehensive set of activities, representative of everyday movements in barrier-free environments, was recorded for the purpose. Our findings reveal that Convolutional Neural Networks (CNNs) slightly outperform Long Short-Term Memory (LSTM) networks on a dataset of five motion tasks, achieving classification accuracies of $96.5 \pm 0.8 \%$ and $87.5 \pm 2.9 \%$, respectively. Furthermore, we demonstrate the system's proficiency in transfer learning, enabling accurate motion classification for new subjects using just ten samples per class for finetuning. The robustness of the model is demonstrated by its resilience to sensor failures resulting in absent signals, maintaining reliable performance in real-world scenarios. These results underscore the potential of deep learning algorithms to enhance the functionality and safety of ankle exoskeletons, ultimately improving their usability in daily life.

Related papers

• MR-GDINO: Efficient Open-World Continual Object Detection [58.066277387205325]
  We propose an open-world continual object detection task requiring detectors to generalize to old, new, and unseen categories. We present a challenging yet practical OW-COD benchmark to assess detection abilities. To mitigate forgetting in unseen categories, we propose MR-GDINO, a strong, efficient and scalable baseline via memory and retrieval mechanisms.
  arXiv  Detail & Related papers  (2024-12-20T15:22:51Z)
• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning [0.0]
  Real-time lower limb movement resistance monitoring is critical for various applications in clinical and sports settings, such as rehabilitation and athletic training. We propose a novel Mobile Multi-Task Learning Network (MMTL-Net) that integrates MobileNetV3 for efficient feature extraction and employs multi-task learning to simultaneously predict resistance levels and recognize activities. The advantages of MMTL-Net include enhanced accuracy, reduced latency, and improved computational efficiency, making it highly suitable for real-time applications.
  arXiv  Detail & Related papers  (2024-10-13T18:19:48Z)
• sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics [5.524068837259551]
  Exoskeletons and rehabilitation systems have the potential to improve human strength and recovery by using adaptive human-machine interfaces. We introduce the Physics-informed Gated Recurrent Network (PiGRN), a novel model designed to predict multi-joint movement dynamics from sEMG data. PiGRN uses a Gated Recurrent Unit (GRU) to process time-series sEMG inputs, estimate multi-joint kinematics and external loads, and predict joint torque while incorporating physics-based constraints during training.
  arXiv  Detail & Related papers  (2024-08-29T15:09:04Z)
• An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals [2.632402517354116]
  We propose utilizing a feature-imitating network (FIN) for closed-form temporal feature learning over a 300ms signal window on Ninapro DB2. We observed that the LSTM-FIN network can achieve up to 99% R2 accuracy in feature reconstruction and 80% accuracy in hand movement recognition.
  arXiv  Detail & Related papers  (2024-05-23T21:45:15Z)
• Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition [5.669438716143601]
  Human Activity Recognition (HAR) has not fully capitalized on the proliferation of deep learning. We propose a novel approach to improve wearable sensor-based HAR by introducing a pose-to-sensor network model. Our contributions include the integration of simultaneous training, direct pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit dataset.
  arXiv  Detail & Related papers  (2024-04-25T10:13:18Z)
• A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
  The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans. We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
  arXiv  Detail & Related papers  (2022-11-16T20:00:38Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• 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)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)
• Human Activity Recognition from Wearable Sensor Data Using Self-Attention [2.9023633922848586]
  We present a self-attention based neural network model for activity recognition from body-worn sensor data. We performed experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-out-subject evaluation.
  arXiv  Detail & Related papers  (2020-03-17T14:16:57Z)

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.