Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle
Activity for Enhanced Myoelectric Control of Hand Prostheses
- URL: http://arxiv.org/abs/2104.14049v1
- Date: Thu, 29 Apr 2021 00:11:32 GMT
- Title: Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle
Activity for Enhanced Myoelectric Control of Hand Prostheses
- Authors: Alessandro Salatiello and Martin A. Giese
- Abstract summary: 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.
- Score: 78.120734120667
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: State-of-the-art motorized hand prostheses are endowed with actuators able to
provide independent and proportional control of as many as six degrees of
freedom (DOFs). The control signals are derived from residual electromyographic
(EMG) activity, recorded concurrently from relevant forearm muscles.
Nevertheless, the functional mapping between forearm EMG activity and hand
kinematics is only known with limited accuracy. Therefore, no robust method
exists for the reliable computation of control signals for the independent and
proportional actuation of more than two DOFs. A common approach to deal with
this limitation is to pre-program the prostheses for the execution of a
restricted number of behaviors (e.g., pinching, grasping, and wrist rotation)
that are activated by the detection of specific EMG activation patterns.
However, this approach severely limits the range of activities users can
perform with the prostheses during their daily living. In this work, we
introduce a novel method, based on a long short-term memory (LSTM) network, to
continuously map forearm EMG activity onto hand kinematics. Critically, unlike
previous work, which often focuses on simple and highly controlled motor tasks,
we tested our method on a dataset of activities of daily living (ADLs): the
KIN-MUS UJI dataset. To the best of our knowledge, ours is the first reported
work on the prediction of hand kinematics that uses this challenging dataset.
Remarkably, we show that our network is able to generalize to novel untrained
ADLs. 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.
Related papers
- 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) - Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health
Monitoring Systems [69.41229290253605]
Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently.
This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data.
We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
arXiv Detail & Related papers (2024-01-19T16:26:35Z) - MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis [1.3654846342364306]
We introduce MELEP, a measure designed to estimate the effectiveness of knowledge transfer from a pre-trained model to a downstream ECG diagnosis task.
Our experiments show that MELEP can predict the performance of pre-trained convolutional and recurrent deep neural networks, on small and imbalanced ECG data.
arXiv Detail & Related papers (2023-10-27T14:57:10Z) - Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement
Learning [53.00683059396803]
Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images.
We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy.
Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
arXiv Detail & Related papers (2023-10-06T10:40:46Z) - An Activity Recognition Framework for Continuous Monitoring of
Non-Steady-State Locomotion of Individuals with Parkinson's Disease [0.9137554315375922]
The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms.
Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8.
arXiv Detail & Related papers (2021-10-08T20:35:45Z) - Segmentation and Classification of EMG Time-Series During Reach-to-Grasp
Motion [10.388787606334745]
We propose a framework for classifying EMG signals generated from continuous grasp movements with variations on dynamic arm/hand postures.
The proposed framework was evaluated in real-time with the accuracy variation over time presented.
arXiv Detail & Related papers (2021-04-19T20:41:06Z) - Am I fit for this physical activity? Neural embedding of physical
conditioning from inertial sensors [0.0]
Inertial Measurement Unit (IMU) sensors are becoming increasingly ubiquitous in everyday devices such as smartphones, fitness watches, etc.
We propose a neural architecture for this task composed of convolutional and LSTM layers.
We evaluate the proposed model, dubbed PCE-LSTM, when predicting the heart rate of 23 subjects performing a variety of physical activities from IMU-sensor data available in public datasets (PAMAP2, PPG-DaLiA). PCE-LSTM yields over 10% lower mean absolute error.
arXiv Detail & Related papers (2021-03-22T18:00:27Z) - Self-supervised transfer learning of physiological representations from
free-living wearable data [12.863826659440026]
We present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels.
We evaluate our model in the largest free-living combined-sensing dataset (comprising >280k hours of wrist accelerometer & wearable ECG data)
arXiv Detail & Related papers (2020-11-18T23:21:34Z) - Statistical control for spatio-temporal MEG/EEG source imaging with
desparsified multi-task Lasso [102.84915019938413]
Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques.
The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge.
We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
arXiv Detail & Related papers (2020-09-29T21:17:16Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z)
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.