Predicting Ground Reaction Force from Inertial Sensors

• URL: http://arxiv.org/abs/2311.02287v1
• Date: Sat, 4 Nov 2023 00:44:40 GMT
• Title: Predicting Ground Reaction Force from Inertial Sensors
• Authors: Bowen Song, Marco Paolieri, Harper E. Stewart, Leana Golubchik, Jill L. McNitt-Gray, Vishal Misra, Devavrat Shah
• Abstract summary: Ground reaction forces (GRF) is used to characterize the mechanical loading experienced by individuals in movements such as running. We consider lightweight approaches in contrast to state-of-the-art prediction using LSTM neural networks. We evaluate the accuracy of these techniques when using training data collected from different athletes.
• Score: 13.505402421169213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The study of ground reaction forces (GRF) is used to characterize the mechanical loading experienced by individuals in movements such as running, which is clinically applicable to identify athletes at risk for stress-related injuries. Our aim in this paper is to determine if data collected with inertial measurement units (IMUs), that can be worn by athletes during outdoor runs, can be used to predict GRF with sufficient accuracy to allow the analysis of its derived biomechanical variables (e.g., contact time and loading rate). In this paper, we consider lightweight approaches in contrast to state-of-the-art prediction using LSTM neural networks. Specifically, we compare use of LSTMs to k-Nearest Neighbors (KNN) regression as well as propose a novel solution, SVD Embedding Regression (SER), using linear regression between singular value decomposition embeddings of IMUs data (input) and GRF data (output). We evaluate the accuracy of these techniques when using training data collected from different athletes, from the same athlete, or both, and we explore the use of acceleration and angular velocity data from sensors at different locations (sacrum and shanks). Our results illustrate that simple machine learning methods such as SER and KNN can be similarly accurate or more accurate than LSTM neural networks, with much faster training times and hyperparameter optimization; in particular, SER and KNN are more accurate when personal training data are available, and KNN comes with benefit of providing provenance of prediction. Notably, the use of personal data reduces prediction errors of all methods for most biomechanical variables.

Related papers

• An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation [2.517043342442487]
  Deep generative learning uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data.
  arXiv  Detail & Related papers  (2024-10-24T18:15:48Z)
• Using deep neural networks to detect non-analytically defined expert event labels in canoe sprint force sensor signals [3.1446633690603356]
  This paper explores convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in terms of their ability to automatically predict paddle stroke events. In our results, an RNN based on bidirectional gated recurrent units (BGRUs) turned out to be the most suitable model for paddle stroke detection.
  arXiv  Detail & Related papers  (2024-07-11T10:59:11Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Asymmetric Momentum: A Rethinking of Gradient Descent [4.1001738811512345]
  We propose the simplest SGD enhanced method, Loss-Controlled Asymmetric Momentum(LCAM) By averaging the loss, we divide training process into different loss phases and using different momentum. We experimentally validate that weights have directional specificity, which are correlated with the specificity of the dataset.
  arXiv  Detail & Related papers  (2023-09-05T11:16:47Z)
• Machine Learning Force Fields with Data Cost Aware Training [94.78998399180519]
  Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels. We propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
  arXiv  Detail & Related papers  (2023-06-05T04:34:54Z)
• 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)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• 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)
• HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks [51.143054943431665]
  We propose Hypergradient Data Relevance Analysis, or HYDRA, which interprets predictions made by deep neural networks (DNNs) as effects of their training data. HYDRA assesses the contribution of training data toward test data points throughout the training trajectory. In addition, we quantitatively demonstrate that HYDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels.
  arXiv  Detail & Related papers  (2021-02-04T10:00:13Z)
• Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval [14.131127382785973]
  The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP) Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. We show how features extracted from the IASI data by a CNN trained to predict a physical variable can be used as inputs to another statistical method designed to predict a different physical variable at low altitude.
  arXiv  Detail & Related papers  (2020-12-09T09:28:42Z)
• RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr [60.07531696857743]
  Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. We propose RIFLE - a strategy that deepens backpropagation in transfer learning settings. RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning.
  arXiv  Detail & Related papers  (2020-07-07T11:27:43Z)

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.