Machine Learning Assisted Postural Movement Recognition using Photoplethysmography(PPG)

• URL: http://arxiv.org/abs/2411.11862v1
• Date: Sat, 02 Nov 2024 18:56:41 GMT
• Title: Machine Learning Assisted Postural Movement Recognition using Photoplethysmography(PPG)
• Authors: Robbie Maccay, Roshan Weerasekera,
• Abstract summary: There is an urgent need for the development of fall detection and fall prevention technologies. This work presents for the first time the use of machine learning techniques to recognize postural movements. Various machine learning approaches were used for classification, and the Artificial Neural Network (ANN) was found to be the best.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing percentage of elderly people and care home admissions, there is an urgent need for the development of fall detection and fall prevention technologies. This work presents, for the first time, the use of machine learning techniques to recognize postural movements exclusively from Photoplethysmography (PPG) data. To achieve this goal, a device was developed for reading the PPG signal, segmenting the PPG signals into individual pulses, extracting pulse morphology and homeostatic characteristic features, and evaluating different ML algorithms. Investigations into different postural movements (stationary, sitting to standing, and lying to standing) were performed by 11 participants. The results of these investigations provided insight into the differences in homeostasis after the movements in the PPG signal. Various machine learning approaches were used for classification, and the Artificial Neural Network (ANN) was found to be the best classifier, with a testing accuracy of 85.2\% and an F1 score of 78\% from experimental results.

Related papers

• Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning [0.0]
  This study addresses the classification of heartbeats from ECG signals through two distinct approaches.<n>Traditional machine learning utilizing hand-crafted features and deep learning via transformed images of ECG beats.<n>Models such as SVM and AdaBoost yielded significantly lower scores, indicating limited suitability for this task.
  arXiv  Detail & Related papers  (2025-06-02T05:16:16Z)
• Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models [45.94962431110573]
  Camera-based monitoring of vital signs, also known as imaging photoplethysmography (i), has seen applications in driver-monitoring, affective computing, and more. We introduce methods that combine signal processing and deep learning methods in an inverse problem.
  arXiv  Detail & Related papers  (2025-03-21T16:11:21Z)
• Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity [43.108040967674185]
  Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD) This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD.
  arXiv  Detail & Related papers  (2025-02-18T12:01:55Z)
• Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity [4.674309739521861]
  This study proposes a practical adversarial learning approach for opto-physiological monitoring by using a generative adversarial network with an attention mechanism (AM-GAN) The AM-GAN learns an MA-resistant mapping from raw and noisy signals to clear PPG signals in an adversarial manner. The study demonstrates the robustness and resilience of AM-GAN, particularly during low-to-high-intensity physical activities.
  arXiv  Detail & Related papers  (2025-02-13T13:08:11Z)
• A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp [11.354158680070652]
  We propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. We also propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc.
  arXiv  Detail & Related papers  (2024-12-10T01:45:14Z)
• Interpretable Pre-Trained Transformers for Heart Time-Series Data [15.377534937558744]
  We create two pre-trained general purpose cardiac models, PPG-PT and ECG-PT. We highlight that individual attention heads respond to specific physiologically relevent features, such as the dicrotic notch in PPG and the P-wave in ECG. These pre-trained models are straightforward to fine-tune for tasks such as classification of atrial fibrillation (AF), and beat detection in photoplethysmography.
  arXiv  Detail & Related papers  (2024-07-30T12:22:03Z)
• Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types [0.16385815610837165]
  This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications.
  arXiv  Detail & Related papers  (2024-01-31T23:13:38Z)
• Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging [5.604613879541013]
  This study aims to bridge the gap by integrating r-modal and thermal imaging to enhance prediction performance. Using r- alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by SVM. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF.
  arXiv  Detail & Related papers  (2024-01-17T11:38:21Z)
• Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms [0.0]
  This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores.
  arXiv  Detail & Related papers  (2023-11-09T15:21:10Z)
• PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics [48.683457383784145]
  Three approaches to presentation attack detection based on r: (i) the physiological domain, (ii) the Deepfakes domain, and (iii) a new Presentation Attack domain. Results show a 21.70% decrease in average classification error rate (ACER) when the presentation attack domain is compared to the physiological and Deepfakes domains. Experiments highlight the efficiency of transfer learning in r-based models and perform well in presentation attack detection in instruments that do not allow copying of this physiological feature.
  arXiv  Detail & Related papers  (2023-10-03T15:24:15Z)
• Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography [53.38589633687604]
  We propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it.
  arXiv  Detail & Related papers  (2023-04-26T14:11:04Z)
• Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques [0.0]
  Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. EEG indicates abnormalities in PD patients. One major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication.
  arXiv  Detail & Related papers  (2023-03-29T06:03:05Z)
• Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN [40.50404819220093]
  A novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of Alzheimer's disease (AD) MP-GAN achieves superior performance compared with the existing methods.
  arXiv  Detail & Related papers  (2021-11-25T03:24:52Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• 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.