BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity

• URL: http://arxiv.org/abs/2210.09843v1
• Date: Tue, 18 Oct 2022 13:26:49 GMT
• Title: BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity
• Authors: Emanuele Maiorana, Chiara Romano, Emiliano Schena, and Carlo Massaroni
• Abstract summary: We propose a BIOmetric recognition approach using Wearable Inertial Sensors detecting Heart activity (BIOWISH) In this paper we investigate the feasibility of exploiting mechanical measurements obtained through seismocardiography and gyrocardiography to recognize a person.
• Score: 6.509758931804478
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Wearable devices are increasingly used, thanks to the wide set of applications that can be deployed exploiting their ability to monitor physical activity and health-related parameters. Their usage has been recently proposed to perform biometric recognition, leveraging on the uniqueness of the recorded traits to generate discriminative identifiers. Most of the studies conducted on this topic have considered signals derived from cardiac activity, detecting it mainly using electrical measurements thorugh electrocardiography, or optical recordings employing photoplethysmography. In this paper we instead propose a BIOmetric recognition approach using Wearable Inertial Sensors detecting Heart activity (BIOWISH). In more detail, we investigate the feasibility of exploiting mechanical measurements obtained through seismocardiography and gyrocardiography to recognize a person. Several feature extractors and classifiers, including deep learning techniques relying on transfer learning and siamese training, are employed to derive distinctive characteristics from the considered signals, and differentiate between legitimate and impostor subjects. An multi-session database, comprising acquisitions taken from subjects performing different activities, is employed to perform experimental tests simulating a verification system. The obtained results testify that identifiers derived from measurements of chest vibrations, collected by wearable inertial sensors, could be employed to guarantee high recognition performance, even when considering short-time recordings.

Related papers

• Multi-objective Feature Selection in Remote Health Monitoring Applications [2.219956413333153]
  In some scenarios, an RF signal analysis framework may prioritize the performance of one task over that of others. We employ a multi-objective optimization approach inspired by biological principles to select discriminative features that enhance the accuracy of breathing patterns recognition. We present a contrariwise result to maximize user identification accuracy and minimize the system's capacity for breathing activity recognition.
  arXiv  Detail & Related papers  (2024-01-10T20:27:43Z)
• Multi-Channel Time-Series Person and Soft-Biometric Identification [65.83256210066787]
  This work investigates person and soft-biometrics identification from recordings of humans performing different activities using deep architectures. We evaluate the method on four datasets of multi-channel time-series human activity recognition (HAR) Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.
  arXiv  Detail & Related papers  (2023-04-04T07:24:51Z)
• Unleashing the Power of Electrocardiograms: A novel approach for Patient Identification in Healthcare Systems with ECG Signals [0.696125353550498]
  This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network is used to classify users based on images extracted from ECG signals.
  arXiv  Detail & Related papers  (2023-02-13T17:14:55Z)
• 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)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Respiratory Sound Classification Using Long-Short Term Memory [62.997667081978825]
  This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. An examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented.
  arXiv  Detail & Related papers  (2020-08-06T23:11:57Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• Wearable Respiration Monitoring: Interpretable Inference with Context and Sensor Biomarkers [5.065947993017157]
  Breathing rate (BR), minute ventilation (VE), and other respiratory parameters are essential for real-time patient monitoring in many acute health conditions, such as asthma. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals.
  arXiv  Detail & Related papers  (2020-07-02T22:12:49Z)
• 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)
• Heart Sound Segmentation using Bidirectional LSTMs with Attention [37.62160903348547]
  We propose a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states. We exploit recent advancements in attention based learning to segment the PCG signal. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings.
  arXiv  Detail & Related papers  (2020-04-02T02:09:11Z)

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.