Smart Human Identification System Based on PPG and ECG Signals in Wearable Devices

2021 ◽  
Author(s):  
Lucas Bastos ◽  
Bruno Cremonezi ◽  
Thais Tavares ◽  
Denis Rosario ◽  
Eduardo Cerqueira ◽  
...  
Keyword(s):  
Human Identification ◽  
Wearable Devices ◽  
Identification System ◽  
Ecg Signals

GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

2022 ◽  
Vol 18 (1) ◽  
pp. 1-24
Author(s):  
Yi Zhang ◽  
Yue Zheng ◽  
Guidong Zhang ◽  
Kun Qian ◽  
Chen Qian ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Human Identification ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Gait Patterns ◽  
Training Samples ◽  
Augmentation Techniques

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

scholarly journals Non-invasive Multi-modal Human Identification System Combining ECG, GSR, and Airflow Biosignals

2015 ◽  
Vol 35 (6) ◽  
pp. 735-748 ◽  
Author(s):  
Carmen Camara ◽  
Pedro Peris-Lopez ◽  
Juan E. Tapiador ◽  
Guillermo Suarez-Tangil
Keyword(s):  
Human Identification ◽  
Identification System ◽  
Non Invasive

Comparative Comparison and Future Challenges for Human Identification System

2019 ◽  
pp. 77-78
Author(s):  
Deven N. Trivedi ◽  
Nimit D. Shah ◽  
Ashish M. Kothari ◽  
Rohit M. Thanki
Keyword(s):  
Human Identification ◽  
Identification System ◽  
Future Challenges

A speed invariant human identification system using gait biometrics

2014 ◽  
Vol 4 (1/2) ◽  
pp. 3 ◽  
Author(s):  
Soumabha Bhowmick ◽  
Anup Nandy ◽  
Pavan Chakraborty ◽  
G.C. Nandi
Keyword(s):  
Human Identification ◽  
Identification System ◽  
Gait Biometrics

scholarly journals Assessing Drivers’ Physiological Responses Using Consumer Grade Devices

2019 ◽  
Vol 9 (24) ◽  
pp. 5353 ◽  
Author(s):  
Timotej Gruden ◽  
Kristina Stojmenova ◽  
Jaka Sodnik ◽  
Grega Jakus
Keyword(s):  
Standard Deviation ◽  
User Interfaces ◽  
User Study ◽  
Physiological Responses ◽  
Wearable Devices ◽  
Mean Value ◽  
Driver Assistance Systems ◽  
Ecg Signals ◽  
Driving Scenario ◽  
Different Levels

The ability to measure drivers’ physiological responses is important for understanding their state and behavior under different driving conditions. Such measurements can be used in the development of novel user interfaces, driver profiling, advanced driver assistance systems, etc. In this paper, we present a user study in which we performed an evaluation of two commercially available wearable devices for assessment of drivers’ physiological signals. Empatica’s E4 wristband measures blood volume pulse (BVP), inter-beat interval (IBI), galvanic skin response (GSR), temperature, and acceleration. Bittium’s Faros 360 is an electrocardiographic (ECG) device that can record up to 3-channel ECG signals. The aim of this study was to explore the use of such devices in a dynamic driving environment and their ability to differentiate between different levels of driving demand. Twenty-two participants (eight female, 14 male) aged between 18 and 45 years old participated in the study. The experiment compared three phases: Baseline (no driving), easy driving scenario, and demanding driving scenario. Mean and median heart rate variability (HRV), standard deviation of R–R intervals (SDNN), HRV variables for shorter time frames (standard deviation of the average R–R intervals over a shorter period—SDANN and mean value of the standard deviations calculated over a shorter period—SDNN index), HRV variables based on successive differences (root mean square of successive differences—RMSSD and percentage of successive differences, greater than 50 ms—pNN50), skin temperature, and GSR were observed in each phase. The results showed that motion artefacts due to driving affect the GSR recordings, which may limit the use of wrist-based wearable devices in a driving environment. In this case, due to the limitations of the photoplethysmography (PPG) sensor, E4 only showed differences between non-driving and driving phases but could not differentiate between different levels of driving demand. On the other hand, the results obtained from the ECG signals from Faros 360 showed statistically significant differences also between the two levels of driving demand.

scholarly journals Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4138
Author(s):  
Kuo-Kun Tseng ◽  
Jiao Lo ◽  
Chih-Cheng Chen ◽  
Shu-Yi Tu ◽  
Cheng-Fu Yang
Keyword(s):  
Matrix Method ◽  
Sparse Matrix ◽  
Human Identification ◽  
Recognition Algorithm ◽  
Blood Oxygen ◽  
Dimensional Approach ◽  
Identification Rate ◽  
Ecg Signals ◽  
Biometric Security ◽  
Biometric Information

Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm.

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