Reliability and Generalization of Gait Biometrics Using 3D Inertial Sensor Data and 3D Optical System Trajectories

2021 ◽  
Author(s):  
Geise Santos ◽  
Tiago Tavares ◽  
Anderson Rocha
Keyword(s):  
Inertial Sensor ◽  
Gait Recognition ◽  
Sensor Data ◽  
Source Characteristics ◽  
Gait Biometrics ◽  
Subject Identification ◽  
Data Source ◽  
Automatic Gait Recognition ◽  
The Impact ◽  
Periodic Components

Abstract Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, fueling the automatic gait recognition field. Whereas, gait recognition works usually focus on improving end-to-end performance measures, and this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. The assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject identification scenario. In this scenario, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and differences between subjects in a verification setup. The explorations concluded that periodic components occurring in frequencies between 6Hz and 10Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.

Multimodal Gait Recognition With Inertial Sensor Data and Video Using Evolutionary Algorithm

2019 ◽  
Vol 27 (5) ◽  
pp. 956-965 ◽  
Author(s):  
Pradeep Kumar ◽  
Subham Mukherjee ◽  
Rajkumar Saini ◽  
Pallavi Kaushik ◽  
Partha Pratim Roy ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Inertial Sensor ◽  
Gait Recognition ◽  
Sensor Data

mTeeth

2021 ◽  
Vol 5 (2) ◽  
pp. 1-25
Author(s):  
Sayma Akther ◽  
Nazir Saleheen ◽  
Mithun Saha ◽  
Vivek Shetty ◽  
Santosh Kumar
Keyword(s):  
Machine Learning ◽  
Natural Environment ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Living Environment ◽  
Sensor Data ◽  
Oral Diseases ◽  
Free Living ◽  
Manual Toothbrush ◽  
The Impact

Ensuring that all the teeth surfaces are adequately covered during daily brushing can reduce the risk of several oral diseases. In this paper, we propose the mTeeth model to detect teeth surfaces being brushed with a manual toothbrush in the natural free-living environment using wrist-worn inertial sensors. To unambiguously label sensor data corresponding to different surfaces and capture all transitions that last only milliseconds, we present a lightweight method to detect the micro-event of brushing strokes that cleanly demarcates transitions among brushing surfaces. Using features extracted from brushing strokes, we propose a Bayesian Ensemble method that leverages the natural hierarchy among teeth surfaces and patterns of transition among them. For training and testing, we enrich a publicly-available wrist-worn inertial sensor dataset collected from the natural environment with time-synchronized precise labels of brushing surface timings and moments of transition. We annotate 10,230 instances of brushing on different surfaces from 114 episodes and evaluate the impact of wide between-person and within-person between-episode variability on machine learning model's performance for brushing surface detection.

Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population (Preprint)

2020 ◽  
Author(s):  
Josimar E. Chire Saire
Keyword(s):  
Text Mining ◽  
Electronic Communication ◽  
Spanish Speakers ◽  
South American ◽  
South American Population ◽  
The People ◽  
Data Source ◽  
The Impact ◽  
General Concern ◽  
Social Sensors

BACKGROUND Infoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and countries took actions to decrease the impact. South American countries have a different context about Economy, Health and Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different perspectives i.e. Economics, Social. OBJECTIVE Analyze the behaviour of South American Capitals in front of covid19 pandemics and show the helpfulness of Text Mining Approach for Infoveillance tasks. METHODS Text Mining process RESULTS - Argentina and Venezuela capitals are the biggest number of post during this period, opposite with Bolivia, Ecuador and Uruguay. - Most relevant users are related to mass media like radio, television or newspapers. - There is a general concern about covid19 but every country talks about different areas: Economics, Health, Environmental Impact. CONCLUSIONS Infoveillance based on Social Sensors with data coming from Twitter can help to understand the trends on the population of the capitals. Besides, it is necessary to filter the posts for processing the text and get insights about frequency, top users, most important terms. This data is useful to analyse the population from different approaches. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1101/2020.04.06.20055749

scholarly journals WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 539
Author(s):  
Saleh Seyedzadeh ◽  
Andrew Agapiou ◽  
Majid Moghaddasi ◽  
Milan Dado ◽  
Ivan Glesk
Keyword(s):  
Optical Networks ◽  
Video Surveillance ◽  
Multiple Access ◽  
Optical Sensing ◽  
Multiple Access Interference ◽  
Service Differentiation ◽  
Sensor Data ◽  
Variable Weight ◽  
Code Division Multiple ◽  
The Impact

The growing demand for extensive and reliable structural health monitoring resulted in the development of advanced optical sensing systems (OSS) that in conjunction with wireless optical networks (WON) are capable of extending the reach of optical sensing to places where fibre provision is not feasible. To support this effort, the paper proposes a new type of a variable weight code called multiweight zero cross-correlation (MW-ZCC) code for its application in wireless optical networks based optical code division multiple access (WON-OCDMA). The code provides improved quality of service (QoS) and better support for simultaneous transmission of video surveillance, comms and sensor data by reducing the impact of multiple access interference (MAI). The MW-ZCC code’s power of two code-weight properties provide enhanced support for the needed service differentiation provisioning. The performance of this novel code has been studied by simulations. This investigation revealed that for a minimum allowable bit error rate of 10−3, 10−9 and 10−12 when supporting triple-play services (sensing, datacomms and video surveillance, respectively), the proposed WON-OCDMA using MW-ZCC codes could support up to 32 simultaneous services over transmission distances up to 32 km in the presence of moderate atmospheric turbulence.

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

scholarly journals Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

2021 ◽  
Vol 185 ◽  
pp. 282-291
Author(s):  
Nizam U. Ahamed ◽  
Kellen T. Krajewski ◽  
Camille C. Johnson ◽  
Adam J. Sterczala ◽  
Julie P. Greeves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Sensor ◽  
Load Carriage ◽  
Sensor Data ◽  
Gait Patterns

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

scholarly journals Do Temporary Dropouts Improve the Composition of Panel Data? An Analysis of “Gap Interviews” in the German Family Panel pairfam

2017 ◽  
Vol 49 (1) ◽  
pp. 193-215 ◽  
Author(s):  
Bettina Müller ◽  
Laura Castiglioni
Keyword(s):  
Social Research ◽  
Life Changes ◽  
Cross Sectional ◽  
German Family ◽  
Sample Composition ◽  
Adequate Representation ◽  
Data Source ◽  
Panel Studies ◽  
Survey Estimates ◽  
The Impact

In the context of cross-sectional surveys, the scope of research on the impact of response enhancing strategies on sample composition and nonresponse bias is vast. This topic has rarely been addressed for panel studies, however, although these are becoming an increasingly important data source in social research. In this article, we evaluate the impact of reissuing wave nonrespondents on sample composition and survey estimates in the German Family Panel pairfam. In light of concerns about an adequate representation of life changes in panel studies, we focus on whether temporary dropouts improve sample composition in this respect: Using retrospective information from these cases provided at reentry, we approximate the impact of “lost” reports of life changes due to attrition. Our analysis reveals that the inclusion of temporary dropouts does increase sample variability regarding life changes. However, example analyses indicate that substantive conclusions would not be compromised if temporary dropouts were excluded.

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

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