Gender Recognition using in-built Inertial Sensors of Smartphone

Meena, Tanushree; Sarawadekar, Kishor

doi:10.1109/tencon50793.2020.9293797

Cited by 17 publications

(8 citation statements)

References 10 publications

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“…The gender classification task is usually conducted alongside human identification, and human re-identification tasks ( Lu et al, 2014 ) since a person’s gender can serve as a soft feature in identifying a person. For instance, gender classification is done first to prune a subset of subjects before human identification is performed ( Castro et al, 2017 ), ( Meena and Sarawadekar, 2020 ). Gender classification is also done first to improve the accuracy of a subsequent age estimation algorithm ( Zhang S. et al, 2019 ).…”

Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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“…The authors achieved an accuracy of 76.8% by processing with Support Vector Machines (SVM) and bagging algorithms. Meena and Saeawadekar [116] presented an approach for gender recognition based on the gait data extracted from smartphone sensors. The authors achieved an accuracy of 96.3% using the bagged tree classiier.…”

Section: Demographicsmentioning

confidence: 99%

“…In [5], the gender of the users was determined with an accuracy of 76.8% by processing with Support Vector Machines (SVM) and bagging algorithms their walking patterns acquired by a smartphone motion sensors. Meena and Saeawadekar [103] based their work on Ensemble Boosted Tree (EBT) and achieved an accuracy of 96.3%. The authors in [135] also focused on gender recognition from the data extracted by the accelerometer and gyroscope, obtaining an accuracy of 80%.…”

Section: A Demographicsmentioning

confidence: 99%

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

et al. 2022

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The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

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“…The proposed model produced an accuracy of 76.8% using support vector machines (SVMs), and bagging algorithms. Moreover, the authors of [ 122 ] described an approach for recognition of gender data using gait (walking) data, which were collected by the mobile sensors. The reported accuracy was 96.3%, and the process used the bagged tree classifier.…”

Section: Proper Management Of Sensitive Private Datamentioning

confidence: 99%

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

Bocu

Явич

2022

Sensors

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The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.

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Gender Recognition using in-built Inertial Sensors of Smartphone

Cited by 17 publications

References 10 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

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