scholarly journals Incoming Call Implicit User Authentication - User Authentication via Hand Movement Pattern

2018 ◽  
Author(s):  
Aleksandr Eremin ◽  
Konstantin Kogos
Keyword(s):  
User Authentication ◽  
Hand Movement ◽  
Movement Pattern ◽  
Incoming Call

Hand Movement-Controlled Image Viewer in an Operating Room by Using Hand Movement Pattern Code

2021 ◽  
Author(s):  
Annop Gobhiran ◽  
Daorutchada Wongjunda ◽  
Kraiyot Kiatsoontorn ◽  
Theekapun Charoenpong
Keyword(s):  
Operating Room ◽  
Hand Movement ◽  
Movement Pattern ◽  
Image Viewer

Hand Movement Training in Braille Reading

1981 ◽  
Vol 75 (8) ◽  
pp. 327-331 ◽  
Author(s):  
Diane P. Wormsley
Keyword(s):  
Character Recognition ◽  
Hand Movement ◽  
Movement Pattern ◽  
Hand Movements ◽  
Dramatic Decrease ◽  
Movement Training ◽  
Perceptual Ability ◽  
Tracking Movements

Twenty-one children ages 6 though 13 were taught to use their hands independently when reading braille to determine how this pattern of hand movements affected reading variables, excluding character recognition. Although all the children learned this pattern of hand movements during the 20 days scheduled for training, only nine children exhibited a dramatic decrease in inefficient tracking movements such as pauses and scrubbing motions. Because these children were younger and more intelligent than the others, read braille more slowly, and had received less training in braille at school, the results strongly suggested that skill in tracking and use of an efficient hand movement pattern is closely tied to perceptual ability. Thus when teaching children to read braille, the motor aspects of the task should be combined with the perceptual aspects from the beginning.

scholarly journals FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration

2020 ◽  
Vol 7 ◽  
Author(s):  
Mohammad Anvaripour ◽  
Mahta Khoshnam ◽  
Carlo Menon ◽  
Mehrdad Saif
Keyword(s):  
Industrial Robot ◽  
Hand Movement ◽  
Movement Pattern ◽  
Machine Learning Algorithms ◽  
Hand Motion ◽  
Course Of Action ◽  
Motor Intention ◽  
Interaction Scheme ◽  
Force Myography ◽  
Human Robot Collaboration

Research on human-robot interactions has been driven by the increasing employment of robotic manipulators in manufacturing and production. Toward developing more effective human-robot collaboration during shared tasks, this paper proposes an interaction scheme by employing machine learning algorithms to interpret biosignals acquired from the human user and accordingly planning the robot reaction. More specifically, a force myography (FMG) band was wrapped around the user's forearm and was used to collect information about muscle contractions during a set of collaborative tasks between the user and an industrial robot. A recurrent neural network model was trained to estimate the user's hand movement pattern based on the collected FMG data to determine whether the performed motion was random or intended as part of the predefined collaborative tasks. Experimental evaluation during two practical collaboration scenarios demonstrated that the trained model could successfully estimate the category of hand motion, i.e., intended or random, such that the robot either assisted with performing the task or changed its course of action to avoid collision. Furthermore, proximity sensors were mounted on the robotic arm to investigate if monitoring the distance between the user and the robot had an effect on the outcome of the collaborative effort. While further investigation is required to rigorously establish the safety of the human worker, this study demonstrates the potential of FMG-based wearable technologies to enhance human-robot collaboration in industrial settings.

Gestural Interaction with Mobile Devices Based on Magnetic Field

2014 ◽  
pp. 203-222
Author(s):  
Kamer Ali Yüksel
Keyword(s):  
Magnetic Field ◽  
User Authentication ◽  
Handwriting Recognition ◽  
Hand Movement ◽  
Data Entry ◽  
Field Pattern ◽  
User Interactions ◽  
Device Interaction ◽  
3D User Interface ◽  
The Magnetic Field

The theory of around device interaction (ADI) has recently gained a lot of attention in the field of human computer interaction (HCI). As an alternative to the classic data entry methods, such as keypads and touch screens, ADI founds a 3D user interface that extends to the peripheral area of a device. In this chapter, the authors introduce a revolutionary interaction framework that is based on the idea of ADI. The proposed method constitutes a touchless data entry system that is based on the interaction between the magnetic fields around a device and a properly shaped magnet. The magnetic field that surrounds the device is generated by a magnetic sensor (compass) that is embedded in the new generation of mobile phones such as Apple’s iPhone 3GS and 4G, and Google’s Nexus one. The user movements of the properly shaped magnet in front of the device, then, deforms the sensor’s original magnetic field pattern whereby we can constitute a new means of communication between the user and the device. Thus, the magnetic field encompassing the device plays the role of a communication channel and encodes the hand-movement patterns of the user into temporal changes of the sensor’s magnetic field. In the back-end of the communication, an engine samples the momentary status of the field during a trial and recognizes the user’s pattern by matching it against some pre-recorded templates. The proposed method has been tested in a variety of applications such as handwriting recognition, user authentication, gesture recognition, and some entertainment applications. The experimental results show that the proposed interface not only elevates the convenience of user-device interactions, but also shows very promising accuracies in a wide range of applications requiring user interactions.

scholarly journals A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic

2021 ◽  
Vol 12 (1) ◽  
pp. 41-59
Author(s):  
Rafal Doroz ◽  
Krzysztof Wrobel ◽  
Piotr Porwik ◽  
Tomasz Orczyk
Keyword(s):  
Feature Selection ◽  
Spatial Analysis ◽  
Input Data ◽  
User Authentication ◽  
Hand Movement ◽  
Experimental Results ◽  
Recording Device ◽  
Distinctive Features ◽  
Metric Analysis ◽  
Feature Importance

Abstract The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of bio-metric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data.

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