Activity-Free User Identification Using Wearables Based on Vision Techniques
In order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/19/7368 |
| _version_ | 1827652995721986048 |
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| author | Alejandro Sanchez Guinea Simon Heinrich Max Mühlhäuser |
| author_facet | Alejandro Sanchez Guinea Simon Heinrich Max Mühlhäuser |
| author_sort | Alejandro Sanchez Guinea |
| collection | DOAJ |
| description | In order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the smart services are to be provided. In this paper, we propose a new approach capable of performing activity-free identification of users based on hand and arm motion patterns obtained from an wrist-worn inertial measurement unit (IMU). Our approach is not constrained to particular types of movements, gestures, or activities, thus, allowing users to perform freely and unconstrained their daily routine while the user identification takes place. We evaluate our approach based on IMU data collected from 23 people performing their daily routines unconstrained. Our results indicate that our approach is able to perform activity-free user identification with an accuracy of 0.9485 for 23 users without requiring any direct input or specific action from users. Furthermore, our evaluation provides evidence regarding the robustness of our approach in various different configurations. |
| first_indexed | 2024-03-09T21:10:53Z |
| format | Article |
| id | doaj.art-3008e683cb45477d896d98f998c8d659 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:53Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3008e683cb45477d896d98f998c8d6592023-11-23T21:48:04ZengMDPI AGSensors1424-82202022-09-012219736810.3390/s22197368Activity-Free User Identification Using Wearables Based on Vision TechniquesAlejandro Sanchez Guinea0Simon Heinrich1Max Mühlhäuser2Department of Computer Science, Technical University of Darmstadt, 64289 Darmstadt, GermanyDepartment of Computer Science, Technical University of Darmstadt, 64289 Darmstadt, GermanyDepartment of Computer Science, Technical University of Darmstadt, 64289 Darmstadt, GermanyIn order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the smart services are to be provided. In this paper, we propose a new approach capable of performing activity-free identification of users based on hand and arm motion patterns obtained from an wrist-worn inertial measurement unit (IMU). Our approach is not constrained to particular types of movements, gestures, or activities, thus, allowing users to perform freely and unconstrained their daily routine while the user identification takes place. We evaluate our approach based on IMU data collected from 23 people performing their daily routines unconstrained. Our results indicate that our approach is able to perform activity-free user identification with an accuracy of 0.9485 for 23 users without requiring any direct input or specific action from users. Furthermore, our evaluation provides evidence regarding the robustness of our approach in various different configurations.https://www.mdpi.com/1424-8220/22/19/7368user identificationimage representationCNNsIMUinertial sensorswearable sensors |
| spellingShingle | Alejandro Sanchez Guinea Simon Heinrich Max Mühlhäuser Activity-Free User Identification Using Wearables Based on Vision Techniques Sensors user identification image representation CNNs IMU inertial sensors wearable sensors |
| title | Activity-Free User Identification Using Wearables Based on Vision Techniques |
| title_full | Activity-Free User Identification Using Wearables Based on Vision Techniques |
| title_fullStr | Activity-Free User Identification Using Wearables Based on Vision Techniques |
| title_full_unstemmed | Activity-Free User Identification Using Wearables Based on Vision Techniques |
| title_short | Activity-Free User Identification Using Wearables Based on Vision Techniques |
| title_sort | activity free user identification using wearables based on vision techniques |
| topic | user identification image representation CNNs IMU inertial sensors wearable sensors |
| url | https://www.mdpi.com/1424-8220/22/19/7368 |
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