Person Identification through Harvesting Kinetic Energy
Energy-based devices made this possible to recognize the need for batteryless wearables. The batteryless wearable notion created an opportunity for continuous and ubiquitous human identification. Traditionally, securing device passwords, PINs, and fingerprints based on the accelerometer to sample th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Sindh
2021-07-01
|
| Series: | University of Sindh Journal of Information and Communication Technology |
| Subjects: | |
| Online Access: | https://sujo.usindh.edu.pk/index.php/USJICT/article/view/2952 |
| _version_ | 1827927608587714560 |
|---|---|
| author | Naadiya Mirbahar Mansoor Ahmed Khuhro Saajid Hussain Sheeba Memon Shafique Ahmed Awan |
| author_facet | Naadiya Mirbahar Mansoor Ahmed Khuhro Saajid Hussain Sheeba Memon Shafique Ahmed Awan |
| author_sort | Naadiya Mirbahar |
| collection | DOAJ |
| description | Energy-based devices made this possible to recognize the need for batteryless wearables. The batteryless wearable notion created an opportunity for continuous and ubiquitous human identification. Traditionally, securing device passwords, PINs, and fingerprints based on the accelerometer to sample the acceleration traces for identification, but the accelerometer's energy consumption has been a critical issue for the existing ubiquitous self-enabled devices. In this paper, a novel method harvesting kinetic energy for identification improves energy efficiency and reduces energy demand to provide the identification. The idea of utilizing harvested power for personal identification is actuated by the phenomena that people walk distinctly and generate different kinetic energy levels leaving their signs with a harvested power signal. The statistical evaluation of experimental results proves that power traces contain sufficient information for person identification. The experimental analysis is conducted on 85 persons walking data for kinetic power signal-based person identification. We select five different classifiers that provide exemplary performance for identifying an individual for their generated power traces, namely NaiveBayes, OneR, and Meta Bagging. The experimental outcomes demonstrate the classifier's accuracy of 90%, 97%, and 98%, respectively. The Dataset used is publicly available for the gait acceleration series. |
| first_indexed | 2024-03-13T05:54:38Z |
| format | Article |
| id | doaj.art-30166ced709f4b2b8b13078ff3c00ca4 |
| institution | Directory Open Access Journal |
| issn | 2521-5582 2523-1235 |
| language | English |
| last_indexed | 2024-03-13T05:54:38Z |
| publishDate | 2021-07-01 |
| publisher | University of Sindh |
| record_format | Article |
| series | University of Sindh Journal of Information and Communication Technology |
| spelling | doaj.art-30166ced709f4b2b8b13078ff3c00ca42023-06-13T06:16:58ZengUniversity of SindhUniversity of Sindh Journal of Information and Communication Technology2521-55822523-12352021-07-0152951002952Person Identification through Harvesting Kinetic EnergyNaadiya Mirbahar0Mansoor Ahmed Khuhro1Saajid Hussain2Sheeba Memon3Shafique Ahmed Awan4Sindh Madressatul Islam UniversityDepartment of Computer science, Sindh Madressatul Islam University, KarachiShaheed Benazir Bhutto University Shaheed Benazirabad, Naushehro Feroz campusDepartment of Information Technology, Govern 1ment College University HyderabadFaculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi Energy-based devices made this possible to recognize the need for batteryless wearables. The batteryless wearable notion created an opportunity for continuous and ubiquitous human identification. Traditionally, securing device passwords, PINs, and fingerprints based on the accelerometer to sample the acceleration traces for identification, but the accelerometer's energy consumption has been a critical issue for the existing ubiquitous self-enabled devices. In this paper, a novel method harvesting kinetic energy for identification improves energy efficiency and reduces energy demand to provide the identification. The idea of utilizing harvested power for personal identification is actuated by the phenomena that people walk distinctly and generate different kinetic energy levels leaving their signs with a harvested power signal. The statistical evaluation of experimental results proves that power traces contain sufficient information for person identification. The experimental analysis is conducted on 85 persons walking data for kinetic power signal-based person identification. We select five different classifiers that provide exemplary performance for identifying an individual for their generated power traces, namely NaiveBayes, OneR, and Meta Bagging. The experimental outcomes demonstrate the classifier's accuracy of 90%, 97%, and 98%, respectively. The Dataset used is publicly available for the gait acceleration series.https://sujo.usindh.edu.pk/index.php/USJICT/article/view/2952person identificationenergy efficiencykinetic energy harvestingbatteryless wearable |
| spellingShingle | Naadiya Mirbahar Mansoor Ahmed Khuhro Saajid Hussain Sheeba Memon Shafique Ahmed Awan Person Identification through Harvesting Kinetic Energy University of Sindh Journal of Information and Communication Technology person identification energy efficiency kinetic energy harvesting batteryless wearable |
| title | Person Identification through Harvesting Kinetic Energy |
| title_full | Person Identification through Harvesting Kinetic Energy |
| title_fullStr | Person Identification through Harvesting Kinetic Energy |
| title_full_unstemmed | Person Identification through Harvesting Kinetic Energy |
| title_short | Person Identification through Harvesting Kinetic Energy |
| title_sort | person identification through harvesting kinetic energy |
| topic | person identification energy efficiency kinetic energy harvesting batteryless wearable |
| url | https://sujo.usindh.edu.pk/index.php/USJICT/article/view/2952 |
| work_keys_str_mv | AT naadiyamirbahar personidentificationthroughharvestingkineticenergy AT mansoorahmedkhuhro personidentificationthroughharvestingkineticenergy AT saajidhussain personidentificationthroughharvestingkineticenergy AT sheebamemon personidentificationthroughharvestingkineticenergy AT shafiqueahmedawan personidentificationthroughharvestingkineticenergy |