The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Inertial Sensor-Based Gait Recognition: A Review
Open accessAbstract
With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.
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Cited by
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2985628 · Cited by 305
Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment
Tran, L., Hoang, T., Nguyen, T., Kim, H., & Choi, D. (2021) · IEEE Access · doi:10.1109/access.2021.3056880 · Cited by 90
Lebel, K., Boissy, P., Nguyen, H., & Duval, C. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0347-6 · Cited by 65
Wearable gait sensors to measure ataxia due to spinocerebellar degeneration
Terayama, K., Sakakibara, R., & Ogawa, A. (2018) · Neurology and Clinical Neuroscience · doi:10.1111/ncn3.12174 · Cited by 16
Metadata
- Authors
- Sprager, S., & Juric, M.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2015-09-02
- Volume
- 15
- Issue
- 9
- Pages
- 22089-22127
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W1840637580
- Cited by
- 347
- References
- 72
- FWCI
- 14.63
- Type
- journal article
- Language
- en
- Open access
- gold
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