Personal Identification using Gait Data on Slipper-device with Accelerometer

Fujii, Miyu; Kato, Kaho; Xia, Chengshuo; Sugiura, Yuta

doi:10.1145/3429360.3468185

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…To protect personal privacy, some wearable devices and other daily accessory-based equipment exist. For example, Fujii et al [10] proposed a method that used time series acquired by multiple accelerometers attached to slippers. They used fast Fourier transform to extract features and a support vector machine (SVM) to identify the frequency features.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Such systems have already presented a great performance, and the accuracy typically was higher than 90%. For example, Fuji et al [10] designed a slipper embedded by several IMUs, and the device could identify 10 people with 93.3% accuracy with 3 IMU used. Choi et al [27] used 8 pressure sensors and 1 IMU to design a smart insole, which could show over 95% accuracy with 14 people tested.…”

Section: A Performance and Characteristicsmentioning

confidence: 99%

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Privacy-Aware Gait Identification With Ultralow-Dimensional Data Using a Distance Sensor

2023

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As one of the most natural user behaviors, walking has been widely focused on developing personal identification systems due to its unique biometric authentication features. Popular visual solutions are usually affected by various environmental conditions, and their redundant user information (e.g., body type and appearance) makes it more challenging for users to maintain privacy and security. This paper proposes a distance sensor-based gait identification system that uses only one-dimensional data with a simple system structure. Specifically, a time-of-flight sensor was placed in front of a walking person, and a time series of distances was acquired. We extracted gait features from the data by calculating the velocity and acceleration curves and identifying individuals using a random forest classifier. We evaluated our system on 10 users using leave-one-out cross-validation. The average identification accuracy was 91.05% for 10 users. This study shows that gait recognition is possible using only one-dimensional time-series data with a noncontact sensor. It can be used as a contactless identification, reducing the computational resources required for low-cost and low-power consumption edge computing.

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Section: Background and Related Workmentioning

confidence: 99%

Section: A Performance and Characteristicsmentioning

confidence: 99%