Abstract
The human gait pattern is an emerging biometric trait for user identification of smart devices. However, one of the challenges in this biometric domain is the gait pattern change caused by footwear, especially if the users are wearing high heels (HH). Wearing HH puts extra stress and pressure on various parts of the human body and it alters the wearer’s common gait pattern, which may cause difficulties in gait recognition. In this paper, we propose the Sensing-HH, a deep hybrid attention model for recognizing the subject’s shoes, flat or different types of HH, using smartphone’s motion sensors. In this model, two streams of convolutional and bidirectional long short-term memory (LSTM) networks are designed as the backbone, which extract the hierarchical spatial and temporal representations of accelerometer and gyroscope individually. We also introduce a spatio attention mechanism into the stacked convolutional layers to scan the crucial structure of the data. This mechanism enables the hybrid neural networks to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier for the footwear recognition task. To evaluate Sensing-HH, we built a dataset with 35 young females, each of whom walked for 4 min wearing shoes with varied heights of the heels. We conducted extensive experiments and the results demonstrated that the Sensing-HH outperformed the baseline models on leave-one-subject-out cross-validation (LOSO-CV). The Sensing-HH achieved the best Fm score, which was 0.827 when the smartphone was attached to the waist. This outperformed all the baseline methods at least by more than 14%. Meanwhile, the F1 Score of the Ultra HH was as high as 0.91. The results suggest the proposed model has made the footwear recognition more efficient and automated. We hope the findings from this study paves the way for a more sophisticated application using data from motion sensors, as well as lead to a path to a more robust biometric system based on gait pattern.
Highlights
With the wearable technology advancing at a fast pace, billions of smart devices have been equipped with built-in motion sensors such as accelerometers and gyroscopes
We categorize the shoes into 3 classes by the height of the heels
We developed Sensing-high heels (HH) for footwear recognition based on daily life gait data captured by built-in motion sensors from smartphones
Summary
With the wearable technology advancing at a fast pace, billions of smart devices have been equipped with built-in motion sensors such as accelerometers and gyroscopes. They can be exploited to log the body motion of users, which can be a very useful tool for the research communities studying motion sensing. The previous works [14,15] studied the gait changes related to the different shoes worn by the subjects. Their experiments were carried out with four kinds of shoes with different weights
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