Motion-To-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users

Yao, Yumin; Song, Lina; Ye, Jin

doi:10.3390/s20041134

Cited by 11 publications

(18 citation statements)

References 30 publications

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“…Recently, the combination of CNNs and LSTM in a unified stack framework has already offered state-of-the-art results in sensor-based recognition [7]. In our previous study, we developed a hybrid deep neural network [9] for gait analysis using data captured from built-in motion sensors in smartphones. The hybrid deep neural network overcomes the challenge of environmental factors.…”

Section: Height Of Heelsmentioning

confidence: 99%

“…Two datasets were collected for identification and verification. In a previous work, we also proposed a hybrid deep neural network [9] to predict the BMI of smartphone users, which was also based on the characteristics of body movement captured by the smartphone's built-in motion sensors.…”

Section: Previous Deep Learning Approaches For Motion Sensors-based Rmentioning

confidence: 99%

“…In the previous work [9], we used the fully connected (FC) layer on top of two-stream CNN-LSTM to produce probability scores on target labels. However, in this paper, to overcome the "one-stream-dominating-the-network" problem, the designed fusion module is combined with the attention weighted learning strategy.…”

Section: Fusion Modulementioning

confidence: 99%

“…To illustrate the difficulty of the task we also compared the approach proposed in this work with standard classification methods typically used for automated assessment systems in other sensor-based recognition [9,[55][56][57][58].…”

Section: Baselinesmentioning

confidence: 99%

“…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. More and more researchers have used the motion characteristics of the human body for various tasks, which ranged from activity recognition [1][2][3][4][5][6], gesture categorization [7], clinical condition monitoring [8], BMI predication [9], to user gait recognition [10][11][12][13]. In particular, identity recognition using the dynamics of the walking pattern seems a promising technique in preventing the use of smart devices and other systems linked with them without the owner's permission.…”

Section: Introductionmentioning

confidence: 99%

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Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

2020

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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.

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Section: Height Of Heelsmentioning

confidence: 99%

Section: Previous Deep Learning Approaches For Motion Sensors-based Rmentioning

confidence: 99%

Section: Fusion Modulementioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

2020

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Next Generation Mobile Sensors: Review Regarding the Significance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

Bocu

2023

Lecture Notes in Networks and Systems

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Enhancing user awareness on inferences obtained from fitness trackers data

Kounoudes

Kapitsaki

Katakis

2023

User Model User-Adap Inter

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In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

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Motion-To-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users

Cited by 11 publications

References 30 publications

Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

Sensing-HH: A Deep Hybrid Attention Model for Footwear Recognition

Next Generation Mobile Sensors: Review Regarding the Significance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

Enhancing user awareness on inferences obtained from fitness trackers data

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