User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems

Abstract

The rapid deployment of sensing technology in smartphones and the explosion of their usage in people's daily lives provide users with the ability to collectively sense the world. This leads to a growing trend of mobile healthcare systems utilizing sensing data collected from smartphones with/without additional external sensors to analyze and understand people's physical and mental states. However, such healthcare systems are vulnerable to user spoofing, in which an adversary distributes his registered device to other users such that data collected from these users can be claimed as his own to obtain more healthcare benefits and undermine the successful operation of mobile healthcare systems. Existing mitigation approaches either only rely on a secret PIN number (which can not deal with colluded attacks) or require an explicit user action for verification. In this paper, we propose a user verification system leveraging unique gait patterns derived from acceleration readings to detect possible user spoofing in mobile healthcare systems. Our framework exploits the readily available accelerometers embedded within smartphones for user verification. Specifically, our user spoofing mitigation framework (which consists of three components, namely Step Cycle Identification, Step Cycle Interpolation, and Similarity Comparison) is used to extract gait patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. We show that our framework can be implemented in two ways: user-centric and server-centric, and it is robust to not only random but also mimic attacks. Our extensive experiments using over 3,000 smartphone-based traces with mobile phones placed on different body positions confirm the effectiveness of the proposed framework with users walking at various speeds. This strongly indicates the feasibility of using smartphone based low grade accelerometer to conduct gait recognition and facilitate effective user verification without active user cooperation.

Gait Identification using Smartphone Handheld Placement with Linear Interpolation Factor, Single Magnitude and One-vs-One Classifier Mapping

“…In the work conducted by Ren, [8], the number of subjects (classes) is 26. In other work by Hoang [16], the number of subject involved is even smaller which is only 14.…”

“…In wearable sensor using a smartphone, the position varies among the past researcher. Most common positions are in pocket [2][3][4], pouch [3][4][5][6], clipped to the waistband of the clothes [7] and multiple body position [8]. Based on the mentioned position and work, it can be seen that none of them tried placing the phone on the palm or handheld.…”

“…In the work conducted by [2,6,8,16] for gait recognition using accelerometer, single classifier method has been adopted for classification. The result is quite convincing but it can be further improved in creating a more efficient and robust learning structure.…”

Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning

“…It has been used in various fields such as robotics [9], computer engineering [10,11], physical science and health industry [12], natural sciences [13], and industrial academic areas [14][15][16]. As an illustration, Sileye and Jean-Marc [17] deployed head pose detection using the Hidden Markov Model to recognize the visual focus of attention of participants in meetings.…”

A data hiding approach for sensitive smartphone data

“…Additionally, the technique allows us to verify the integrity of embedded sensitive data, for example to confirm that no tampering has taken place in the form of record removal, decimal rounding, or filtering. Finally, our technique allows users to prove ownership of sensor or healthcare data that they have shared, and as such provides spoof resistance against tampered medical sensor data [31].…”