Identifying Users of Portable Devices from Gait Pattern with Accelerometers

Mäntyjärvi, Jani; Lindholm, Mikko; Vildjiounaite, Elena; Mäkelä, Satu-Marja; Ailisto, Heikki

doi:10.1109/icassp.2005.1415569

Cited by 288 publications

(225 citation statements)

References 12 publications

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“…Previous work using the wearable sensor-based approach includes work by Mantyjarvi et al [6], where users were identified via a small accelerometer-based device placed on a belt at the middle of the user's waistlines in back. Data was collected from thirty-six subjects who walked at fast, normal, and slow walking speeds.…”

Section: Related Workmentioning

confidence: 99%

“…While we measure user movements using an accelerometer, other studies have focused on video images of users, and this alternate method has also been shown to be effective for identification [4]. While some previous work has examined sensor-based gait recognition [5][6][7][8][9][10], our work differs in that we identify users based on the way they move during multiple activities (i.e., not just walking) using only commercially available smart phones, which are carried in the user's pocket.…”

Section: Introductionmentioning

confidence: 99%

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Cell phone-based biometric identification

Kwapisz

Weiss

Moore

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

231

161

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Abstract-Mobile devices are becoming increasingly sophisticated and now incorporate many diverse and powerful sensors. The latest generation of smart phones is especially laden with sensors, including GPS sensors, vision sensors (cameras), audio sensors (microphones), light sensors, temperature sensors, direction sensors (compasses), and acceleration sensors. In this paper we describe and evaluate a system that uses phone-based acceleration sensors, called accelerometers, to identify and authenticate cell phone users. This form of behavioral biometric identification is possible because a person's movements form a unique signature and this is reflected in the accelerometer data that they generate. To implement our system we collected accelerometer data from thirty-six users as they performed normal daily activities such as walking, jogging, and climbing stairs, aggregated this time series data into examples, and then applied standard classification algorithms to the resulting data to generate predictive models. These models either predict the identity of the individual from the set of thirty-six users, a task we call user identification, or predict whether (or not) the user is a specific user, a task we call user authentication. This work is notable because it enables identification and authentication to occur unobtrusively, without the users taking any extra actions-all they need to do is carry their cell phones. There are many uses for this work. For example, in environments where sharing may take place, our work can be used to automatically customize a mobile device to a user. It can also be used to provide device security by enabling usage for only specific users and can provide an extra level of identity verification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cell phone-based biometric identification

Kwapisz

Weiss

Moore

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

231

161

View full text Add to dashboard Cite

show abstract

“…The data that we supplied was from a variety of physical activities whereas in this work we simplify things by only using walking data. There have also been numerous other accelerometer-based biometric identification studies, where accelerometers were placed on one more body parts [6,7,13]. But it should be noted that there other means for using motion to perform biometric identification, most notably vision-based systems [17], which tend to be more popular that accelerometer-based systems.…”

Section: Related Workmentioning

confidence: 99%

Identifying user traits by mining smart phone accelerometer data

Weiss

Lockhart

2011

Proceedings of the Fifth International Workshop on Knowledge Discovery From Sensor Data

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Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as -soft biometrics‖ because these traits are not sufficiently distinctive or invariant to uniquely identify an individual-but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user's weight when calculating calories burned).

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“…Before smartwatch and other wearables are available, researchers have been studying gait based authentication using wearable motion sensors such as accelerometer or gyroscope [5,7]. In those works, a wearable sensor is located on a specific area on human body to collect motion data [8][9][10].…”

Section: Related Workmentioning

confidence: 99%

“…However this method is sensitive to the location of the hardware on human hand. Meanwhile Gait based recognition suffers from long training process [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

User-Authentication on Wearable Devices Based on Punch Gesture Biometrics

Liang

2017

ITM Web Conf.

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Abstract. Due to commoditization and convenience, wearable technology are interwoven with our daily life. However, privacy sensitive data stored on those devices such as personal email, message can be easily stolen. Most devices require a PIN input to unlock. However, this mechanism is vulnerable to shoulder surfing attack. Thus many novel authentication approaches have been proposed to solve this problem. And biometric-based methods have been adopted by many researchers because of the efficiency and excellent performance. In this paper, we propose a new biometric-based authentication system. We focus on how the user performs a straight punch gesture subconsciously. By analysis the acceleration data from the smartwatch when user performing the gesture, we are able to profile the user. And we authenticate the user according to the biometrics of this action. This mechanism is light-weighted and do not require user to remember any secret code. We develop an authentication system on Samsung Gear Fit 2 and conducted a real-world experiment on 20 volunteers. And we collected 13000 gesture samples to evaluate our system. Results show that our system can achieve a classification accuracy of at least 95.45%. In attacking scenario, our system can achieve an equal error rate lower than 4%. The maximum number of samples required by a well-trained classifier is 25.

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Identifying Users of Portable Devices from Gait Pattern with Accelerometers

Cited by 288 publications

References 12 publications

Cell phone-based biometric identification

Cell phone-based biometric identification

Identifying user traits by mining smart phone accelerometer data

User-Authentication on Wearable Devices Based on Punch Gesture Biometrics

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