Accelerometer-Based Speed-Adaptive Gait Authentication Method for Wearable IoT Devices

Sun, Fangmin; Mao, Chenfei; Fan, Xiaomao; Li, Ye

doi:10.1109/jiot.2018.2860592

Cited by 90 publications

(51 citation statements)

References 28 publications

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“…However, the sampled signal quality was found to be deteriorated by the external environment. Human gait, due to its uniqueness and non-variability over time, has started to be utilized for securing wearable IoT devices in numerous studies [16][17][18][19][20][21][22][23][24][25][26][27][28][29], and is becoming an emerging research field.…”

Section: A Random Bit Sequence Generation Methodsmentioning

confidence: 99%

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Accelerometer-Based Key Generation and Distribution Method for Wearable IoT Devices

Sun

Zang

Huang

et al. 2021

IEEE Internet Things J.

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With the fast development of wearable IoT devices, their applications are becoming more and more pervasive, ranging from social networking, payment, and navigation to health and activity monitoring. The security of the communication between these devices is essential to protect the transmitted sensitive information from tampering and eavesdropping. With the integration of accelerometers into wearable IoT devices, the gait-based biometric cryptography technology has emerged as a data securing tool for wearables. This paper proposes a lightweight noise-based group key generation method, which utilizes the noise signals imposed on the raw acceleration signals to generate an M-bit key with high randomness and bit generation rate. Moreover, a signed sliding window coding (SSWC)-based common feature extraction method was designed to extract the common feature for sharing the generated M-bit key among devices worn on different body parts. Finally, a fuzzy vault-based group key distribution system was implemented and evaluated using a public dataset. The performed comprehensive analysis of the proposed key generation and distribution method proved that the binary keys generated via the introduced noise-based procedure have high entropy and can pass both the NIST and Dieharder statistical tests with high efficiency. The experimental results obtained prove the robustness of the proposed SSWC-based common feature extraction method in terms of the similarity and discriminability of intra-and inter-class features, respectively.

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Section: A Random Bit Sequence Generation Methodsmentioning

confidence: 99%

“…However, these physiological biometrics require specialized sensors to be in physical contact with the human body, which can make the user uncomfortable. Moreover, the sampled signal quality can be easily interfered with by the external environment and human activity [16].…”

mentioning

confidence: 99%

Accelerometer-Based Key Generation and Distribution Method for Wearable IoT Devices

Sun

Zang

Huang

et al. 2021

IEEE Internet Things J.

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“…The huge demands of LBS devices in the future wearable IoT (WIoT) market has promoted investigations in the related areas, and a large number of investigations on indoor localization techniques, methods, and systems are reported in the literature [15,16]. New techniques and methods have been introduced to this field and combined methods have been proposed and practiced.…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

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“…In [3] key generation is done based on the nearest-neighbor algorithm. Pairing based on accelerometer data for wearable devices is addressed in [37] and [16]. The work in [4] also add audio data (by using microphones and speakers) in addition to data collected from acceleration sensors.…”

Section: A Related Workmentioning

confidence: 99%

Secure Accelerometer-Based Pairing of Mobile Devices in Multi-Modal Transport

et al. 2020

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We address the secure pairing of mobile devices based on accelerometer data under various transportation environments, e.g., train, tram, car, bike, walking, etc. As users commonly commute by several transportation modes, extracting session keys from various scenarios to secure the private network of user's devices or even the public network formed by devices belonging to distinct users that share the same location is crucial. The main goal of our work is to establish the amount of entropy that can be collected from these environments in order to determine concrete security bounds for each environment. We test several signal processing techniques on the extracted data, e.g., low-pass and high-pass filters, then apply sigma-delta modulation in order to expand the size of the feature vectors and increase both the pairing success rate and security level. Further, we bootstrap secure session keys by the use of existing cryptographic building blocks EKE (Encrypted Key Exchange) and SPEKE (Simple Password Exponential Key Exchange). We implement our proof-of-concept application on Android smart-phones and take benefit from numerical processing environments for the off-line analysis of the collected datasets.

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Accelerometer-Based Speed-Adaptive Gait Authentication Method for Wearable IoT Devices

Cited by 90 publications

References 28 publications

Accelerometer-Based Key Generation and Distribution Method for Wearable IoT Devices

Accelerometer-Based Key Generation and Distribution Method for Wearable IoT Devices

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Secure Accelerometer-Based Pairing of Mobile Devices in Multi-Modal Transport

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