Gait-based identification for elderly users in wearable healthcare systems

Sun, Fangmin; Zang, Weilin; Gravina, Raffaele; Fortino, Giancarlo; Li, Ye

doi:10.1016/j.inffus.2019.06.023

Cited by 96 publications

(44 citation statements)

References 32 publications

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“…The biggest challenges in gait template generation is the adaptability of algorithms when gait speed changes. That is why it is recommended that at least 3 different algorithms are used when generating such template (third as a fallback algorithm) [61]. The splasticity of algorithms used is mostly affected by the wearer's age.…”

Section: Terminal Swingmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: Terminal Swingmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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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%

“…Due to human gait uniqueness and non-variability over time, it has been utilized for user authentication or identification of wearable IoT devices in multiple previous studies [19][20][21][22]. These studies used gait information to protect the security of the wearable devices by providing a gait-based access control mechanism; however, the safety of the data transmitted between devices was not guaranteed.…”

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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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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“…There are evidences of versatile use of wearable devices for emerging applications in healthcare monitoring [24][25][26]. Various new sensors for healthcare tracking have been devised in the recent past for different purposes [27][28][29][30][31] and these devices have been used for various applications [32,33]. The commonly acknowledged applications of these wearable devices include mental health assessment [34] and sleep monitoring [35].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

2020

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Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

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Gait-based identification for elderly users in wearable healthcare systems

Cited by 96 publications

References 32 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

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

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

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