The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these gold-standard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the "leave-one-out" validation, we find that a lighter $k$-Nearest Neighbor ($k$-NN) model with $k = 2$ can obtain an average accuracy of $0.93 \pm 0.06$, $F_1$ score $0.93 \pm 0.03$, and {\em false positive rate} (FPR) below $0.08$ at 50\% level of confidence, which shows the promise of this work.
National Chiao Tung University（国立交通大学）；National Taiwan Normal University（国立台湾师范大学）
Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test cases, which is not suitable for streaming Inertial-Measurement-Unit (IMU) sensor data and fails to adapt to different scenarios. This paper presents a segmentation based gait phase detection with only a single six-axis IMU sensor, which can easily adapt to both walking and running at various speeds. The proposed segmentation uses CNN with gait phase aware recepti…
Movement disorders, such as Parkinson's disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step length provides valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, we present a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. …
KIST（韩国科学技术研究院）；University of Science & Technology（科学技术大学）；KHU（庆熙大学）
Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to explore a way of improving the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data was gathered from elderly subjects equipped with three IMU sensors while they walked. The input was taken only from the waist sensor was used to train 16 deep-learning mode…
Gait analysis (GA) has been widely used in physical activity monitoring and clinical contexts, and the estimation of the spatial-temporal gait parameters is of primary importance for GA. With the quick development of smart tiny sensors, GA methods based on wearable devices have become more popular recently. However, most existing wearable GA methods focus on data analysis from inertial sensors. In this paper, we firstly present a two-foot-worn in-shoe system (Gaitboter) based on low-cost, wearable and multimodal sensors' fusion for GA, comprising an inertial sensor and eight film-pressure sens…
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Bangladesh University of Engineering and Technology（孟加拉国工程与技术大学）；Khulna University of Engineering and Technology（库尔纳工程与技术大学）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
University of Padova（帕多瓦大学）；National Centre of Neurology and Psychiatry (NCNP)（国立精神·神经医疗研究中心）；Newcastle University（纽卡斯尔大学）；Newcastle upon Tyne Hospitals NHS Foundation Trust（纽卡斯尔医院NHS基金会信托）
Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson's disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth…
Activity monitors are widely used to measure various physical activities (PA) as an indicator of mobility, fitness and general health. Similarly, real-time monitoring of longitudinal trends in step count has significant clinical potential as a personalized measure of disease related changes in daily activity. However, inconsistent step count accuracy across vendors, body locations, and individual gait differences limits clinical utility. The tri-axial accelerometer inside PA monitors can be exploited to improve step count accuracy across devices and individuals. In this study, we hypothesize: …
Technische Universität Dresden（德累斯顿工业大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Padova（帕多瓦大学）；M31
Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and behavioral biometric information. Here, we are concerned with gait analysis systems that extract meaningful information from a user's movements to identify anomalies and changes in their walking style. The solution that is put forward is sub…
Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data
University of Exeter（埃克塞特大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Cambridge（剑桥大学）；University of Exeter Medical School（埃克塞特大学医学院）；NIHR Exeter BRC（英国国立卫生研究院埃克塞特生物医学研究中心）
Passive and non-obtrusive health monitoring using wearables can potentially bring new insights into the user's health status throughout the day and may support clinical diagnosis and treatment. However, identifying segments of free-living data that sufficiently reflect the user's health is challenging. In this work we have studied the problem of modelling real-life gait which is a very indicative behaviour for multiple movement disorders including Parkinson's disease (PD). We have developed a probabilistic framework for unsupervised analysis of the gait, clustering it into different types, whi…
As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implici…
Coronary artery disease, heart failure, angina pectoris and diabetes are among the leading causes of morbidity and mortality over the globe. Susceptibility to such disorders is compounded by changing lifestyles, poor dietary routines, aging and obesity. Besides, conventional diagnostics are limited in their capability to detect such pathologies at an early stage. This generates demand for automatic recommender systems that could effectively monitor and predict pathogenic behaviors in the body. To this end, we propose human gait analysis for predicting two important physiological parameters ass…
Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. E…
Rupert-Karls-University Heidelberg（海德堡鲁珀特-卡尔斯大学）；University of Oslo（奥斯陆大学）；University of Chicago（芝加哥大学）
Breathing disorders such as sleep apnea is a critical disorder that affects a large number of individuals due to the insufficient capacity of the lungs to contain/exchange oxygen and carbon dioxide to ensure that the body is in the stable state of homeostasis. Respiratory Measurements such as minute ventilation can be used in correlation with other physiological measurements such as heart rate and heart rate variability for remote monitoring of health and detecting symptoms of such breathing related disorders. In this work, we formulate a deep learning based approach to measure remote ventilat…
In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wireless wearable system may lead to incorrect results due to uneven breathing. To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data. The DCT…
Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation
Department of Computer Science and Engineering, Seoul National University of Science and Technology（首尔科学技术大学计算机科学与工程系）；Geometric Media Lab, School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学几何媒体实验室）；School for Engineering of Matter, Transport and Energy, Arizona State University（亚利桑那州立大学工程与能量科学学院）
Clinical methods that assess gait in Parkinson's Disease (PD) are mostly qualitative. Quantitative methods necessitate costly instrumentation or cumbersome wearable devices, which limits their usability. Only few of these methods can discriminate different stages in PD progression. This study applies machine learning methods to discriminate six stages of PD. The data was acquired by low cost walker-mounted sensors in an experiment at a movement disorders clinic and the PD stages were clinically labeled. A large set of features, some unique to this study are extracted and three feature selectio…
Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Department of Computer Science and Engineering, Seoul National University of Science and Technology（首尔科学技术大学计算机科学与工程系）；Geometric Media Lab, School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University（亚利桑那州立大学几何媒体实验室、艺术、媒体与工程学院和电气、计算机与能源工程学院）；School for Engineering of Matter, Transport and Energy, Arizona State University（亚利桑那州立大学工程科学学院）；Department of Computer Science and Engineering, University of Nevada, Reno（内华达大学里诺分校计算机科学与工程系）
