A Survey of Wearable Biometric Recognition Systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work

A survey of wearable biometric recognition systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner's physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY) and the CAM Grant S2013/ICE-3095 (CIBERDINE

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Event and state detection in time series by genetic programming

Event and state detection in time series has significant value in scientific areas and real-world applications. The aim of detecting time series event and state patterns is to identify particular variations of user-interest in one or more channels of time series streams. For example, dangerous driving behaviours such as sudden braking and harsh acceleration can be detected from continuous recordings from inertial sensors. However, the existing methods are highly dependent on domain knowledge such as the size of the time series pattern and a set of effective features. Furthermore, they are not directly suitable for multi-channel time series data. In this study, we establish a genetic programming based method which can perform classification on multi-channel time series data. It does not need the domain knowledge required by the existing methods. The investigation consists of four parts: the methodology, an evaluation on event detection tasks, an evaluation on state detection tasks and an analysis on the suitability for real-world applications. In the methodology, a GP based method is proposed for processing and analysing multi-channel time series streams. The function set includes basic mathematical operations. In addition, specific functions and terminals are introduced to reserve historical information, capture temporal dependency across time points and handle dependency between channels. These functions and terminals help the GP based method to automatically find the pattern size and extract features. This study also investigates two different fitness functions - accuracy and area under the curve. The proposed method is investigated on a range of event detection tasks. The investigation starts from synthetic tasks such as detecting complete sine waves. The performance of the GP based method is compared to traditional classification methods. On the raw data the GP based method achieves 100 percent accuracy, which outperforms all the non-GP methods.The performance of the non-GP methods is comparable to the GP based method only with suitable features. In addition, the GP based method is investigated on two complex real-world event detection tasks - dangerous driving behaviour detection and video shot detection. In the task of detecting three dangerous driving behaviours from 21-channel time series data, the GP based method performs consistently better than the non-GP classifiers even when features are provided. In the video shot detection task, the GP based method achieves comparable performance on 11200-channel time series to the non-GP classifiers on 28 features. The GP based method is more accurate than a commercial product. The GP based method has also been investigated on state detection tasks. This involves synthetic tasks such as detecting concurrent high values in four of five channels and a real-world activity recognition problem. The results also show that the GP based method consistently outperforms the non-GP methods even with the presence of manually constructed features. As part of the investigation, a mobile phone based activity recognition data set was collected as there was no existing publicly available data set. The suitability of the GP based method for solving real-world problems is further analysed. Our analysis shows that the GP based method can be successfully extended for multi-class classification. The analysis of the evolved programs demonstrates that they do capture time series patterns. On synthetic data sets, the injected regularities are revealed in understandable individuals. The best programs for three real-world problems are more difficult to explain but still provide some insight. The selection of relevant channels and data points by the programs are consistent with domain knowledge. In addition, the analysis shows that the proposed method still performs well for time series pattern of different sizes. The effective window sizes of the evolved GP programs are close to the pattern size. Finally, our study on execution performance of the evolved programs shows that these programs are fast in execution and are suitable for real-time applications. In summary, the GP based method is suitable for the kinds of real-world applications studied in this thesis. This thesis concludes that, with a suitable representation, genetic programming can be an effective method for event and state detection in multi-channel time series for a range of synthetic and real-world tasks. This method does not require much domain knowledge such as the pattern size and suitable features. It offers an effective classification method in similar tasks that are studied in this thesis

Proceedings of the 4th Workshop on Interacting with Smart Objects 2015

These are the Proceedings of the 4th IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine