Digitization of Paper Electrocardiogram

In order to diagnose a possible cardiac disorder, ECG (electrocardiogram) signals are usually recorded on standard grid papers in hospitals. Many efforts have been made to advance the technology in order to improve the diagnosis and management of cardiovascular disease. There is a need to convert the existing ECG records into digital forms, as it is the most efficient method to store and analyze ECG attributes for clinical uses. The main purpose of this chapter is to review the existing algorithms for digital conversion of paper ECG. It discusses the various challenges and a systematic study on different methods that have been used so far to convert paper ECG records into digitized form so that they can be retrieved efficiently. Initial challenge involved in the digitization process is gridline removal. In this process, information of ECG signal is also removed. None of the existing methods provide flawless gridline removal. The paper ECG used in hospitals differs in shape, size, formats, so the main challenge in digitization process is to achieve a worldwide ECG format.

Multi-Modal Motion-Capture-Based Biometric Systems for Emergency Response and Patient Rehabilitation

This chapter outlines the current state of the art of Kinect sensor gait and activity authentication. It also focuses on emotional cues that could be observed from human body and posture. It presents a prototype of a system that combines recently developed behavioral gait and posture recognition methods for human emotion identification. A backbone of the system is Kinect sensor gait recognition, which explores the relationship between joint-relative angles and joint-relative distances through machine learning. The chapter then introduces a real-time gesture recognition system developed using Kinect sensor and trained with SVM classifier. Preliminary experimental results demonstrate accuracy and feasibility of using such systems in real-world scenarios. While gait and emotion from body movement has been researched in the context of standalone biometric security systems, they were never previously explored for physiotherapy rehabilitation and real-time patient feedback. The survey of recent progress and open problems in crucial areas of medical patient rehabilitation and rescue operations conclude this chapter.

EarLocalizer

With much concern over security, it has become essential to maintain the identity and track of an individual's activities in the modern healthcare sector. Although there are biometric authentication systems based on different modalities, recognition of a person using the ear has gained much attention as ears are unique. Ear localization is a first step for ear-based biometric authentication systems, and this needs to be accurate, since it plays a crucial role in the overall performance of the system. The localization of ear in the side face images captured in the wild possess great challenges due to varying angles, light, scale, background clutter, blur and occlusion, etc. In this chapter, the authors have proposed EarLocalizer model to localize the ear, which is inspired by Faster-RCNN. The model is evaluated on two wild ear databases, UBEAR-II and USTB-III, and has achieved an accuracy of 95% and 99.08%, respectively, at IOU (Intersection over Union) = 0.5. The results of the proposed model signify that the model is invariant to the environmental conditions.

Voice Liveness Detection for Medical Devices

Many healthcare providers integrate biometric recognition/verification schemes into patient identification or other information security systems. While overcoming the disadvantages of using passwords, PINs, and tokens which may be forgotten, or stolen, biometric systems are susceptible to spoofing attacks, or presentation attacks. Liveness detection is an effective mechanism used to defeat a presentation attack. This chapter focuses on voice liveness detection in automatic speaker verification (ASV) systems. The authors explain the spoofing attacks to ASV systems comprising impersonation, voice conversion, speech synthesis, and replay and then present four types of liveness detection (anti-spoofing) methods used to mitigate ASV spoofing attacks: challenge-response-based methods, acoustic feature-based methods, hardware-based methods, and multi-modal biometric-based methods. This chapter analyzes the advantages and disadvantages of each kind of liveness detection method and proposes the possible application of voiceprint-based liveness detection schemes in the insulin pump system.

Pre-Clinical ASD Screening Using Multi-Biometrics-Based Systems

Autism Spectrum Disorder (ASD) screening is still an ongoing process due to few objective and effective screening approaches. The consideration of facial patterns, fingerprints, and other metrics such as footprints and ear size can well explain the ASD phenotypes. These physical anomalies provide a simpler and more objective approach to screen the disorder through rather than considering the complex biological factors. Moreover, it is very easy to acquire these metrics as compared to those lengthier and restrictive procedures. The screening engine in which both the biometrics are integrated has better and reliable outcomes as compared to singular approaches. The objective of this chapter is to present an ASD screening system based on the combination of biometrics of face and fingers. The novelty of the chapter is that the classifier-based matching and fusion of the modalities has been proposed. Hence, multi-biometric-based pre-clinical system has the potential to follow ASD-affected individuals continuously, objectively, and periodically.

Indexing for Healthcare Biometric Databases

The healthcare industry offers highly personalized services to its patients. It is necessary to correctly identify the patient and efficiently link his medical records as and when needed. It is important to note that the need of identification arises in many desperate scenarios when the patient may not be able to tell anything about himself. Biometrics can help in this scenario by using physiological or behavioral characteristics. Some of the biometrics traits could be acquired without direct participation of the person, and therefore, the patient need not provide any pin, password, or token for his identification. Biometrics can handle challenges of duplicate medical records and identity theft. However, there is an important issue that may arise when a large number of patients get registered to the system. Increase in the size of the biometric database gradually escalates the time required for identification. This calls for the need of an efficient indexing approach that can confine the search space and decrease the response time. This chapter highlights biometric indexing approaches suitable for the healthcare industry.

Deep Learning for Healthcare Biometrics

Mistakes in healthcare systems such as a mix-up of records or confusing medical charts lead to the wrong medications to patients. Major tasks such as administrative costs, legal expenses, and liabilities incur high cost to the healthcare industry using traditional, inaccurate patient identification processes. This can be resolved by biometric technology. Only physiological features can be used for patient identification to eliminate need of SSN, insurance card, or date of birth during registration. A biometric template can be directly mapped to an electronic health record to accurately authenticate individuals on subsequent visits. This technology ensures no medical records can be mimicked and the right care is provided to the right patient. Deep learning provides a platform to solve identification and diagnostic problems arising in medicine and can be used in healthcare biometrics to analyze clinical parameters and their combinations for disease prognosis (e.g., prediction of disease, extracting medical knowledge, therapy planning, and support).

Security, Reliability, and Performance Assessment for Healthcare Biometrics

A health system comprises, all things considered, individuals and activities whose essential purpose is to advance, reestablish, or look after wellbeing. This incorporates endeavors to impact the determinants of wellbeing-enhancing exercises. Biometrics has reformed the human services industry; gadgets can take exceptional data about you from your eye, your imprint, or your thumbprint and utilize it to recognize you. This data is utilized to guarantee that you are who you say you are, and you have the authorization to work with the medicinal services data you are endeavoring to get to. It offers a thorough review of biometric innovation applications intending to electronic social insurance security issues. Information classification, information legitimacy, information respectability, and client/substance verification are discussed in this chapter along with security, which is viewed as a foundation for medicinal services data frameworks as they contain amazingly touchy data.

Biometric Technologies in Healthcare Biometrics

The aim of this chapter is to introduce biometrics systems and discuss the essential components of biometrics technologies in the healthcare system. The discussion also includes the state-of-the-art biometrics technologies, selection criteria of a suitable biometrics system, biometrics identity management, and multi-biometrics fusion for healthcare biometrics system.