Photoplethysmogram based biometric identification for twins incorporating gender variability

This study focuses on a Photoplethysmogram (PPG) based biometric identification for twins incorporating gender variability. To the best of our knowledge, little has been said pertaining to this research which identifies twins using PPG signals. PPG device has been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. PPG signals has the capability to ensure the person to be present during the acquisition process which suggest that PPG can provide liveness detection suitable for a biometric system which is not available in other biometric modalities such as fingerprint. A total of four couple of twins which consists of four female and four male subjects in age range between twenty two to thirty years old were used to assess the feasibility of the proposed system. The acquired PPG signals were then processed to remove unwanted noise using low pass filter. After that, multiple cycles of PPG waveforms were extracted and later classified using Radial Basis Function (RBF) and Bayes Network (BN) to categorize the subjects using the discriminant features to calculate and analyze the performance of this system. The outcome also provides a complimentary mechanism to detect twins besides using the current existing methods

Photoplethysmogram Based Biometric Identification for Twins Incorporating Gender Variability

This study focuses on a Photoplethysmogram (PPG) based biometric identification for twins incorporating gender variability. To the best of our knowledge, little has been said pertaining to this research which identifies twins using PPG signals. PPG device has been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. PPG signals has the capability to ensure the person to be present during the acquisition process which suggest that PPG can provide liveness detection suitable for a biometric system which is not available in other biometric modalities such as fingerprint. A total of four couple of twins which consists of four female and four male subjects in age range between twenty two to thirty years old were used to assess the feasibility of the proposed system. The acquired PPG signals were then processed to remove unwanted noise using low pass filter. After that, multiple cycles of PPG waveforms were extracted and later classified using Radial Basis Function (RBF) and Bayes Network (BN) to categorize the subjects using the discriminant features to calculate and analyze the performance of this system. The outcome also provides a complimentary mechanism to detect twins besides using the current existing methods

Development of an Electrocardiogram Based Biometric Identification System: A Case Study in the University

This paper focuses on the electrocardiogram (ECG) based biometric identification system in the university scenario as an alternative to the traditional methods being used nowadays. There are a lot of researches and studies about ECG based biometric system where some of them showed positive result. However, ECG based biometric system in the university scenario is under-researched. Therefore, this issue will be the main focus of our study. A total of five subjects were used for experimentation purposes. A bandpass filter is used to remove unwanted portion of the signal. Unique features are extracted from these filtered ECG signals. Later, Multilayer Perceptron and Naïve Bayes are used to classify the subjects using the discriminant features. Based on the experimentation results, classification accuracies of 90% and 80 % were achieved which suggest the capability of our proposed system to identify individuals. The result provides an alternative mechanism to detect a person besides using the traditional method

Biometric Recognition for Twins Inconsideration of Age Variability Using PPG Signals

Recent biometric modalities involve biomedical signals such as PPG to identify individuals. This study has been motivated by this new research area using PPG signal to identify twins incorporating age variability. The proposed system is suggested to be a substitute to the current traditional methods being used widely nowadays. A total of 21 subjects were used for experimentation purposes and lowpass filter is applied to remove unwanted noise from the signal. Distinctive features are extracted from the filtered PPG signals. Later, Bayes Network (BN), Naïve Bayes (NB), Radial Basis Function (RBF) and Multilayer Perceptron (MLP) were used to classify the subjects using the discriminant features. Based on the experimentation results, classification accuracies ranging from 90% to 100% were achieved by categorizing the data into six different sets which are overall dataset, Groups I, II, III, IV and V. The result provides an alternative mechanism to identify twins using PPG signals incorporating age variability besides using the traditional methods

Information, Motivation and Behavioural Factors in Influencing Diabetes Self-Care: A Conceptual Paper

Globally, diabetes is a major public health concern and has impacted an estimated 425 million adults.  The increasing trend of diabetes incidence has impacted the Malaysian population and healthcare system. Evidence from studies suggested that diabetes can be treated and controlled through behavioural intervention. These include combining lifestyle with pharmacotherapy. Scholars in health behaviour highlighted the importance of assessing and monitoring the behavioural intervention among diabetic patients in terms of psychosocial aspects, such as information, motivation and behavioural factors, in relations with diabetes self-care. This article provides an overview of the empirical evidence regarding the importance of identifying information, motivation and behavioural factors, in relations with diabetes self-care. Information is among the prominent factors in establishing good diabetes management. Motivation can be conceptually defined as factors that predispose one to action and cues to behaviour change. Behavioural factors identified in this review includes compliance towards diabetes self-care. The outcome of this review could provide a better understanding of information, motivation and behavioural factors, and its relations with diabetes self-care

Biometric recognition for twins inconsideration of age variability using PPG signals

Recent biometric modalities involve biomedical signals such as PPG to identify individuals. This study has been motivated by this new research area using PPG signal to identify twins incorporating age variability. The proposed system is suggested to be a substitute to the current traditional methods being used widely nowadays. A total of 21 subjects were used for experimentation purposes and lowpass filter is applied to remove unwanted noise from the signal. Distinctive features are extracted from the filtered PPG signals. Later, Bayes Network (BN), Naive Bayes (NB), Radial Basis Function (RBF) and Multilayer Perceptron (MLP) were used to classify the subjects using the discriminant features. Based on the experimentation results, classification accuracies ranging from 90% to 100% were achieved by categorizing the data into six different sets which are overall dataset, Groups I, II, III, IV and V. The result provides an alternative mechanism to identify twins using PPG signals incorporating age variability besides using the traditional methods

Development of an electrocardiogram based biometric identification system: a case study in the university

This paper focuses on the electrocardiogram (ECG) based biometric identification system in the university scenario as an alternative to the traditional methods being used nowadays. There are a lot of researches and studies about ECG based biometric system where some of them showed positive result. However, ECG based biometric system in the university scenario is under-researched. Therefore, this issue will be the main focus of our study. A total of five subjects were used for experimentation purposes. A bandpass filter is used to remove unwanted portion of the signal. Unique features are extracted from these filtered ECG signals. Later, Multilayer Perceptron and Naïve Bayes are used to classify the subjects using the discriminant features. Based on the experimentation results, classification accuracies of 90% and 80 % were achieved which suggest the capability of our proposed system to identify individuals. The result provides an alternative mechanism to detect a person besides using the traditional methods

Diabetes Knowledge and Perceived Information Needs: The Experiences, Views, and Challenges of Patients with Type 2 Diabetes Mellitus in Malaysia (A Qualitative Study) Diabetes Knowledge and Perceived Information Needs Among T2DM Patients

Objective Amidst the increased diabetes prevalence in Malaysia, it is important to look into matters that influence patients’ self-management. Patients’ knowledge has been determined as an initiator for the improvement in diabetes self-management. This study aimed to explore patients’ experiences, views, and challenges in obtaining diabetes knowledge as well as to assess the information needs among patients. Method Type 2 Diabetes Mellitus (T2DM) patients (n=21) were recruited from Hospital Tengku Ampuan Rahimah and Meru Health Clinic in Klang via purposive sampling. In-depth Interviews (IDI) were performed using a semi-structured interview guide comprising open-ended questions. All data were analysed thematically. Results The patients aged between 29 to 79 years old and the majority were male. Most of the patients had T2DM for more than 5 years. Their main sources of information were healthcare providers (HCP) and the media. Although patients obtained the required knowledge on diabetes from the HCP, they still faced certain challenges, including the need for further information, especially on medication and dietary practice. Conclusion T2DM patients mainly sought information from the HCP and media since both sources were easily accessible and approachable. However, certain information, particularly on medication and diet, was vague and redundant. Thus, patients often requested further detailed information to aid the development of their self-management skills. An individual-based diagnostic instrument can be beneficial as it can serve as the guideline to oversee the needs and issues in tackling patients’ issues in DM management. Furthermore, government and other relevant stakeholders can diversify the approaches to improve the information delivery process by optimising effective communication channels (i.e., printed, internet, broadcasting) to cater to T2DM patients’ information needs