Golam Mushih Tanimul Ahsan

Seeing Beyond Visibility: A Four Way Fusion of User Authentication for Efficient Usable Security on Mobile Devices

Security and usability plays an important role in the success in todays computer, mobile, and technology based system. In these systems, security gets higher priority for high security measures ignoring the fact that the system may lead to an unusable one. However, everyday technology users need a system that is usable and also safe. In this paper, we propose a novel approach for usable security for mobile system i.e. a mobile system that will be usable and also secured. Traditional mobile device authentication techniques do not consider the combination of user biometrics, environmental information, and information provided by the sensors within the pervasive system. In this paper, we present a four way fusion of user authentication techniques for efficient usable security on mobile devices. In this approach user is not required to remember any alphanumeric password. The location traces, gait pattern, emotion of user and context of an image is used as metric for authentication. This system silently does authentication using location traces and gait pattern of its user. If the silent authentication fails then system authenticates its users based on users emotion sequence and an image context previously stored by the user. Our proposed fusion based authentication technique can be useful for numerous real life scenarios where mobile device security is important.

Symptom Levels in Care-Seeking Bangladeshi and Nepalese Adults With Advanced Cancer

Purpose Three-fourths of patients with advanced cancer are reported to suffer from pain. A primary barrier to provision of adequate symptom treatment is failure to appreciate the intensity of the symptoms patients are experiencing. Because data on Bangladeshi and Nepalese patients’ perceptions of their symptomatic status are limited, we sought such information using a cell phone questionnaire. Methods At tertiary care centers in Dhaka and Kathmandu, we recruited 640 and 383 adult patients, respectively, with incurable malignancy presenting for outpatient visits and instructed them for that single visit on one-time completion of a cell phone platform 15-item survey of questions about common cancer-associated symptoms and their magnitudes using Likert scales of 0 to 10. The questions were taken from the Edmonton Symptom Assessment System and the Brief Pain Inventory instruments. Results All but two Bangladeshi patients recruited agreed to study participation. Two-thirds of Bangladeshi patients reported usual pain levels ≥ 5, and 50% of Nepalese patients reported usual pain levels ≥ 4 (population differences significant at P < .001). Conclusion Bangladeshi and Nepalese adults with advanced cancer are comfortable with cell phone questionnaires about their symptoms and report high levels of pain. Greater attention to the suffering of these patients is warranted.

Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high.

Privacy in Healthcare

In recent years, the field of healthcare has seen an increased prevalence of electronic healthcare systems. Some of these systems seek to help patients make more informed decisions about their own health, while others may assist users in receiving proper care no matter where they are. Despite these positive impacts, the systems bring with them new risks. In particular, electronic healthcare systems have a variety of privacy concerns surrounding their use due to the personal nature of the data collected. In this chapter, we introduce several electronic healthcare systems that are currently in use and explore the different privacy challenges surrounding some of these systems. Finally, we highlight a few methods to address these privacy concerns and, thereby, improve privacy protection in healthcare systems.

Reality Versus Grant Application Research “Plans”

This article describes the implementation of the American Indian mHealth Smoking Dependence Study focusing on the differences between what was written in the grant application compared to what happened in reality. The study was designed to evaluate a multicomponent intervention involving 256 participants randomly assigned to one of 15 groups. Participants received either a minimal or an intense level of four intervention components: (1) nicotine replacement therapy, (2) precessation counseling, (3) cessation counseling, and (4) mHealth text messaging. The project team met via biweekly webinars as well as one to two in-person meetings per year throughout the study. The project team openly shared progress and challenges and collaborated to find proactive solutions to address challenges as compared to what was planned in the original grant application. The project team used multiple strategies to overcome unanticipated intervention issues: (1) cell phone challenges, (2) making difficult staffing decisions, (3) survey lessons, (4) nicotine replacement therapy, (5) mHealth text messages, (6) motivational interviewing counseling sessions, and (7) use of e-cigarettes. Smoking cessation studies should be designed based on the grant plans. However, on the ground reality issues needed to be addressed to assure the scientific rigor and innovativeness of this study.

An approach to localization in crowded area

Every year millions of people gather at Makkah, Saudi Arabia during the Hajj, an annual Islamic pilgrimage. The area at Makkah is small, and the number of attendees increases each year, which has created an ongoing and ever increasing problem of crowd management. In this paper, we present our integrated solution to the localization challenge of tracking specific users in a highly crowded area where GPS signal may be weak or even unavailable. Smartphone based Human Activity Recognition (HAR) uses various sensors that are built into the smartphone to sense a persons activity in real time. Applications that incorporate HAR can be used to track a persons movements and are very useful in areas such as health care. We also propose a group-tracking mechanism that can be applied when a group member appears to get lost. Other members of the group will be immediately notified and receive an estimation of the lost members location. Using wireless signals (RSSI) and inertial sensor data, we have developed a mathematical model and a system for both outdoor and indoor localization. The experimental results show that the proposed system is able to detect locations of users with high accuracy, with an error of less than 2.5 meters. The system will be used by millions of users in Makkah, where there have been thousands of reported cases of pilgrims getting lost during the Hajj, however, it is scalable to accommodate any other crowded population.

Collaborative Design with Veterans: Identifying challenges of designing mhealth solution for veterans

We are investigating how mobile technology can help through the process of civilian reintegration to address the impact of reintegration related stress on veterans. As part of our investigation we have developed the prototype of a mobile-phone based veteran peer-mentor support system called iPeer. The mobile technology facilitates real-time behavioral data collection from the participating veteran mentees. Each mentee receives a behavioral survey twice a week. A report on the mentees survey response is then presented to the veteran mentor. As a result this system facilitates remote tracking of mental-health conditions. Our primary goal is to monitor the social functioning and risk taking behaviors of the veterans trying to readjust into social life. These behaviors are considered to be indications of difficulties adjusting to civilian life, and in more extreme cases, mental illness like anxiety, depression or post-traumatic stress disorder (PTSD) [1].

A Mobile Survey Tool for Smoking Dependency Among Native Americans

Smoking and tobacco related cancers are very common among Native Americans. Gathering information during different phases of smoking cessation can help us understand different factors that may work during smoking cessation. In this paper, we will present a survey system designed to collect data for several phases of smoking cessation. We designed and developed a survey system that helps researchers to collect data from people who are going through different phases of smoking cessation. We evaluate this system from the experiences of end users and by generating reports.