Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

e-ESAS: Evolution of a Participatory Design-based Solution for Breast Cancer (BC) Patients in Rural Bangladesh

Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients’ side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS—the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses several challenges faced by patients and doctors and positively impact their lives

Smartphone Based Multimodal Activity Detection System Using Plantar Pressure Sensors

There have been numerous efforts to detect human physical activities automatically. Healthcare professionals who want to monitor patients remotely, people who want to know their measure of physical activity objectively, or people who develop context-sensitive systems are interested in such systems. A majority of such systems use accelerometers to collect data from different parts of the body. Recently, some systems have used the accelerometer and gyroscope sensors of smart phones to develop unobtrusive systems. Such systems require users to carry smart phones with them. Such requirement limits the practical usability of these systems because people often place their phones on the table while sitting and women usually carry phones in their purses. We have developed a multimodal system where we used pressure sensor data from shoes along with accelerometers and gyroscope data from smart phones to make a more robust system. In this paper, we present our novel activity detection system along with evaluation briefly

Preserve Your Privacy with PCO: A Privacy Sensitive Architecture for Context Obfuscation for Pervasive E-Community Based Applications

Context awareness is just beginning to revolutionize the ways we interact with networked devices. In order for context awareness to flourish, especially in a pervasive environment, users must be certain that their privacy is respected. Privacy in pervasive online community depends on the level of granularity of the provided information, user’s relation to possible recipients, and the possible usage of user’s data. Conventional privacy preservation techniques are not suitable for these pervasive applications. The notion of this paper is to present the preliminary results of using a unique architecture of obfuscation techniques to preserve users\u27 privacy in e-community based applications. This paper describes our current work in developing a novel Privacy-sensitive architecture for Context Obfuscation (PCO) for privacy preservation in pervasive online community based applications. More specifically, PCO safeguards a user’s privacy by generalizing the contextual data (e.g. the user’s current activity) provided to the applications and distributed to the user’s peers. To support multiple levels of granularity for the released contextual data, the obfuscation procedure uses an ontological description that states the granularity of object type instances. We have developed and evaluated a contextual instant messaging application (PCO application) in Android platform that incorporates level-based privacy of the user’s contextual information. We also evaluate our prototype application through user evaluation survey

User Privacy Protection in Pervasive Social Networking Applications Using PCO

Privacy is the most often-cited criticism of context awareness in pervasive environment, and may be the utmost barrier to its enduring success. However, privacy implications associated with pervasive online community-based applications depend on the level of identifiability of the information provided, its possible recipients, and its possible uses. Unfortunately, conventional privacy preservation techniques are not suitable for these types of application. This paper describes our current work in developing a novel privacy sensitive architecture for context obfuscation (PCO) for privacy preservation in pervasive online community-based applications. More specifically, PCO preserves users\u27 privacy by generalising request parameters as well as the context data provided to the application. To support multiple levels of granularity for the released context data, the obfuscation procedure uses an ontological description that states the granularity of object type instances. We have developed and evaluated a contextual instant messaging application (PCO application) in Android platform that incorporates level-based privacy of the user\u27s contextual information. We also evaluate our prototype application through user evaluation survey. The PCO architecture can be extended to be used in diverse online community-based applications

A Novel Activity Detection System Using Plantar Pressure Sensors and Smartphone

Physical activities detection plays a vital role to healthcare professionals who would like to monitor patients remotely and to develop context-sensitive systems. Major number of physical activity detection systems use accelerometers to collect data from different parts of the body. Since those approaches have limitations from users\u27 point of view, we have used smart phones that are coming with built-in accelerometers and gyroscopes. We have proposed and developed three novel approaches for activity recognition. Firstly, we have developed a multimodal system where we used pressure sensor data from shoes along with accelerometers and gyroscope data from smart phone. Again, we have presented the details of our novel activity detection system along with evaluation. In the second approach, we considered our sensor data as time series shapelets and apply recently developed algorithms to differentiate those shapelets. Finally, we applied Gaussian Mixture Models with time-delay embedding for detecting different activities

PryGuard: A Secure Distributed Authentication Protocol for Pervasive Computing Environment

Handheld devices have become so commonplace nowadays that they are an integral part of our everyday life. Proliferation of these mobile handheld devices equipped with wide range of capabilities has bolstered widespread popularity of pervasive computing applications. In such applications many devices interact with each other by forming ad hoc wireless networks. The necessity of such unavoidable inter-device dependency along with volatile nature of connectivity and the lack of a fixed infrastructure for authentication and authorization, devices are susceptible and vulnerable to malicious active and passive snoopers. If a device registers a malicious device as its valid neighbor, the security and privacy of entire system might be jeopardized. Such sensitivity to malevolent activity necessitates the need for a robust mechanism to maintain a list of valid devices that will help to prevent malicious devices from authenticating successfully. In this paper, we present the feasibility of using a decentralized protocol in order to prevent malicious devices from participating illicitly into the ad hoc networks