A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

BACKGROUND: Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. METHODS: Using off-the-shelf wireless sensors we designed a prototype WBAN which features a standard ZigBee compliant radio and a common set of physiological, kinetic, and environmental sensors. RESULTS: We introduce a multi-tier telemedicine system and describe how we optimized our prototype WBAN implementation for computer-assisted physical rehabilitation applications and ambulatory monitoring. The system performs real-time analysis of sensors' data, provides guidance and feedback to the user, and can generate warnings based on the user's state, level of activity, and environmental conditions. In addition, all recorded information can be transferred to medical servers via the Internet and seamlessly integrated into the user's electronic medical record and research databases. CONCLUSION: WBANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for prolonged periods of time. To make this technology ubiquitous and affordable, a number of challenging issues should be resolved, such as system design, configuration and customization, seamless integration, standardization, further utilization of common off-the-shelf components, security and privacy, and social issues

Instruction-set customization for multi-tasking embedded systems

Ph.DDOCTOR OF PHILOSOPH

Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Role of prosumer driven 3D food printing in innovating food value chains

Digital platforms have created an impact in almost all facets of our life in a short period. Today, they are an integral and critical part of consumer experience. When combined with revolutionary 3d printing technology, these platforms are great enablers of prosumption, i.e., production undertaken by consumers. The associated paradigm change is already visible in the specialized goods sector. With the emergence of 3d food printing technology, similar changes are very much anticipated in the food sector. The purpose of this master’s thesis is to create an understanding on how digitally-driven 3d food printing could be best utilized for food prosumption. Three research questions were raised with an aim of identifying key challenges, and uncertainties in prosumer driven 3d food printing; defining the characteristics and customization parameters of a prosumer platform for 3d food printing; and identifying most potential archetypes and use cases for prosumer-driven 3d food printing. To answer the research questions, 3 research themes were identified, namely food value chain, prosumption, and 3d food printing. After an extensive literature review process based upon the research themes, relevant data were gathered using Mixed Methods Research (MMR) approach. 15 semi-structured interviews were conducted with experts from industry and academia. This was followed by a quantitative survey with a pool of respondents from within the identified research themes. Finally, a stakeholder workshop was carried out to finalize and further refine the concepts generated through MMR. Personalized nutrition is found to be an area where 3d food printing has a lot of scope, especially for applications in fitness centres, senior homes, and hospitals. Also, utilization of prosumer driven 3d food printing in fine dining restaurants has one of the highest business potential and feasibility at this point of time. Overall, the research implies that leveraging digital platforms in 3d food printing has the potential to generate futuristic food value chains that are connected, collaborative, data-driven, and transparent

UML-Based co-design framework for body sensor network applications

Ph.DDOCTOR OF PHILOSOPH

Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page

Integrating Various Sensor Readings from MySignals into a Standalone Mobile Health App

This project integrates various health parameters Temperature, Heart Rate, Pulse Rate, Blood Pressure, Respiration Rate, ECG, EMG, GSR, Spirometry values, Snore, Blood Sugar, Body Positions and Weight measured through wired and wireless sensors, into a standalone mobile health app. It shows Health Status of the user based on these parameters, and displays the values in different colors for normal and abnormal readings. Users can plot graphs of their selected parameters to enhance their understanding and how one parameter can affect the other. The project also presents a deeper analysis of Glucose and Heart Rate by calculating Glucose Variability and Heart Rate Variability. These analyses give patients and doctors more insightful information about their health and may act as a guidance to decide a better treatment regimen.It can also count the number of steps of patients for monitoring their activity and send SMS to patients and caregivers if the Health Status is abnormal