Clinical quality guaranteed physiological data compression in mobile health monitoring

2012 ◽  
Author(s):  
Sungwon Yang ◽  
Jihyoung Kim ◽  
Mario Gerla
Keyword(s):  
Data Compression ◽  
Mobile Health ◽  
Health Monitoring ◽  
Physiological Data ◽  
Clinical Quality ◽  
Mobile Health Monitoring

scholarly journals Stretchable, Patch-Type, Wireless, 6-axis Inertial Measurement Unit for Mobile Health Monitoring

2020 ◽  
Vol 14 ◽  
pp. 16-21 ◽  
Author(s):  
Jae Keun Lee ◽  
Kangil Kim ◽  
Sangmin Lee
Keyword(s):  
Mobile Health ◽  
Human Body ◽  
Health Monitoring ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Biometric Data ◽  
Patch Type ◽  
Inertial Measurement ◽  
Dependency Care ◽  
Mobile Health Monitoring

Wearable devices which measure and transfer signals from the human body can provide useful biometric data for various biomedical applications. In this paper, we present an implementation of the advanced Inertial Measurement Unit (IMU) with wireless communication technology for mobile health monitoring. The device consists of rigid silicon-based components on a flexible/stretchable substrate for applications in epidermal electronic devices to collect precise data from the human body. Using the Bluetooth Low Energy (BLE) System-on-a-chip (SoC), the device can be miniaturized and portable, and the collected data can be processed with low power consumption. The dimensions of the implemented system are approximately 40 mm × 40 mm × 100 mm. Also, the device can be attached closely to human skin, which results in minimized signal distortion due to body movements or skin deformations. In order to achieve device flexibility and stretch ability, the interconnection wires are designed as serpentine-shaped structures on a stretchable substrate. The previously reported “cut-and-paste” method is utilized to fabricate the device that produces complex, twisty interconnections with thin metal sheets. The implemented patch-type, wireless, 6-axis IMU is expected to have potential in various applications, such as health monitoring, dependency care, and daily lifelogging.

scholarly journals Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1932
Author(s):  
Ramyar Saeedi ◽  
Keyvan Sasani ◽  
Assefaw H. Gebremedhin
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Mobile Health ◽  
Health Monitoring ◽  
Learning Models ◽  
Healthcare Applications ◽  
User Data ◽  
Adaptation Cost ◽  
Mobile Health Monitoring ◽  
Machine Learning Models

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

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