Proc. SPIE. 9411, Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015
KEYWORDS: Mobile devices, Detection and tracking algorithms, Sensors, Feature extraction, Data acquisition, Data processing, Signal processing, Gyroscopes, Galactic astronomy, Global Positioning System
Modern mobile devices contain integrated sensors that enable multitude of applications in such fields as mobile health (mHealth), entertainment, sports, etc. Human physical activity monitoring is one of such the emerging applications. There exists a range of challenges that relate to activity monitoring tasks, and, particularly, exploiting optimal solutions and architectures for respective mobile software application development.
This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption. Energy consumption significantly varies as sensor data acquisition is followed by data analysis including various transformations and signal processing algorithms. This paper will address several aspects of more optimal activity monitoring implementations exploiting state-of-the-art capabilities of modern platforms.
Cell phones and other mobile devices become part of human culture and change activity and lifestyle patterns. Mobile
phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications.
Latest generations of smart phones incorporate GPS and WLAN location finding modules, vision cameras, microphones,
accelerometers, temperature sensors etc. The availability of these sensors in mass-market communication devices creates
exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors
are very promising. This paper reviews different approaches of human activity recognition.
Cellular telephony has become a bright example of co-evolution of human society and information technology. This
trend has also been reflected in health care and health promotion projects which included cell phones in data collection
and communication chain. While many successful projects have been realized, the review of phone-based data collection
techniques reveals that the existing technologies do not completely address health promotion research needs. The paper
presents approaches which close this gap by extending existing versatile platforms. The messaging systems are designed
for a health-promotion research to prevent obesity and
obesity-related health disparities among low-income Latino
adolescent girls. Messaging and polling mechanisms are used to communicate and automatically process response data
for the target constituency. Preliminary survey data provide an insight on phone availability and technology perception
for the study group.