Smartphone-Oriented Development of Video Data Based Services

The massive introduction of video capturing devices in Internet of Things (IoT) environments leads to development of various video data based services. In this paper, we consider the need and background on the video data based services in IoT environments. Based on the smart spaces approach, we introduce the architecture and distributed configurations to construct such services using primarily local devices and to deliver such services using smartphones. We discuss possible data models that can be used on such mediatory components as a local video server and a semantic information broker

Towards a personal at-home lab for motion video tracking in patients with Parkinson's disease

Many digital healthcare services now employ the opportunities of mobile and smart Internet technologies. The Internet is used to deliver such services as medical consultations, diagnosis, and prescriptions. The services are constructed and delivered in the ubiquitous style - anywhere, anytime, and using surrounding devices of our everyday life. In this paper, we discuss the opportunities of motion video tracking in at-home settings for a patient. Parkinson's disease (PD) serves as a case study. First, we define the problem of motion video tracking in PD patients. Then, we consider Internet-enabled methods for motion video tracking, which are essentially restricted with professional settings of a medical environment. Finally, we propose to create a personal at-home lab based on such cheap home-based cameras as any smartphone has. Our early experiment shows that such cameras provide reliable capture quality for the practical use in PD patient motion video tracking

Smartphone-based Motion Video Tracking in Patients with Parkinson’s Disease

Mobile treatment is an important component in regular medical support for people in their everyday life. A mobile application on a smartphone can recognize human images. thus tracking the behavior and gait. In this demo, we show simple use of a smartphone and its camera for motion video tracking of patients with Parkinson’s Disease. Our results indicate that such low-cost cameras can provide reliable capture quality in at-home settings