Enhancing Mobile Robots’ Navigation through Mobility Assessments in Domestic Environments

Isken, Melvin; Vester, Björn; Frenken, Thomas; Steen, Enno-E.; Brell, Melina; Hein, Andreas

doi:10.1007/978-3-642-18167-2_16

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The utilisation of numerous devices from diverse vendors can result in a range of problems, like uncommon standards for data exchange and wireless and wired connectivity; different models of usage like using devices with the same function in another way; and having no connectivity between the devices (Isken, Frenken, Brell, & Hein Offis, 2011). Isken et al claim that with a general standard for AAL technologies and data exchange, such problems could be managed.…”

Section: Technology Concernsmentioning

confidence: 99%

Using RFID and Wi-Fi in Healthcare

Dingli

Seychell²

2014

International Journal of E-Health and Medical Communications

View full text Add to dashboard Cite

Europe faces a considerable challenge in providing good quality health care in the forthcoming future as the aging population increases. The phenomenon also results in a considerable cost on society due to the dependency on the public health sector particularly because such individuals would not be able to contribute to the economy. On the other hand, younger persons would need to make alternative arrangements to assist their elderly parents or relatives, potentially affecting productivity. The project PervasIve Nursing And docToral Assistant (PINATA) seeks to tackle this matter through the merging of Ambient Intelligence (AmI) and semantic web technologies. PINATA utilises pervasive devices to aid doctors and nurses to focus on the patient and thus improve the quality of healthcare services. This project proves the significant importance of using wireless technology in healthcare. This paper focuses on the use of Wi-Fi and RFID in an effort to enable continuous and intelligent monitoring of patients' conditions remotely. Results show that the detection system performs well (over 80%) accuracy on fall detection and the manual readings mapped 100% with the results collected by the system over the Wi-Fi infrastructure discussed in this paper.

show abstract

Section: Technology Concernsmentioning

confidence: 99%