Falls Risk Assessment for Hospitalised Older Adults: A Combination of Motion Data and Vital Signs
| aut.relation.volume | On line | en_NZ |
| aut.researcher | Gholamhosseini, Hamid | |
| dc.contributor.author | Baig | en_NZ |
| dc.contributor.author | Gholamhosseini, H | en_NZ |
| dc.contributor.author | Connolly, MJ | en_NZ |
| dc.date.accessioned | 2016-09-19T02:50:58Z | |
| dc.date.available | 2016-09-19T02:50:58Z | |
| dc.date.copyright | 2016-01-19 | en_NZ |
| dc.date.issued | 2016-01-19 | en_NZ |
| dc.description.abstract | Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Currently hospital falls are a major healthcare concern worldwide because of the ageing population. Current observational data and vital signs give the critical information related to the patient’s physiology, and motion data provide an additional tool in falls risk assessment. These data combined with the patient’s medical history potentially may give the interpretation model high information accessibility to predict falls risk. This study aims to develop a robust falls risk assessment system, in order to avoid falls and its related long-term disabilities in hospitals especially among older adults. The proposed system employs real-time vital signs, motion data, falls history and other clinical information. The falls risk assessment model has been tested and evaluated with 30 patients. The results of the proposed system have been compared with and evaluated against the hospital’s falls scoring scale. | en_NZ |
| dc.identifier.citation | Aging Clinical and Experimental Research. doi:10.1007/s40520-015-0510-5 | en_NZ |
| dc.identifier.doi | 10.1007/s40520-015-0510-5 | en_NZ |
| dc.identifier.issn | 1720-8319 | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/10035 | |
| dc.publisher | Springer | |
| dc.relation.uri | http://dx.doi.org/10.1007/s40520-015-0510-5 | en_NZ |
| dc.rights | An author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation). | |
| dc.rights.accessrights | OpenAccess | en_NZ |
| dc.subject | Falls assessment system; Automated falls scoring; Falls in older adults; Older adults falls; Hospitalised falls prevention system; Smart falls assessment system | |
| dc.title | Falls Risk Assessment for Hospitalised Older Adults: A Combination of Motion Data and Vital Signs | en_NZ |
| dc.type | Journal Article | |
| pubs.elements-id | 196661 | |
| pubs.organisational-data | /AUT | |
| pubs.organisational-data | /AUT/Design & Creative Technologies | |
| pubs.organisational-data | /AUT/Design & Creative Technologies/School of Engineering |