Document Type
Conference Proceeding
Publication Date
2010
Abstract
Patients with end stage renal diseases (ESRD) on hemodialysis (HD) have high morbidity and mortality due to multiple causes, one of which is dramatically higher fall rates than the general population. The mobility mechanisms that contribute to falls in this population must be understood if adequate interventions for fall prevention are to be achieved. This study utilizes emerging noninvasive, portable gait, posture, strength, and stability assessment technologies to extract various mobility parameters that research has shown to be predictive of fall risk in the general population. As part of an ongoing human subjects study, mobility measures such as postural and locomotion profiles were obtained from five (5) ESRD patients undergoing HD treatments. To assess the effects of post-HD-fatigue on fall risk, both the pre- and post-HD measurements were obtained. Additionally, the effects of inter-HD periods (two days vs. three days) were investigated using the non-invasive, wireless, body-worn motion capture technology and novel signal processing algorithms. The results indicated that HD treatment influenced strength and mobility (i.e., weaker and slower after the dialysis, increasing the susceptibility to falls while returning home) and interdialysis period influenced pre-HD profiles (increasing the susceptibility to falls before they come in for a HD treatment). Methodology for early detection of increased fall risk – before a fall event occurs – using the portable mobility assessment technology for out-patient monitoring is further explored, including targeting interventions to identified individuals for fall prevention.
Recommended Citation
Lockhart TE, Barth AT, Zhang X, Songra R, Abdel-Rahman E, Lach J. Portable, non-invasive fall risk assessment in end stage renal disease patients on hemodialysis. ACM Trans Comput Hum Interact. 2010:84-93. doi:10.1145/1921081.1921092
Copyright
Association for Computing Machinery
Included in
Endocrine System Diseases Commons, Movement and Mind-Body Therapies Commons, Musculoskeletal System Commons, Physical Therapy Commons
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in ACM Transactions on Computer-Human Interaction in 2010 following peer review. The definitive publisher-authenticated version is available online at DOI:10.1145/1921081.1921092.