TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Eigen Posture Based Fall Risk Assessment System Using Kinect

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
KustantajaIEEE
Sivut1-4
Sivumäärä4
Vuosikerta2018-July
ISBN (elektroninen)9781538636466
DOI - pysyväislinkit
TilaJulkaistu - 26 lokakuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaAnnual International Conference of the IEEE Engineering in Medicine and Biology Society - Honolulu, Yhdysvallat
Kesto: 18 heinäkuuta 201821 heinäkuuta 2018

Julkaisusarja

Nimi
ISSN (elektroninen)1558-4615

Conference

ConferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
MaaYhdysvallat
KaupunkiHonolulu
Ajanjakso18/07/1821/07/18

Tiivistelmä

Postural Instability (PI) is a major reason for fall in geriatric population as well as for people with diseases or disorders like Parkinson's, stroke etc. Conventional stability indicators like Berg Balance Scale (BBS) require clinical settings with skilled personnel's interventions to detect PI and finally classify the person into low, mid or high fall risk categories. Moreover these tests demand a number of functional tasks to be performed by the patient for proper assessment. In this paper a machine learning based approach is developed to determine fall risk with minimal human intervention using only Single Limb Stance exercise. The analysis is done based on the spatiotemporal dynamics of skeleton joint positions obtained from Kinect sensor. A novel posture modeling method has been applied for feature extraction along with some traditional time domain and metadata features to successfully predict the fall risk category. The proposed unobstrusive, affordable system is tested over 224 subjects and is able to achieve 75% mean accuracy on the geriatric and patient population.