Authors:
Jose Albites-Sanabria
1
;
2
;
Pierpaolo Palumbo
1
;
Stefania Bandinelli
3
;
Luca Palmerini
1
;
4
and
Lorenzo Chiari
1
;
4
Affiliations:
1
Department of Electrical, Electronic, and Information Engineering – DEI, University of Bologna, Italy
;
2
Institute of Advanced Studies, University of Bologna, Italy
;
3
Azienda Sanitaria Toscana Centro, Firenze, Piero Palagi Hospital, Firenze, Italy
;
4
Health Sciences and Technologies-Interdepartmental Center for Industrial Research, University of Bologna, Italy
Keyword(s):
Turning, Wearable Sensors, Continuous Monitoring, Older Adults, Falls.
Abstract:
Turning deficits have been linked to aging and movement disorders and are a common cause of falls and fractures. Despite previous works on the automatic identification of turns and on its relation to fall risk, different algorithms for turn identification have been used, but their agreement and differences have not been investigated. In this study, we compared the two most-used turn-validated algorithms (El-Gohary and Pham) using a dataset comprising real-world data from 171 community-dwelling older adults monitored for one week with a single wearable sensor. The quantity and quality of turn parameters were calculated and used as predictors of future falls. After the analysis, the El-Gohary and Pham algorithms identified 1,063,810 and 942,845 turns, respectively. The agreement of the algorithms showed a very high to moderate correlation for all turn parameters. We found that prospective fallers take longer to perform a turn, and their movements are less smooth when compared to non-fa
llers. A fall risk assessment model built only on turn parameters showed reasonable performance for both algorithms (AUC = 0.6). Our results show that differences between turn parameters in the algorithms, when averaged at the single-subject level, are less of a concern when looking for associations with prospective falls.
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