Sensor-based Fall Risk Assessment – an Expert ‘to go’
| Autor: | Matthias Gietzelt, Anja Rehwald, G. Nemitz, Michael Marschollek, Reinhold Haux, Klaus-Hendrik Wolf, H. Meyer zu Schwabedissen |
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| Rok vydání: | 2011 |
| Předmět: |
Male
Activities of daily living Waist 020205 medical informatics Movement Acceleration Spec# Poison control Health Informatics 02 engineering and technology Logistic regression Risk Assessment Sensitivity and Specificity 03 medical and health sciences 0302 clinical medicine Health Information Management Assisted Living Facilities Predictive Value of Tests Positive predicative value Activities of Daily Living Statistics 0202 electrical engineering electronic engineering information engineering Humans Medicine Prospective Studies 030212 general & internal medicine Geriatric Assessment Simulation Aged computer.programming_language Aged 80 and over Advanced and Specialized Nursing Inpatients business.industry Area under the curve Biomechanical Phenomena Brier score Area Under Curve Accidental Falls Female business computer Algorithms |
| Zdroj: | Methods of Information in Medicine. 50:420-426 |
| ISSN: | 2511-705X 0026-1270 |
| Popis: | SummaryBackground: Falls are a predominant problem in our aging society, often leading to severe somatic and psychological consequences, and having an incidence of about 30% in the group of persons aged 65 years or above. In order to identify persons at risk, many assessment tools and tests have been developed, but most of these have to be conducted in a supervised setting and are dependent on an expert rater.Objectives: The overall aim of our research work is to develop an objective and unobtrusive method to determine individual fall risk based on the use of motion sensor data. The aims of our work for this paper are to derive a fall risk model based on sensor data that may potentially be measured during typical activities of daily life (aim #1), and to evaluate the resulting model with data from a one-year follow-up study (aim #2).Methods: A sample of n = 119 geriatric inpatients wore an accelerometer on the waist during a Timed ‘Up & Go’ test and a 20 m walk. Fifty patients were included in a one-year follow-up study, assessing fall events and scoring average physical activity at home in telephone interviews. The sensor data were processed to extract gait and dynamic balance parameters, from which four fall risk models – two classification trees and two logistic regression models – were computed: models CT#1 and SL#1 using accelerometer data only, models CT#2 and SL#2 including the physical activity score. The risk models were evaluated in a ten-times tenfold cross-validation procedure, calculating sensitivity (SENS), speci ficity (SPEC), positive and negative predictive values (PPV, NPV), classification accuracy, area under the curve (AUC) and the Brier score.Results: Both classification trees show a fair to good performance (models CT#1/ CT#2): SENS 74% / 58%, SPEC 96% / 82%, PPV 92% / 74%, NPV 77%/82%, accuracy 80% / 78%, AUC 0.83 / 0.87 and Brier scores 0.14 / 0.14. The logistic regression models (SL#1/ SL#2) perform worse: SENS 42% / 58%, SPEC 82% / 78%, PPV 62% / 65%, NPV 67% / 72%, accuracy 65% /70%, AUC 0.65 / 0.72 and Brier scores 0.23 / 0.21.Conclusions: Our results suggest that accelerometer data may be used to predict falls in an unsupervised setting. Furthermore, the parameters used for prediction are measurable with an unobtrusive sensor device during normal activities of daily living. These promising results have to be validated in a larger, long-term prospective trial. |
| Databáze: | OpenAIRE |
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