Recent advancements in the field of smart wearable sensors provide the opportunity of continuous analysis of user's movements, which enables the assessment of clinical conditions like frailty. This study explores the use of Continuous Wavelet Transform in combination with sensor-derived gait parameters for frailty status assessment. A total of 34 volunteers aged 70+ were initially screened by geriatricians for the presence of frailty according to Fried's criteria. After screening, participants were asked to perform a 60 m walk test at preferred pace, while wearing an accelerometer on the wrist. A gait detection technique was applied to the sensor-derived signal, in order to identify segments made of four gait cycles. Continuous Wavelet Transform was applied to obtain time-frequency domain representations, which were subsequently used in a band-based feature extraction phase. Here, the most significant band-based features for frailty status assessment were identified by means of ANOVA and statistical t-test. Finally, a Random Forest for each frequency band was trained and tested for classifying subjects as robust or nonrobust (i.e., pre-frail or frail). Results from both the statistical analysis and machine learning show that features extracted from [1.5, 2.5]Hz frequency band can provide valuable information for recognizing frailty in older adults. This information may help achieve continuous assessment of frailty in older adults with a wrist-worn device

Wavelet-based analysis of gait for automated frailty assessment with a wrist-worn device

D Minici;M Avvenuti
2021-01-01

Abstract

Recent advancements in the field of smart wearable sensors provide the opportunity of continuous analysis of user's movements, which enables the assessment of clinical conditions like frailty. This study explores the use of Continuous Wavelet Transform in combination with sensor-derived gait parameters for frailty status assessment. A total of 34 volunteers aged 70+ were initially screened by geriatricians for the presence of frailty according to Fried's criteria. After screening, participants were asked to perform a 60 m walk test at preferred pace, while wearing an accelerometer on the wrist. A gait detection technique was applied to the sensor-derived signal, in order to identify segments made of four gait cycles. Continuous Wavelet Transform was applied to obtain time-frequency domain representations, which were subsequently used in a band-based feature extraction phase. Here, the most significant band-based features for frailty status assessment were identified by means of ANOVA and statistical t-test. Finally, a Random Forest for each frequency band was trained and tested for classifying subjects as robust or nonrobust (i.e., pre-frail or frail). Results from both the statistical analysis and machine learning show that features extracted from [1.5, 2.5]Hz frequency band can provide valuable information for recognizing frailty in older adults. This information may help achieve continuous assessment of frailty in older adults with a wrist-worn device
2021
Minici, D; Cola, G; Giordano, A; Antoci, S; Girardi, E; Di Bari, M; Avvenuti, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1109271
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