Authors:
Farah Othmen
1
;
2
;
3
;
André Eugenio Lazzaretti
4
;
Mouna Baklouti
1
;
Marwa Jmal
3
and
Mohamed Abid
1
Affiliations:
1
CES Lab, National School of Engineers of Sfax, University of Sfax, Tunisia
;
2
Ecole Polytechnique de Tunisie, Université de Carthage, La Marsa, Tunisia
;
3
Telnet Innovation Labs, Telnet Holding, Ariana, Tunisia
;
4
CPGEI, Universidade Tecnológica Federal do Paraná, Curitiba, Brazil
Keyword(s):
Additive White Gaussian Noise, Machine Learning, Supervised Dictionary Learning, Wearable Fall detection.
Abstract:
Elderly falls are becoming a more crucial and major health problem relatively with the significant growth of the involved population over the years. Wrist-based fall detection solution gained much interest for its comfortable and indoor-outdoor use, yet, a very moving and unstable location to the Inertial measurement unit. Indeed, acquired data might be exposed to random noises challenging the classifier’s reliability to spot falls among other daily activities. In this paper, we address the limits faced by Machine Learning models regarding noisy and overlapped data by proposing a study of the Supervised Dictionary Learning (SDL) technique for on-wrist fall detection. Following the same prior work experimental protocol, the five most popular SDL models were evaluated and compared in performance with two benchmark Machine learning models. The evaluation setup follows two main experiments; processing clean data and casting different additive white Gaussian noise (AWGN). A distinguishabl
e achievement was obtained by the SDL algorithms, of which the Sparse Representation-based Classifier (SRC) algorithm surpass other models especially using noisy data. The latter maintained almost 98% for 0db AWGN versus 96.4% for KNN.
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