e-journal

Triaxial Accelerometer-Based Fall Detection Method Using a Self-Constructing Cascade-AdaBoost-SVM Classifier

---

Abstract—In this paper, we propose a cascade-AdaBoostsupport vector machine (SVM) classifier to complete the triaxial accelerometer-basedfalldetectionmethod.Themethodusestheacceleration signals of daily activities of volunteers from a database and calculates feature values. By taking the feature values of a sliding window as an input vector, the cascade-AdaBoost-SVM algorithm can self-construct based on training vectors, and the AdaBoost algorithm of each layer can automatically select several optimal weak classifiers to form a strong classifier, which accelerates effectively the processing speed in the testing phase, requiring only selected features rather than all features. In addition, the algorithm can automatically determine whether to replace the AdaBoost classifier by support vector machine. We used the UCI database for the experiment, in which the triaxial accelerometers are, respectively, worn around the left and right ankles, and on the chest as well as the waist. The results are compared to those of the neural network, support vector machine, and the cascadeAdaBoostclassifier.Theexperimentalresultsshowthatthetriaxial accelerometersaroundthechestandwaistproduceoptimalresults, andourproposedmethodhasthehighestaccuracyrateanddetection rate as well as the lowest false alarm rate.

Index Terms—Feature selection, signal magnitude area (SMA), signal magnitude vector (SMV), sliding window, weak classifier.

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

-

No. Panggil

-

Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013 p. 411-419

Bahasa

English

ISBN/ISSN

2168-2194

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TEKNOLOGI KEDOKTERAN

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Wen-Chang Cheng and Ding-Mao Jhan

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar