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A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals

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Abstract—As an essential branch of context awareness, activity awareness, especially daily activity monitoring and fall detection, is important to healthcare for the elderly and patients with chronic diseases. In this paper, a framework for activity awareness using surface electromyography and accelerometer (ACC) signals is proposed. First, histogram negative entropy was employed to determine the start- and end-points of static and dynamic active segments. Then, the angle of each ACC axis was calculated to indicate body postures, which assisted with sorting dynamic activities
into two categories: dynamic gait activities and dynamic transition ones, by judging whether the pre- and post-postures are both standing. Next, the dynamic gait activities were identified by the double-stream hidden Markov models. Besides, the dynamic transition activities were distinguished into normal transition activities and falls by resultant ACC amplitude. Finally, a continuous daily activity monitoring and fall detection scheme was performed with the recognition accuracy over 98%, demonstrating the excellent fall detection performance and the great feasibility of the proposed method in daily activities awareness. Index Terms—Activity awareness, entropy, fall detection, surface electromyography (SEMG).

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Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 1, JANUARY 2013 p. 38-45

Bahasa

English

ISBN/ISSN

DOI: 10.1109/TITB.20

Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNOLOGI KEDOKTERAN

Info Detail Spesifik

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Pernyataan Tanggungjawab

Nurasmi

Versi lain/terkait

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