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Masataka Nishino

dblp:44/7735 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › biomedical signal processing › physiological signal analysis
electromyography
0.112009
Evaluation of frequency band technique in estimating muscle fatigue during dynamic contraction task · ICRA 2009
Medical and health informatics › biomedical signal processing
physiological signal analysis
0.112009
Evaluation of frequency band technique in estimating muscle fatigue during dynamic contraction task · ICRA 2009

Methods — techniques the papers use, named apart from their topics

handgrip endurance task · 0.1frequency band analysis · 0.1
YearPublicationVenuePosition
2009 Evaluation of frequency band technique in estimating muscle fatigue during dynamic contraction task
abstract
Long-time exposure to repetitive or monotonous work is associated with increased risk for musculoskeletal disorders that are due to muscle fatigue. Previously, researchers reported that muscle fatigue can be estimated using a low-frequency band of an SEMG signal. However, these studies were performed exclusively during static contraction tasks. The objective of the present study was to evaluate and determine the frequency range for a low-frequency band. In addition, the performance during dynamic contraction tasks was analyzed. A group of healthy university students (8 males) was recruited, and endurance handgrip tasks were conducted. SEMG signals were captured from the forearm muscle. The frequency range for the low-frequency band was redefined as 5 - 40 Hz. The results from a dynamic contraction task indicated that a low-frequency band is a reliable method for indexing muscle fatigue from SEMG signals.
Yewguan Soo, Masataka Nishino, Masao Sugi, Hiroshi Yokoi, Tamio Arai, Ryu Kato, Tatsuhiro Nakamura, Jun Ota 0001
ICRA2