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Shuhei Kadoya

dblp:151/9336 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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.

Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 77% Health and well-being technologies · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › gait analysis
gait phase detection
0.212014
A precise gait phase detection based on high-frequency vibration on lower limbs · ICRA 2014

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

vibration analysis · 0.2piezo film sensing · 0.2
YearPublicationVenuePosition
2014 A precise gait phase detection based on high-frequency vibration on lower limbs
abstract
A novel gait phase detection method that can extract the timing of foot contact conditions (Heel Strike and Toe Off) by a single piezo film sensor attached on each leg is proposed. We focused on the occurrence of high-frequency vibrations (> 100 Hz) in the beginning and the end of stance phases during gait. After the features of vibration waveforms during gait were confirmed, we proposed the phase detection method. The optimal parameters were investigated to detect the phases robustly despite the walking speed and the shoe types. Experimental results showed that the detected ground contact events had high accuracy of timing. Finally, we confirmed the feasibility with the prototype wearable device in usual environments.
Shuhei Kadoya, Naohisa Nagaya, Masashi Konyo, Satoshi Tadokoro
ICRA1