Shun Ishii

Mr. Shun Ishii

Aoyama Gakuin University
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2014 in Journal of Physical Therapy Science
Verified
Journal Article
Open access
JSJunya Sakamoto
Junya Sakamoto
YMYosuke Morimoto
Yosuke Morimoto
Shun IshiiShun Ishii
Shun Ishii
JNJiro Nakano
Jiro Nakano
YMYoshitaka Manabe
Yoshitaka Manabe
TTToshiyuki Tsurumoto
Toshiyuki Tsurumoto
[Purpose] The aim of this study was to examine the incidence and patterns of referred pain in patients with hip disease, as well as the nerve distribution in the hip and knee joints of 2 cadavers. [Subjects and Methods] A total of 113 patients with hip joint disease were included in the investigation. The incidence of regional pain and referred pain patterns were evaluated before and after arthroplasty. Two cadavers were macroscopically observed to verify the nerve innervation of the hip and knee joints. [Results] Anterior knee pain was observed preoperatively in 13.3% (in resting) and 33.6% (in motion) of the patients, which was comparable with the incidence of greater trochanter pain. In addition, the preoperative incidence rates of knee pain in resting and motion markedly decreased postoperatively. Of note is the remarkable incidence of pain radiating to the ventral lower limb. An anteromedial innervation was determined in the cadavers by the articular branches of the obturator and femoral nerve, which supply small branches to the knee joints. [Conclusion] Our results suggest that the distribution of the incidence of pain among the patients with hip disease is diverse owing to the sensory distribution of the femoral and obturator nerves.

25 December 2020 in Sensors
Verified
Journal Article
Open access
PDF file
Shun IshiiShun Ishii
Shun Ishii
AYAnna Yokokubo
Anna Yokokubo
MLMika Luimula
Mika Luimula
GLGuillaume Lopez
Guillaume Lopez
Wearable devices are currently popular for fitness tracking. However, these general usage devices only can track limited and prespecified exercises. In our previous work, we introduced ExerSense that segments, classifies, and counts multiple physical exercises in real-time based on a correlation method. It also can track user-specified exercises collected only one motion in advance. This paper is the extension of that work. We collected acceleration data for five types of regular exercises by four different wearable devices. To find the best accurate device and its position for multiple exercise recognition, we conducted 50 times random validations. Our result shows the robustness of ExerSense, working well with various devices. Among the four general usage devices, the chest-mounted sensor is the best for our target exercises, and the upper-arm-mounted smartphone is a close second. The wrist-mounted smartwatch is third, and the worst one is the ear-mounted sensor.

24 December 2020
Verified
Book Chapter
Closed access
PDF file
Shun IshiiShun Ishii
Shun Ishii
KNKizito Nkurikiyeyezu
Kizito Nkurikiyeyezu
MLMika Luimula
Mika Luimula
AYAnna Yokokubo
Anna Yokokubo
GLGuillaume Lopez
Guillaume Lopez

3 July 2019 in Supportive Care in Cancer
Verified
Journal Article
Closed access
PDF file
TFTakuya Fukushima
Takuya Fukushima
JNJiro Nakano
Jiro Nakano
Shun IshiiShun Ishii
Shun Ishii
ANAyumi Natsuzako
Ayumi Natsuzako
SSShuntaro Sato
Shuntaro Sato
MOMinoru Okita
Minoru Okita

9 September 2019
Verified
Conference Paper
Open access
PDF file
TKTakumi Kondo
Takumi Kondo
HKHaruka Kamachi
Haruka Kamachi
Shun IshiiShun Ishii
Shun Ishii
AYAnna Yokokubo
Anna Yokokubo
GLGuillaume Lopez
Guillaume Lopez

4 May 2022
Verified
Book Chapter
Closed access
PDF file
YKYuki Kondo
Yuki Kondo
Shun IshiiShun Ishii
Shun Ishii
HAHikari Aoyagi
Hikari Aoyagi
THTahera Hossain
Tahera Hossain
AYAnna Yokokubo
Anna Yokokubo
GLGuillaume Lopez
Guillaume Lopez