TY - JOUR
T1 - Evaluating upper limb functions based on motion analysis
AU - Suzuki, Kento
AU - Santos, Luciano H.O.
AU - Liu, Chang
AU - Ueshima, Hiroaki
AU - Yamamoto, Goshiro
AU - Okahashi, Sayaka
AU - Hiragi, Shusuke
AU - Sugiyama, Osamu
AU - Okamoto, Kazuya
AU - Kuroda, Tomohiro
N1 - Publisher Copyright:
© 2021, Japan Soc. of Med. Electronics and Biol. Engineering. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Conventional evaluation indices for upper limb function rehabilitation are based on the time to complete a task and the duration of movement. How-ever, these metrics are insufficient to quantify motor performance attributes, such as smoothness of movement and presence of compensatory movements. This study aims to introduce a quantitative index for the evaluation of upper limb functions based on rehabilitation exercises performed by patients. For our initial evaluation, we chose the Grasp movement performed in ARAT (Action Research Arm Test), a conventional evaluation method for upper limb functions in patients with post-stroke syndrome. We use RGB videos of therapist imitating a patient with posterior syndrome. Machine learning techniques were employed to esti-mate posture and extract skeletal information, using time-series analysis, an evaluation model was created to quantify the compensatory movements of post-stroke syndrome and healthy patients.
AB - Conventional evaluation indices for upper limb function rehabilitation are based on the time to complete a task and the duration of movement. How-ever, these metrics are insufficient to quantify motor performance attributes, such as smoothness of movement and presence of compensatory movements. This study aims to introduce a quantitative index for the evaluation of upper limb functions based on rehabilitation exercises performed by patients. For our initial evaluation, we chose the Grasp movement performed in ARAT (Action Research Arm Test), a conventional evaluation method for upper limb functions in patients with post-stroke syndrome. We use RGB videos of therapist imitating a patient with posterior syndrome. Machine learning techniques were employed to esti-mate posture and extract skeletal information, using time-series analysis, an evaluation model was created to quantify the compensatory movements of post-stroke syndrome and healthy patients.
KW - ARAT
KW - Computer Vision
KW - Rehabilitation
KW - Skeletal Information
UR - http://www.scopus.com/inward/record.url?scp=85135127386&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135127386&partnerID=8YFLogxK
U2 - 10.11239/jsmbe.Annual59.805
DO - 10.11239/jsmbe.Annual59.805
M3 - Article
AN - SCOPUS:85135127386
SN - 1881-4379
VL - Annual 59
SP - 805
EP - 807
JO - Transactions of Japanese Society for Medical and Biological Engineering
JF - Transactions of Japanese Society for Medical and Biological Engineering
IS - Proc
ER -