TY - JOUR
T1 - Full Body-Worn Textile-Integrated Nanomaterials and Soft Electronics for Real-Time Continuous Motion Recognition Using Cloud Computing
AU - Kwon, Kangkyu
AU - Lee, Yoon Jae
AU - Chung, Suyeong
AU - Lee, Jimin
AU - Na, Yewon
AU - Kwon, Youngjin
AU - Shin, Beomjune
AU - Bateman, Allison
AU - Lee, Jaeho
AU - Guess, Matthew
AU - Sohn, Jung Woo
AU - Lee, Jinwoo
AU - Yeo, Woon Hong
N1 - Publisher Copyright:
© 2025 The Authors. Published by American Chemical Society.
PY - 2025/2/5
Y1 - 2025/2/5
N2 - Recognizing human body motions opens possibilities for real-time observation of users’ daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system’s performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.
AB - Recognizing human body motions opens possibilities for real-time observation of users’ daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system’s performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.
KW - cloud computing
KW - deep learning
KW - motion recognition
KW - textile-integrated sensors
KW - wearable electronics
UR - https://www.scopus.com/pages/publications/85215953241
U2 - 10.1021/acsami.4c17369
DO - 10.1021/acsami.4c17369
M3 - Article
C2 - 39851169
AN - SCOPUS:85215953241
SN - 1944-8244
VL - 17
SP - 7977
EP - 7988
JO - ACS Applied Materials and Interfaces
JF - ACS Applied Materials and Interfaces
IS - 5
ER -