TY - GEN
T1 - Deep-Learning Enabled Assessment of Neurocognitive Performance in Object Following in Mixed Reality
AU - Sharma, Ansh
AU - Nallamotu, Keerthana
AU - Umashankar, Mukhilshankar
AU - Wang, Shenlong
AU - Kim, Inki
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022
Y1 - 2022
N2 - The objective of this article is to develop a deep learning model to construct a comprehensive, machine-learnable representation of human performance that spans visual, cognitive, and motor-control abilities associated with an object-following task in mixed reality (MR). Compared to direct observations by trained clinical staffs, which is the current standard for clinical diagnosis, a deep learning approach is expected to detect subtle signs of neurocognitive abilities and/or impairment. If successful, the resultant representation will bring a new opportunity to be shared and communicated with humans, a first step to collaborative workflows between clinical staffs and artificial intelligence (AI) specialists for diagnosis.
AB - The objective of this article is to develop a deep learning model to construct a comprehensive, machine-learnable representation of human performance that spans visual, cognitive, and motor-control abilities associated with an object-following task in mixed reality (MR). Compared to direct observations by trained clinical staffs, which is the current standard for clinical diagnosis, a deep learning approach is expected to detect subtle signs of neurocognitive abilities and/or impairment. If successful, the resultant representation will bring a new opportunity to be shared and communicated with humans, a first step to collaborative workflows between clinical staffs and artificial intelligence (AI) specialists for diagnosis.
KW - Deep Learning
KW - Human Performance Modeling
KW - Mixed Reality
KW - Spatial-Temporal Transformer Network
UR - http://www.scopus.com/inward/record.url?scp=85146367376&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146367376&partnerID=8YFLogxK
U2 - 10.1145/3551455.3566163
DO - 10.1145/3551455.3566163
M3 - Conference contribution
AN - SCOPUS:85146367376
T3 - Proceedings - 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022
SP - 203
EP - 207
BT - Proceedings - 2022 IEEE/ACM International Conference on Connected Health
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022
Y2 - 17 November 2022 through 19 November 2022
ER -