TY - GEN
T1 - PSM
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
AU - Tafrishi, Seyed Amir
AU - Ravankar, Ankit A.
AU - Hirata, Yasuhisa
N1 - Funding Information:
VI. ACKNOWLEDGMENT This work was supported by JSPS KAKENHI grant number JP21K20391 and partially Japan Science and Technology Agency (JST) [Moonshot R&D Program] under Grant JPMJMS2034.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Quantifying the safety of the human body ori-entation is an important issue in human-robot interaction. Knowing the changing physical constraints on human motion can improve inspection of safe human motions and bring essential information about stability and normality of human body orientations with real-time risk assessment. Also, this information can be used in cooperative robots and monitoring systems to evaluate and interact in the environment more freely. Furthermore, the workspace area can be more deterministic with the known physical characteristics of safety. Based on this motivation, we propose a novel predictive safety model (PSM) that relies on the information of an inertial measurement unit on the human chest. The PSM encompasses a 3-Dofs spring-damper pendulum model that predicts human motion based on a safe motion dataset. The estimated safe orientation of humans is obtained by integrating a safety dataset and an elastic spring-damper model in a way that the proposed approach can realize complex motions at different safety levels. We did experiments in a real-world scenario to verify our novel proposed model. This novel approach can be used in different guidance/assistive robots and health monitoring systems to support and evaluate the human condition, particularly elders.
AB - Quantifying the safety of the human body ori-entation is an important issue in human-robot interaction. Knowing the changing physical constraints on human motion can improve inspection of safe human motions and bring essential information about stability and normality of human body orientations with real-time risk assessment. Also, this information can be used in cooperative robots and monitoring systems to evaluate and interact in the environment more freely. Furthermore, the workspace area can be more deterministic with the known physical characteristics of safety. Based on this motivation, we propose a novel predictive safety model (PSM) that relies on the information of an inertial measurement unit on the human chest. The PSM encompasses a 3-Dofs spring-damper pendulum model that predicts human motion based on a safe motion dataset. The estimated safe orientation of humans is obtained by integrating a safety dataset and an elastic spring-damper model in a way that the proposed approach can realize complex motions at different safety levels. We did experiments in a real-world scenario to verify our novel proposed model. This novel approach can be used in different guidance/assistive robots and health monitoring systems to support and evaluate the human condition, particularly elders.
UR - http://www.scopus.com/inward/record.url?scp=85146308730&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146308730&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9981274
DO - 10.1109/IROS47612.2022.9981274
M3 - Conference contribution
AN - SCOPUS:85146308730
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6657
EP - 6664
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2022 through 27 October 2022
ER -
