Characterization of inertial measurement unit placement on the human body upon repeated donnings

Abstract

Given projections of an increase in the number of consumers wearing sensors in the upcoming years coupled with NASA's technological roadmap for future human space exploration, there is a need to understand and appropriately incorporate the variability of humans during the use of sensors that extract human activity and diagnostics. Accurate estimations of variability in multiple donnings of sensor suites may aid algorithm development for wearable motion capture systems that make use of Inertial Measurement Units (IMUs). The accuracy of any algorithm incorporating these sensors is limited by the accuracy of the sensor to segment calibration. In this study, 22 participants self-placed IMUs on three locations and performed six prescribed motions during each of these five donnings. Placement of the IMU was quantified as distance, orientation, and rotation. For orientation of the sensors at the beginning of the prescribed motions, the bicep orientation mean was less than the forearm, which was less than the chest. No difference in sensor rotation was found between the bicep and forearm, but both locations differed from the chest location. It was found that even with a guide to assist with the starting and ending positions of a motion, the placement of the sensor on the human-body varied at the beginning and end of a motion. This study found no consistent effect of donning on placement, but did find an effect of motion on the measures. Since the placement measures did vary through a motion, the underlying assumptions of a rigid body model used by motion capture algorithms might not be appropriate. Motion capture algorithms need to be careful when using these rigid body assumptions and account for the changes in position and Euler angles due to natural human variability and calibration during multiple donnings of IMUs by non-experts. This study will aid in the development of quick don and doff sensor suites that can be reliably used by a non-expert for real-time decision making.