Becoming InterActive for Life: Mobilizing Relational Knowledge for Physical Educators

The overarching purpose of the InterActive for Life (IA4L) project is to mobilize relational knowledge of partnered movement practices for physical education practitioners. Through a participatory, motion-sensing phenomenological methodology, relational knowledge gleaned from world class experts in salsa dance, equestrian arts, push hands Tai Chi and acroyoga, and analyzed through the Function2Flow conceptual model, was shared with Physical Education Teacher Education (PETE) students. They, in turn, made sense of the ways these experts cultivate relational connections through a process of designing interactive games suitable for physical education curricula. The kinetic, kinesthetic, affective and energetic dynamics of these games were then shared through professional development workshops, mentoring, and open-access resources. Each phase of the IA4L project invites us to depart from the predominance of individualistic ways of conceiving and teaching movement and instead explore what it means to be attuned to the pulse of life as we break away from tendencies to objectify movement as something our bodies do or that is done to them. Consideration is given to the ways in which meaningful relational connections are formed in and through movement and how this learning prioritizes the InterActive Functions, Forms, Feelings and Flows of moving purposefully, playfully and expressively with others. In so doing, what this research offers is an understanding of how knowledge of an essentially motion-sensitive kind, which can breathe life into physical education curricula, can be actively and interactively mobilized.

Analysis of a Dynamic Calibration Target for Through-Wall and Through-Rubble Motion Sensing Doppler Radar

Through-wall and through-barrier motion-sensing systems are becoming increasingly important tools to locate humans concealed behind barriers and under rubble. The sensing performance of these systems is best determined with appropriately designed calibration targets, which are ones that can emulate human motion. The effectiveness of various dynamic calibration targets that emulate human respiration, heart rate, and other body motions were analyzed. Moreover, these targets should be amenable to field deployment and not manifest angular or orientation dependences. The three targets examined in this thesis possess spherical polyhedral geometries. Spherical geometries were selected due to their isotropic radar cross-sectional characteristics, which provide for consistent radar returns independent of the orientation of the radar transceiver relative to the test target. The aspect-independence of a sphere allows for more accurate and repeatable calibration of a radar than using a nonspherical calibration artifact. In addition, the radar cross section (RCS) for scattering spheres is well known and can be calculated using far-field approximations. For Doppler radar testing, it is desired to apply these calibration advantages to a dynamically expanding-and-contracting sphere-like device that can emulate motions of the human body. Monostatic RCS simulations at 3.6 GHz were documented for each geometry. The results provide a visual way of representing the effectiveness of each design as a dynamic calibration target for human detection purposes.

Active Magnetoelectric Motion Sensing: Examining Performance Metrics with an Experimental Setup

Magnetoelectric (ME) sensors with a form factor of a few millimeters offer a comparatively low magnetic noise density of a few pT/Hz in a narrow frequency band near the first bending mode. While a high resonance frequency (kHz range) and limited bandwidth present a challenge to biomagnetic measurements, they can potentially be exploited in indirect sensing of non-magnetic quantities, where artificial magnetic sources are applicable. In this paper, we present the novel concept of an active magnetic motion sensing system optimized for ME sensors. Based on the signal chain, we investigated and quantified key drivers of the signal-to-noise ratio (SNR), which is closely related to sensor noise and bandwidth. These considerations were demonstrated by corresponding measurements in a simplified one-dimensional motion setup. Accordingly, we introduced a customized filter structure that enables a flexible bandwidth selection as well as a frequency-based separation of multiple artificial sources. Both design goals target the prospective application of ME sensors in medical movement analysis, where a multitude of distributed sensors and sources might be applied.