Quantification of the development of trunk control in healthy infants using inertial measurement units

Abstract

I would like to start by thanking my supervisors, dr. Daniel Lemus Perez, dr. Herwin Horemans, dr. Manon Kok and prof. dr.-ing Heike Vallery. I have enjoyed working with all off you and thoroughly appreciate your guidance throughout this process. You have helped me to perform better and I could not have come this far without your input and feedback. I would also like to thank all clinicians that have provided input during this process, but I would specifically like to thank dr. Robert Pangalila and AIOS Jolien Deferme for their continued involvement.Of course, there have also been many non-scientific contributions that brought me here. First and foremost, I would like to thank my family, Mees, and Maaike (my unofficial family), without whom I would not be where I am today. They have always believed in me and my abilities and have provided me with unconditional support. I also owe a huge thanks to my roommates, both in Delft and in The Hague, for being my personal cheerleaders, putting up with me when I was stressed and being there for me through it all. Of course, I cannot leave out my study buddies and friends. Working together and hanging out together has made the last years so much more fun, whether we were struggling together in the masterraum, enjoying extravagant lunches at the MBP office or even working together through Zoom.

Comparison of Gait Smoothness Metrics in Healthy Elderly and Young People

“…In the medical context, physical impairments have been assessed through smoothness evaluation on people after stroke [6][7][8], Parkinson's disease individuals [9,10] and cerebellar-ataxia-affected subjects [11]. Furthermore, it has been exploited in the sports field to improve the coordination classification protocol of para-swimmer athletes [12] and, more generally, to assess motor skills in activities of daily living (ADLs) in both open chain [13,14] and closed chain [15].…”

“…Smoothness metrics should possess dimensionless attributes, ensuring independence from movement amplitude and duration while demonstrating a consistent response within the physiological range [13]. Several metrics have been proposed to evaluate gait smoothness, including jerk-based measures such as jerk peak inflections [8], jerk root mean square (RMS) [14], log dimensionless jerk (LDLJ) [11,15,16] or the estimation of movement's frequency spectrum like the harmonic ratio (HR) [2,3,11,17] and spectral arc length (SPARC) [9,10,18,19]. The latter, proposed by Balasubramanian et al [13], is claimed as reliable, independent from duration and amplitude and not influenced by noise and hence able to outstand other existing metrics that were not robust enough.…”

“…Several studies evaluated smoothness in balance, gait and stability, mainly in the clinical context. Blok et al quantified through the linear jerk root mean square (RMS) metric the smoothness for assessing the trunk balance control in healthy infants [14], while linear jerk inflection counting was implemented by Chandler et al to determine any differences regarding motion fluidity in sit-to-stand, sit-to-walk and walking in people who had suffered a stroke [8]. Individuals in two different post-stroke phases were also evaluated in terms of body center of mass (CoM) smoothness by comparing LDLJ and SPARC on CoM velocity, finding that LDLJ is more responsive and reliable than SPARC in differentiating gait of both classes [15].…”