Evaluation of Human Movement Smoothness and influence of signal processing techniques

Movement smoothness is a pivotal parameter for evaluating the quality of human motion, reflecting its fluidity and continuity. This parameter holds significant importance in fields such as industrial ergonomics, medical rehabilitation and sports performance optimization. Metrics such as Spectral Arc-Length (SPARC) and Log of Dimensionless Jerk (LDLJ) are commonly used to quantify smoothness, but the impact of signal segmentation on these measurements remains underexplored. This study investigates how segmenting motion signals influences smoothness assessments in different movement tasks.Objective:The primary aim of this research is to assess the effect of signal segmentation on movement smoothness, specifically comparing smoothness values derived from whole signal analysis versus segmented signal analysis. The study also examines how these effects differ across various movement tasks, such as walking and upper limb motion.Methods:Both synthetic and real-world motion signals were analyzed. Synthetic signals, modeled as sinusoidal and Gaussian profiles, simulate idealized movement behaviors, allowing for controlled examination of the segmentation effect. Real-world motion data were collected using motion and force sensors, representing natural human movements. SPARC and LDLJ metrics were calculated in MATLAB® to evaluate the smoothness of each signal, comparing the results obtained from whole and segmented signals.Results:The analysis reveals that signal segmentation significantly affects smoothness measurements. In periodic movements, segmenting the signal into individual steps leads to different smoothness values compared to analyzing the entire movement as a continuous cycle. These findings underscore that smoothness is context-dependent and influenced by the segmentation approach.Conclusions:This study demonstrates that movement smoothness is not only an inherent property of the movement itself, but it is also a measure influenced by signal processing techniques. The results highlight the importance of standardized segmentation methods for reliable smoothness evaluations. The study provides practical guidelines for using SPARC and LDLJ metrics in different contexts and suggests future research directions to refine smoothness assessment methodologies.