Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes...

Area of Science:

• Biomechanics
• Human Movement Science
• Kinesiology

Background:

• Identifying fatigue-induced movement strategies is often hindered by significant between-subject variability in joint angle data.
• Traditional and computationally intensive methods struggle to provide efficient and holistic assessments of kinematic changes due to fatigue.
• Symbolic Motion Structure Representation (SMSR) offers a novel approach using string descriptors of temporal joint angle trajectories.

Purpose of the Study:

• To evaluate the efficacy of the SMSR algorithm in detecting changes in upper extremity kinematic data during a fatiguing repetitive task.
• To assess SMSR's ability to identify adaptive movement strategies adopted by individuals as they experience muscle fatigue.
• To establish SMSR as a viable and sensitive method for kinematic fatigue identification.

Main Methods:

• Twenty-eight participants performed a seated repetitive upper extremity task until fatigue.
• Motion capture data was used to extract joint angles for representative task cycles at the start and end of the task.
• Symbolic Motion Structure Representation (SMSR) was applied to time-series joint angle data, comparing results with traditional kinematic descriptors (averages and ranges).

Main Results:

• While group-level analysis showed increased joint angle ranges with high between-subject variability, SMSR effectively captured individual adaptive movement strategies.
• Changes in SMSRs across participants correlated with the adoption of new movement patterns indicative of fatigue.
• SMSR demonstrated sensitivity in identifying kinematic shifts associated with muscle fatigue.

Conclusions:

• The Symbolic Motion Structure Representation (SMSR) algorithm is a viable, logical, and sensitive method for identifying fatigue-induced kinematic changes.
• SMSR provides a pragmatic and visually assessable tool for understanding fatigue development and adaptive movement strategies.
• This method offers a promising alternative to traditional analyses, particularly in contexts with high inter-individual variability.