Interpersonal synchrony (IS), a crucial indicator of social interactions, is traditionally studied through video data and manual coding methods. Our study aims to develop automated coding and visualization tools for time series data collected by IMU sensors, offering a more efficient, objective, and scalable analysis of interaction dynamics. This study evaluates several time series similarity analyses, including Cross-correlation (CC), Dynamic Time Warping (DTW), and Cross-Wavelet Analysis, to predict interaction levels using regression and classification techniques. We conducted a comparative study using both simulated data and real-world data involving sessions between teachers and children with and without autism to optimize the parameters for each algorithm and investigate different combinations of time series analysis. The results demonstrate that combining different time series analyses is advantageous, with Cross-wavelet Analysis (CWA) being particularly effective in quantifying interpersonal synchrony due to its ability to analyze synchrony across different frequencies. Additionally, a visualization tool is designed to display the dynamics of interaction over time and pairwise interactions conveniently. This paper provides a step-by-step guideline for studying interpersonal synchrony. It establishes a robust automated algorithm as a tool to track, visualize and understand social interactions, which can be easily adapted to a broader range of motor-coordination-related applications.