Development of a Wearable Activity Tracker Based on BBC Micro:bit and Its Performance Analysis for Detecting Bachata Dance Steps

The rising popularity of wearable activity tracking devices can be attributed to their capacity for gathering and analysing ambient data, which finds utility across numerous applications. In this study, a wearable activity tracking device is developed using the BBC micro:bit development board to identify basic bachata dance steps. Initially, a pair of smart ankle bracelets is crafted, employing the BBC micro:bit board equipped with a built-in accelerometer sensor and a Bluetooth module for transmitting accelerometer data to smartphones. Subsequently, a dataset encompassing six core bachata dance steps synchronized to four beats is created from ten participants to examine the performance of the system. A metric using squared Euclidean distance is applied for the accelerometer raw data to facilitate and standardize the automatic detection of the steps by the system. A user interface, built with Python and Tkinter library, is developed to enable automatic step detection using the accelerometer dataset. The results demonstrated a system accuracy rate of 79.2%.