ST-HAR: A Single Stretchable Sensor Dataset for Human Activity Recognition

Nowadays, Human Activity Recognition (HAR) is growing in interest considering the widespread adoption of cheap and healthcare-based devices like Inertial Measurement Units (IMUs) or smartwatches. This study introduces three key advancements in HAR in the context of sports performance monitoring: (i) the development of a stretchable-sensor-based dataset comprising five individuals performing walking, jogging, and running; (ii) the design of an ultra-lightweight image encoding technique for sensor signals; and (iii) the creation of a custom tiny Convolutional Neural Network (CNN) optimized for future near-sensor hardware deployment. The CNN was trained and tested on a literature dataset (w-HAR) and a custom dataset (ST-HAR), both based on a single stretchable sensor. ST-HAR was specifically developed to address the lack of sports-related data from this type of sensor and includes activities performed at speeds from 0.5 to 14 km/h. Future work will expand this dataset and deploy a custom hardware accelerator for the proposed CNN model.