Novel Non-Invasive In-house Fabricated Wearable System with a Hybrid Algorithm for Fetal Movement Recognition

TL;DR

This paper presents a low-cost, non-invasive wearable system with a hybrid algorithm for reliable fetal movement monitoring at home, validated through clinical testing with over 120 pregnant women and supported by a mobile app.

Contribution

The study introduces a novel in-house fabricated sensor system combined with a hybrid signal processing algorithm for fetal movement detection in non-clinical settings.

Findings

High accuracy in fetal movement detection compared to ultrasound ground truth

Effective implementation of multiple signal processing algorithms for optimal performance

Successful development of a user-friendly mobile application for pregnant mothers

Abstract

Fetal movement count monitoring is one of the most commonly used methods of assessing fetal well-being. While few methods are available to monitor fetal movements, they consist of several adverse qualities such as unreliability as well as the inability to be conducted in a non-clinical setting. Therefore, this research was conducted to design a complete system that will enable pregnant mothers to monitor fetal movement at home. This system consists of a non-invasive, non-transmitting sensor unit that can be fabricated at a low cost. An accelerometer was utilized as the primary sensor and a micro-controller based circuit was implemented. Clinical testing was conducted utilizing this sensor unit. Two phases of clinical testing procedures were done and readings from more than 120 pregnant mothers were taking. Validation was done by conducting an abdominal ultrasound scan which was utilized as the ground truth during the second phase of the clinical testing procedure. A clinical survey was also conducted in parallel with clinical testings in order to improve the sensor unit as well as to improve the final system. Four different signal processing algorithms were implemented on the data set and the performance of each was compared with each other. Consequently, the most feasible as well as the best performing algorithm was determined and it was utilized in the final system. Furthermore, a mobile application was also developed to be used with the sensor unit by pregnant mothers. Finally, a complete end to end method to monitor fetal movement in a non-clinical setting was presented by the proposed system.