A smart point-of-care compliance monitoring solution for brace treatment of adolescent idiopathic scoliosis patients

Abstract

Scoliosis is a medical condition which occurs in adolescents, where an individual's spine develops curvature. A Thoracolumbosacral orthosis (TLSO) is a type of brace used as a long term treat-ment method to control the lateral curvature of the spine in adolescent idiopathic scoliosis (AIS). However, compliance of the daily prescribed duration and tightness of the brace is a big challenge, which plays a significant role in the success of brace treatment. In this paper, we designed and de-veloped a wearable multi-modal sensor solution, which is embedded into the AIS patient's brace. The custom-designed hardware consists of a sensor board, a force sensor & housing, a miniature triaxial accelerometer and gyroscope sensor. The force sensor records the force being exerted by the patient's brace padding, while the accelerometer and gyroscope sensors capture upper trunk movements, providing cues to determine the patient's activities and lifestyle to help with more accurate estimation of the two key parameters, including 1) the in-brace tightness level 2) the duration of the brace wear, in an unsupervised manner. Furthermore, we presented a signal pro-cessing and data analytic methodology to identify the duration and tightness of the brace wear, in addition to identifying various patient activities based on the fusion of continuous force and iner-tial motion sensor recordings. We, specifically, derived a signal processing activity identification method using power spectral density estimation of the sensor data recordings. Then, we evaluate of the effectiveness of brace treatment in a pervasive manner. The proposed method evaluates the duration of brace wear through the process of segmentation and calculates the tightness level of the brace by estimating the baseline force per segment of data. This analysis is done when the patient performs activities including sitting, standing, climbing, walking, running and lying. We investigated an experimental scenario in which the patient performs a series of pre-defined activ-ities at home during day-long segments of brace wear. All analysis is performed using pervasive sensor data recordings. The experimental results demonstrated that we achieved an overall accu-racy of 99.8%, 100% and 99.9% for patients 1, 2 and 3 respectively for semi-supervised activity detection. We continued our compliance study for approximately a month. The level of tight-ness of brace-fit gradually reduced over a period of 2 weeks by 30% as the compliance of brace treatment increased from 7% to 90% on average. Given our experimental results and objective observations, our proposed system is capable of arranging for re-fitting sessions automatically so that the physician can adjust the brace tightness levels for a more effective brace treatment. • Brace treatment for AIS patients is long-term & pervasive demanding self-compliance. • Pervasive monitoring can provide two key factors of duration and tightness of the brace-wear. • We design a multimodal smart brace monitoring system to estimate the two factors. • We design a robust signal processing recipe to undertake unsupervised recordings. • Our experiments demonstrated that our system generates reliable & robust estimates.