Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach

Abstract

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.

Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review

“…ML could help either in the diagnosis [32, 34, 35, 37-39, 41, 42] or in supporting doctors' decisions [33,40] or guiding patients through a personalized therapeutic plan [29,31,36], with accuracy and in real-time, giving important clinical information to doctors and therapists and allowing adherence monitoring. ML could also be useful to identify patients' response to functional rehabilitation [30], automatic recognition of fear-avoidance behaviour [41,42] or to categorize patients into different subgroups of LBP risk [37]. Some of the studies using ML for LBP treatment use apps [29,31,36], since almost everyone has a smartphone nowadays, including medical students and doctors use medical smartphone apps related to procedure documentation, disease diagnosis, clinical score and drug reference [43].…”

“…Because exercises increase the level of pain, anxiety about anticipated pain increase may lead to seatback and intensified sensitivity to pain [35] and a fear-avoidance behaviour [46]; the avoidance is expressed through self-protective body movement to avoid strain in the painful area, decreasing exercise adherence [41,42]. Visual Analogue Scale or Oswestry Low Back Pain Questionnaire are examples of most used tools for pain assessment and disability in LBP patients [30,37]. Depression scales are also used in LBP research [35], as well as daily activity questionnaires [33,40].…”

Uso de sensores inerciales en fisioterapia: Una aproximación a procesos de evaluación del movimiento humano

“…Los IMU han apoyado diversos procesos evaluativos, permitiendo analizar el control postural (31) , estabilidad de la columna vertebral (27) , rangos de movimiento articular (7) en miembros superiores (45) e inferiores y la ejecución de patrones de movimiento como, realizar una estocada, correr (32,33) , ascender y descender escaleras, marchar sobre superficie inclinada, declinada y nivelada (46) , y en comparación con softwares de análisis de movimiento optoelectrónicos mostraron una buena correlación, resaltando su precisión y bajo costo (34,7,14,33,46) lo cual perfila a estos dispositivos como una alternativa efectiva.…”

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

“…All reported evaluation outcomes and their corresponding evaluation method are included in Table 6 and depicted in Figure 6. The most common evaluation method was descriptive statistics (61.4%) including or not statistical tests [37,39- [34][35][36]38,45,53,60,63,[68][69][70]77,[79][80][81]86,95,97]. Due to the lack of a standardized evaluation metric across studies, we do not summarize (calculate mean, standard deviation, etc.)…”

“…The number of studies related to the issue of validation on sensors used for patient monitoring has significantly increased since 2010, with a number of papers between 2017 and 2020, more than twice the number of papers between 2010 and 2017 (see Figure 2). Studies using machine learning as a validation method also became more numerous since 2010 [34][35][36]38,45,53,60,63,[68][69][70]77,[79][80][81]86,95,97], with a stable proportion compared to the total number of studies per year. Evolution of the number of papers considering the issue of validation for the use of commercial wearable devices in chronic disease monitoring, with a distinction between papers using machine learning (in red) or not (in blue).…”