Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
| Autor: | Sajad Ashouri, Kinda Khalaf, Ehsan Rashedi, Nasibeh Azadeh-Fard, Mohsen Abedi, Masoud Abdollahi, Mohamad Parnianpour |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Computer science medicine.medical_treatment STarT back screening tool Machine learning computer.software_genre lcsh:Chemical technology Biochemistry Article Analytical Chemistry objective clinical decision-making trunk kinematics Machine Learning 03 medical and health sciences 0302 clinical medicine medicine Humans lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation wearable systems 030222 orthopedics Rehabilitation business.industry pattern recognition Torso Middle Aged Low back pain Atomic and Molecular Physics and Optics Biomechanical Phenomena Categorization classification Artificial intelligence medicine.symptom business computer Low Back Pain 030217 neurology & neurosurgery |
| Zdroj: | Sensors Volume 20 Issue 12 Sensors, Vol 20, Iss 3600, p 3600 (2020) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20123600 |
| Popis: | Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today&rsquo 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. |
| Databáze: | OpenAIRE |
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