A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

Davoudi, Mehrdad; Shokouhyan, Seyed Mohammadreza; Abedi, Mohsen; Meftahi, Narges; Rahimi, Atefeh; Rashedi, Ehsan; Hoviattalab, Maryam; Narimani, Roya; Parnianpour, Mohamad; Khalaf, Kinda

doi:10.3390/s20102902

Cited by 7 publications

(14 citation statements)

References 37 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They were also able to classify them according to the risk of chronicity [12]. Similar results with lower accuracy were obtained with the same test but using classical statistical analysis (linear discriminant analysis) [41]. In our study, we found that the SENS2 had higher discriminating power in both classical statistical analysis and ML analysis.…”

Section: Discussionsupporting

confidence: 82%

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

Thiry

Houry

Philippe

et al. 2022

Sensors

View full text Add to dashboard Cite

Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients.

show abstract

Section: Discussionsupporting

confidence: 82%

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

Thiry

Houry

Philippe

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The ability to maintain stability and balance to prevent falls in the elderly is critical. It is not surprising that many studies have identified lower back muscle weakness and balance problems (66,67) as the most important risk factors for falls (68). Furthermore, in COPD patients, the relationship between different components of balance and falling has been well investigated (29)(30)(31).…”

Section: Balance Deficitsmentioning

confidence: 99%

Falls Incidence and Associated Risk Factors among People with Chronic Obstructive Pulmonary Disease (COPD)

Davoudi¹,

Shokouhyan²,

Abedi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a systemic disease rather than a mere disorder of the lungs. Central (respiratory) and peripheral (limb) muscle weakness are among the main pronounced systemic effects of COPD. While the disease primarily affects the lower limb muscles and contributes to gait impairment, COPD is also associated with an increasing risk of falls in patients (COPDp). Previous studies have reported higher rates of falls among COPDp (1.17 to 1.20 falls/person-year), amounting to four times higher than an age-matched healthy group. Potential fall risk factors include muscle weakness, impaired daily activities, cognitive dysfunction, and gait and balance impairment. Although COPDp often manifest many of these risk factors, there remains a gap in literature regarding falls during walking in this population. This study aimed to 1. analyze the literature to identify the risk factors of falling in COPDp, and 2. investigate the underlying mechanisms by which these risk factors can lead to increased prevalence of falling. The results suggest that in addition to the known risk factors of falling, low back pain and mental fatigue should also be considered as relevant risk factors in the treatment process of these patients. Moreover, respiratory problems, which are common in this population, have demonstrated pronounced effects on energy expenditure, gait, and other types of activities of daily living (ADLs), leading to reduced intensity, disrupted coordination of the trunk-pelvic structure with the lower limbs during gait, and altered motor control performance due to activation of muscles in an inefficient synergic manner. These problems potentially lead to the increased vulnerability of these patients to external disturbances and higher incidence risk of falls and injuries. Cognitive problems, which are typically due to reduced oxygen received by the brain, as well as general inflammation caused by COPD, also play a significant role in gait disruption and balance. Future research is warranted to determine the prevalence of falls in COPDp by examining the response of these patients to Medio-Lateral (ML) and Anterior-Posterior (AP) disturbances during gait in association with traditional and recommended fall risk factors.

show abstract

“…A Support Vector Machine classifier methodology was utilized for data classification, which enabled the distinction between the two groups with high accuracy and sensitivity. More recently, smart wearables and sensor-based methodologies have yielded encouraging results for the quantification and classification of LBP ( Davoudi et al, 2020 ). These novel technologies, along with big data management tools (AI, ML, SVM, etc.)…”

Section: Introductionmentioning

confidence: 99%

Distinction of non-specific low back pain patients with proprioceptive disorders from healthy individuals by linear discriminant analysis

Shokouhyan

Davoudi

Hoviattalab

et al. 2022

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

The central nervous system (CNS) dynamically employs a sophisticated weighting strategy of sensory input, including vision, vestibular and proprioception signals, towards attaining optimal postural control during different conditions. Non-specific low back pain (NSLBP) patients frequently demonstrate postural control deficiencies which are generally attributed to challenges in proprioceptive reweighting, where they often rely on an ankle strategy regardless of postural conditions. Such impairment could lead to potential loss of balance, increased risk of falling, and Low back pain recurrence. In this study, linear and non-linear indicators were extracted from center-of-pressure (COP) and trunk sagittal angle data based on 4 conditions of vibration positioning (vibration on the back, ankle, none or both), 2 surface conditions (foam or rigid), and 2 different groups (healthy and non-specific low back pain patients). Linear discriminant analysis (LDA) was performed on linear and non-linear indicators to identify the best sensory condition towards accurate distinction of non-specific low back pain patients from healthy controls. Two indicators: Phase Plane Portrait ML and Entropy ML with foam surface condition and both ankle and back vibration on, were able to completely differentiate the non-specific low back pain groups. The proposed methodology can help clinicians quantitatively assess the sensory status of non-specific low back pain patients at the initial phase of diagnosis and throughout treatment. Although the results demonstrated the potential effectiveness of our approach in Low back pain patient distinction, a larger and more diverse population is required for comprehensive validation.

show abstract

A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings

Cited by 7 publications

References 37 publications

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

Falls Incidence and Associated Risk Factors among People with Chronic Obstructive Pulmonary Disease (COPD)

Distinction of non-specific low back pain patients with proprioceptive disorders from healthy individuals by linear discriminant analysis

Contact Info

Product

Resources

About