Lumbar spinal stenosis (LSS) is a condition associated with the degeneration of spinal disks in the lower back. A significant majority of the elderly population experiences LSS, and the number is expected to grow. The primary objective of medical treatment for LSS patients has focused on improving functional outcomes (e.g., walking ability) and thus, an accurate, objective, and inexpensive method to evaluate patients' functional levels is in great need. This paper aims to quantify the functional level of LSS patients by analyzing their clinical information and their walking ability from a 10 m self-paced walking test using a pair of sensorized shoes. Machine learning algorithms were used to estimate the Oswestry Disability Index, a clinically well-established functional outcome, from a total of 29 LSS patients. The estimated ODI scores showed a significant correlation to the reported ODI scores with a Pearson correlation coefficient (r) of 0.81 and p<3.5×10(-11). It was further shown that the data extracted from the sensorized shoes contribute most to the reported estimation results, and that the contribution of the clinical information was minimal. This study enables new research and clinical opportunities for monitoring the functional level of LSS patients in hospital and ambulatory settings.

Abstract-Cervical spondylotic myelopathy (CSM) is a chronic spinal disorder in the neck region. Its prevalence is growing rapidly in developed nations, creating a need for an objective assessment tool. This article introduces a system for quantifying hand motor function using a handgrip device and target tracking test. In those with CSM, hand motor impairment often interferes with essential daily activities. The analytic method applied machine learning techniques to investigate the efficacy of the system in (1) detecting the presence of impairments in hand motor function, (2) estimating the perceived motor deficits of patients with CSM using the Oswestry Disability Index (ODI), and (3) detecting changes in physical condition after surgery, all of which were performed while ensuring testretest reliability. The results based on a pilot data set collected from 30 patients with CSM and 30 nondisabled control subjects produced a c-statistic of 0.89 for the detection of impairments, Pearson r of 0.76 with p < 0.001 for the estimation of ODI, and a c-statistic of 0.82 for responsiveness. These results validate the use of the presented system as a means to provide objective and accurate assessment of hand motor function impairment and surgical outcomes.

BackgroundApproximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of significant clinical benefit. However, many of the studied predictors to date suffer from subjective recall bias, lack fine digital measures, and yield poor correlation to outcomes.MethodsThis study utilized smart-shoes to capture gait parameters extracted preoperatively during a 10 m self-paced walking test, which was hypothesized to provide objective, digital measurements regarding the level of gait impairment caused by LSS symptoms, with the goal of predicting postoperative outcomes in a cohort of LSS patients who received lumbar decompression and/or fusion surgery. The Oswestry Disability Index (ODI) and predominant pain level measured via the Visual Analogue Scale (VAS) were used as the postoperative clinical outcome variables.ResultsThe gait parameters extracted from the smart-shoes made statistically significant predictions of the postoperative improvement in ODI (RMSE =0.13, r=0.93, and p<3.92×10−7) and predominant pain level (RMSE =0.19, r=0.83, and p<1.28×10−4). Additionally, the gait parameters produced greater prediction accuracy compared to the clinical variables that had been previously investigated.ConclusionsThe reported results herein support the hypothesis that the measurement of gait characteristics by our smart-shoe system can provide accurate predictions of the surgical outcomes, assisting clinicians in identifying which LSS patient population can benefit from the surgical intervention and optimize treatment strategies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-017-0288-0) contains supplementary material, which is available to authorized users.

Alcohol-induced gait disturbances can be detected with smart shoe technology for an automated monitoring in ubiquitous environment. We demonstrated that personal monitoring and machine learning-based prediction could be customized to detect individual variation rather than applying uniform boundary parameters of gait.

No abstract