If you had competency anywhere amongst your therapists, doctors and hospital this would easily be seen as a way to objectively measure stroke survivors gait deficits and then use that to assign EXACT REHAB PROTOCOLS TO 100% RECOVER. But nothing will occur, you don't have two neurons to rub together amongst your stroke medical 'professionals'.
Do you prefer your incompetence NOT KNOWING? OR NOT DOING?
Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection
Journal of NeuroEngineering and Rehabilitation volume 19, Article number: 132 (2022)
Abstract
Background
Vestibular deficits can impair an individual’s ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers.
Methods
Thirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task.
Results
Several models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed.
Conclusions
These findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.
Background
Vestibular disorders such as bilateral/unilateral vestibular hypofunction can impair an individual’s ability to maintain postural and/or gaze stability during standing and walking [1, 2]. The loss of vestibular function may result in symptoms of dizziness, unsteadiness and an increased risk for near-falls and falls during mobility and gait [3, 4]. Prior studies have estimated that up to 35
of Americans experience vestibular-related issues during their lifetime [1, 5, 6]. Typically, a vestibular diagnosis is determined through a battery of specialized tests (e.g., Computerized Dynamic Posturography, Videonystagmography (VNG) and Rotational Chair Testing). Access to such diagnostic tools relies, however, on the referral to specialists by primary care providers, but referral rates remain low [7], leaving affected individuals under-diagnosed.
Prior to in-depth diagnostic testing, screening tests are typically used to determine whether an individual would benefit from such specialized testing. Bedside screening tests for vestibular deficits consist of tests to assess: vestibular-ocular reflex (VOR) such as head impulse tests [8], spatial orientation such as the Fukuda Stepping test [9], and balance performance during static (standing balance) and dynamic tasks (walking balance) [10]. Additionally, the Dix-Hallpike and supine roll tests can be used to screen for Benign Paroxysmal Positional Vertigo (BPPV) [11]. However general providers may have limited knowledge and experience in performing and interpreting such tests accurately.
Clinicians typically assess balance and fall risk during gait using conventional gait assessment tools that include the Functional Gait Assessment (FGA) [12], the Dynamic Gait Index (DGI) [13], 10-Meter Walk Test [14], and Timed Up and Go (TUG) [15]. However, these conventional clinical assessments rely on timed tests and observations of path deviations that do not consider the subtle changes in full-body kinematics resulting from vestibular deficits. Observational assessments allow clinicians to examine the overall movements of individuals to assess their reactions but may not detect changes to the patterns of individual body segments during gait. Vestibular deficits have been shown to affect spatiotemporal gait parameters (such as cadence, step length [16], step width, path adherence [17], etc.) and cause gaze stability deficits, often resulting in abnormal head-trunk stabilization during stepping and walking [18]. Standard clinical gait assessment tools do not capture features related to spatiotemporal gait parameters and upper-body coordination and therefore offer limited insights into the complex kinematics of vestibular gait.
Wearable sensors such as inertial measurement units (IMUs) present an opportunity to measure and characterize gait by quantifying the movement of various body segments throughout the gait cycle. While other movement tracking technologies such as motion capture have been used in research settings, IMUs are better adapted to clinical use as they are more cost-effective, require minimal set-up, and are easily integrated into portable/wearable electronics. IMUs have been used to detect gait events and estimate spatiotemporal gait parameters such as stride length, stride time, stance time, swing time and gait speed [19], as well as to estimate upper-body kinematics [20]. Wearable IMUs have been used to estimate spatiotemporal gait metrics of individuals with vestibular deficits during the 2-Minute Walk Test [21], and measure walking/turning times during conventional gait tests such as the FGA [22] and TUG [23], thus providing a quantitative measurement of kinematic changes that occur during gait tasks when the vestibular system is affected.
Machine learning (ML) methods have further enabled the use of wearable IMUs in a variety of balance assessment and gait analysis contexts to automatically detect balance deficiencies and classify pathological gait due to Parkinson’s disease [24,25,26,27,28], cerebellar ataxia [29,30,31], and cerebral thrombosis [32]. However, few studies have applied ML methods to IMU-based kinematic data to classify and screen for gait abnormalities related to vestibular deficits. Namely, a study by Ikizoglu et al. [33] reported a binary (vestibular/control) classification model based on a dataset of kinematic data captured using IMUs placed on the feet, knees and lower back while participants walked along a 11.5 m path. Because the IMUs in this study were only placed on the lower body, abnormalities in the upper-body coordination strategies observed in populations with vestibular deficits were not captured. In addition, participants performed a single simple gait task. Another study by Nguyen et al. [34] demonstrated a binary (vestibular/control) classification model based on kinematic data from one IMU placed on the upper back while participants performed the DGI. A single IMU on the upper back was used in this study resulting in the capture of movements from one segment of the participants’ bodies during gait tasks involving head movements, stepping over an obstacle, changing speed and a pivot turn. While these studies provide evidence of the feasibility IMU-based automatic classification of vestibular gait, they investigated only a limited set of IMUs and gait tasks.
The number and placement of IMUs as well as the selection of gait tasks used to classify vestibular gait have impacts on model performance and practical implications when such systems are deployed for clinical use. Different IMU placements measure features related to the movement of different body segments and therefore capture various compensatory strategies or adaptations reported within the vestibular population such as changes to spatiotemporal gait parameters [16, 17, 35] and abnormal head-trunk stabilization [18]. The choice of IMU placement on different body segments was shown to have an effect on model performance in the context of gait-based classification of stroke and other neurological disorders [36], and the choice of IMU placement on a given body segment was shown to affect the accuracy of estimated spatio-temporal gait parameters [37, 38] and measures of stability [39]. In addition, the number of IMUs and their placement are important factors for technology adoption within clinical settings - fewer sensors and ease of placement are preferable [40]. Similarly, gait task selection can affect classification performance as different gait tasks within common functional assessments challenge participants’ sensory compensation strategies in different ways [41, 42], and therefore highlight their gait deficits under different sensory conditions. The number of tasks needed to screen for vestibular deficits also has practical implications on the duration of testing. Therefore, the goals of this study were to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers in order to inform the design of accurate, reliable and adoptable IMU-based automatic screening tools.
Methods
In this study, we aimed to identify wearable IMU placements and gait tasks best suited for the automatic classification of vestibular gait through ML. We used a set of full-body (including the head, trunk, arms, wrists, thighs, shanks, and feet) wearable IMUs to capture the kinematics of various body segments among participants with vestibular deficits and age-matched controls during a variety of gait tasks. We then used the kinematic data collected to extract descriptive features and train ML models to classify vestibular gait. We assessed the predictive power of models trained on features extracted from various combinations of single IMU placements and different gait tasks in terms of area under the receiver operating characteristic curve (AUROC) scores.
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