Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Abstract

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls.

Effects of vestibular neurectomy and neural compensation on head movements in patients undergoing vestibular schwannoma resection

“…Current availability of wearable sensors as well as their accuracy for measuring gait parameters [37][38][39][40] make them suitable for augmenting the information during clinical gait assessments such as FGA. Also, given the importance of head motion in both understanding vestibular processing [41][42][43][44] and diagnosing and treating vestibular disorders 15,22,25,26,[45][46][47][48][49][50][51] , we expect that this field will expand in importance over the next few years and will eventually become an integral component of clinical vestibular medicine.…”

Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection

“…Other IMU placements also achieved good classification performance for some of the gait tasks (AUROC 0.70) (Table 4 ). Previous studies have reported ML-based classification models based on kinematic data collected from IMUs placed on the lower limbs and/or trunk during gait for participants with vestibular deficits [ 33 , 34 ], but they have not explored upper extremity placements of IMUs. In a previous study examining the effect of IMU placement on the classification of stroke and other neurological disorders [ 36 ], a shank IMU placement resulted in better model performance compared with feet, thighs, and lower back placements.…”

“…The kinematic and spatiotemporal gait features extracted from each IMU placement are described in Table 3 . Features included ones that have been previously reported to show differences between gait patterns from individuals with vestibular deficits and controls [ 33 , 34 ] as well as features commonly used to quantify balance performance during gait.…”

“…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.…”

The Relationship of Balance Disorders with Falling, the Effect of Health Problems, and Social Life on Postural Balance in the Elderly Living in a District in Turkey

“…Balance disorders generate a significant healthcare burden due to the rise in hospitalization, morbidities, and mortalities in the elderly population [8]. Most of the patients who present to emergency services complain of balance disorders.…”