Local dynamic stability during gait for predicting falls in elderly people: A one-year prospective study

Abstract

Computing the local dynamic stability using accelerometer data from inertial sensors has recently been proposed as a gait measure which may be able to identify elderly people at fall risk. However, the assumptions supporting this potential were concluded as most studies implement a retrospective fall history observation. The aim of this study was to evaluate the potential of local dynamic stability for fall risk prediction in a cohort of subjects over the age of 60 years using a prospective fall occurrence observation. A total of 131 elderly subjects voluntarily participated in this study. The baseline measurement included gait stability assessment using inertial sensors and clinical examination by Tinetti Balance Assessment Tool. After the baseline measurement, subjects were observed for a period of one year for fall occurrence. Our results demonstrated poor multiple falls predictive ability of trunk local dynamic stability (AUC = 0.673). The predictive ability improved when the local dynamic stability was combined with clinical measures, a combination of trunk medial-lateral local dynamic stability and Tinetti total score being the best predictor (AUC = 0.755). Together, the present findings suggest that the medial-lateral local dynamic stability during gait combined with a clinical score is a potential fall risk assessment measure in the elderly population.

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

“…Using a partial least-squares discriminant analysis, they were able to classify fallers and nonfallers with an AUC ranging from 0.83 to 0.93, depending on the features included in classification [21]. In another approach, Bizovska et al also employed inertial sensors and demonstrated improved predictive power for fall risk (AUC 0.755) when combining clinical features with measures of local dynamic stability, compared to local dynamic stability alone (AUC 0.673) [22]. Hemmatpour et al utilized the accelerometer and gyroscope sensors built into smartphones to predict abnormal gait with high accuracy (93.5%) [23].…”

Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

“…The use of LDS to characterize gait stability and assess fall risk has gained popularity over recent years (Mochizuki & Aliberti, 2017;Bizovska et al, 2018;Mehdizadeh, 2018). Computing ACI in addition to LDS is straightforward and using the measures together could be fruitful, as information about gait automaticity and cautiousness would complement information about gait stability.…”

Peer Review #2 of "Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues (v0.2)"

“…The only significant correlations concerned the AP-LDS and they were negative, i.e., went in opposite direction compared to the ACI correlations. ML-LDS has been shown to be an index of gait instability and fall risk (Bizovska et al, 2018). This may be due to the importance of lateral stability for maintaining a steady and safe gait (Bauby & Kuo, 2000;Gafner et al, 2017).…”