Quantifying the effect of sagittal plane joint angle variability on bipedal fall risk

Abstract

Falls are a major issue for bipeds. For elderly adults, falls can have a negative impact on their quality of life and lead to increased medical costs. Fortunately, interventional methods are effective at reducing falls assuming they are prescribed. For biped robots, falls prevent them from completing required tasks. Thus, it is important to understand what aspects of gait increase fall risk. Gait variability may be associated with increased fall risk; however, previous studies have not investigated the variation in the movement of the legs. The purpose of this study was to determine the effect of joint angle variability on falling to determine which component(s) of variability were statistically significant. In order to investigate joint angle variability, a physics-based simulation model that captured joint angle variability as a function of time through Fourier series was used. This allowed the magnitude, the frequency mean, and the frequency standard deviation of the variability to be altered. For the values tested, results indicated that the magnitude of the variability had the most significant impact on falling, and specifically that the stance knee flexion variability magnitude was the most significant factor. This suggests that increasing the joint variability magnitude may increase fall risk, particularly if the controller is not able to actively compensate. Altering the variability frequency had little to no effect on falling.

Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

“…in Fig 6 ). The computed variability is similar to other studies [ 44 – 46 ]. The form and absolute values of the computed average signals are also similar to those demonstrated in previous studies [ 47 – 49 , 50, p. 165].…”

Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

“…in Fig.6). The computed variability is similar to other studies (44–46). The form and absolute values of the computed average signals are also similar to those demonstrated in previous studies (47–50).…”

Design of SmartWalk for Estimating Implication of Pathway With Turn and Task Condition on Postural and Gait Indices: Relevance to Fear of Fall

“…In contrast, for GrpH, the increase in %CV was found to be statistical only while comparing ST and MT (irrespective of the pathways and only for toe-off event). Stronger effect on knee flexion of GrpPD than that of GrpH due to increase in task complexity can be possibly attributed to the fact that DT and MT are more attention-demanding than ST inducing increased postural instability [15], adversely affecting the balance of GrpPD thereby making them more prone to falls [38].…”