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.
Highlights
Falls in bipeds are widely acknowledged to be undesirable, but difficult to predict [1,2,3,4]
Human studies generally focus on finding correlations between risk factors [16,17,18,19] or gait parameters [20,21,22] and fall risk
Stance knee magnitude had the most significant effect on falling; on average, the model took 31 more steps when at the low level of variability compared to the high level of variability when combining results from all speeds
Summary
Falls in bipeds are widely acknowledged to be undesirable, but difficult to predict [1,2,3,4]. Exercise and risk management can reduce falls [6,7,8], motivating identification of potential fallers to provide interventional care. One challenge with predicting falls is that they have many potential causes. Robotic studies have primarily evaluated falls due external factors, such as walking across uneven terrain [13] or due to velocity disturbances such as a push [14, 15]. These works tend to focus on discrete, rather than continuous, perturbations. Human studies generally focus on finding correlations between risk factors [16,17,18,19] or gait parameters [20,21,22] and fall risk
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call