Effects of barefoot vs. shod walking during indoor and outdoor conditions in younger and older adults

Self Cite

The evaluation of local divergence exponent (LDE) has been proposed as a common gait stability measure in people with multiple sclerosis (PwMS). However, differences in methods of determining LDE may lead to different results. Therefore, the purpose of the current study was to determine the effect of different sensor locations and LDE measures on the sensitivity to discriminate PwMS. To accomplish this, 86 PwMS and 30 healthy participants were instructed to complete a six-minute walk wearing inertial sensors attached to the foot, trunk and lumbar spine. Due to possible fatigue effects, the LDE short (~50% of stride) and very short (~5% of stride) were calculated for the remaining first, middle and last 30 strides. The effect of group (PwMS vs. healthy participants) and time (begin, mid, end) and the effect of Expanded Disability Status Scale (EDSS) and time were assessed with linear random intercepts models. We found that perturbations seem to be better compensated in healthy participants on a longer time scale based on trunk movements and on a shorter time scale (almost instantaneously) according to the foot kinematics. Therefore, we suggest to consider both sensor location and time scale of LDE when calculating local gait stability in PwMS.

Section: Data Processingmentioning

confidence: 99%

Measuring Gait Stability in People with Multiple Sclerosis Using Different Sensor Locations and Time Scales

Müller

Schreff

Koch

et al. 2021

Self Cite

“…Previous studies on forward step initiation highlighted that shoes do not impact postural preparation along the AP axis [32,33]; consistently, our results showed no significant effects of footwear along the AP direction. A possible explanation is that, although shoes modify dynamic postural control and foot stability [35][36][37]50,51], they do not influence the mechanism underlying the AP CoP displacement as much (i.e., ankle dorsiflexion) [13,15,52]. On the contrary, and in line with the literature on forward step [32,33], footwear significantly affected APAs along the ML direction.…”

Section: Discussionmentioning

confidence: 59%

“…APAs are known to be modulated by the central nervous system based on environmental factors [53][54][55] as well as tactile, kinesthetic, and proprioceptive information [2,3,56]. The literature shows that footwear may represent a constraint for ankle and foot joints [35,37,38] and may also interfere with afferent information gathered by the feet soles, due to the interposition of cushioning elements (e.g., midsole). In addition, larger postural preparation along the ML direction may result in smaller velocities and accelerations of the body during the movement [21,27].…”

Section: Discussionmentioning

confidence: 99%

“…For instance, footwear and limb preference play a relevant role, especially when considering everyday life conditions [31][32][33]. Footwear is present in most of the steps people take every day, providing sanitary and mechanical protection, yet at the same time modifying body kinematics [34][35][36][37]. Research has shown that footwear induces alteration of gait either by altering the ankle range of motion [38] or by influencing somatosensory information [39,40].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Anticipatory Postural Adjustments in Lateral Stepping: Impact of Footwear and Lower Limb Preference

Russo

Marinković

Obradović

et al. 2021

Lateral stepping is a motor task that is widely used in everyday life to modify the base of support, change direction, and avoid obstacles. Anticipatory Postural Adjustments (APAs) are often analyzed to describe postural preparation prior to forward stepping, however, little is known about lateral stepping. The aim of the study is to characterize APAs preceding lateral steps and to investigate how these are affected by footwear and lower limb preference. Twenty-two healthy young participants performed a lateral step using both their preferred and non-preferred leg in both barefoot and shod conditions. APA spatiotemporal parameters (size, duration, and speed) along both the anteroposterior and mediolateral axes were obtained through force plate data. APAs preceding lateral stepping showed typical patterns both along the anteroposterior and mediolateral axis. RM-ANOVA highlighted a significant effect of footwear only on medio-lateral APAs amplitude (p = 0.008) and velocity (p = 0.037). No differences were found for the limb preference. APAs in lateral stepping presented consistent features in the sagittal component, regardless of limb/shoe factors. Interestingly, the study observed that footwear induced an increase in the medio-lateral APAs size and velocity, highlighting the importance of including this factor when studying lateral stepping.

“…Second, all experiments were conducted indoors because the MoCap results were used as the gold standard. However, according to previous studies, different gait kinematic results are obtained depending on the test environment, including whether the subjects are barefoot or in shoes [ 42 ], indoors or outdoors [ 43 ], male or female [ 44 ], and young or old [ 45 ]. Thus, in future studies, a larger group of participants with differences in demographic features, such as sex, age, height, and weight, needs to be recruited, and experiments should be conducted in various environments.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

Sung

Han

Park

et al. 2021

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.