Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.

Associations between locomotive and non-locomotive physical activity and physical performance in older community-dwelling females with and without locomotive syndrome: a cross-sectional study

Background Locomotive syndrome (LS) is a condition of reduced mobility due to a disorder of the locomotive system. Increasing moderate to vigorous physical activity (MVPA) has been recommended to prevent LS. However, to increase daily MVPA is difficult for older people with LS. The MVPA consists of not only locomotive activities such as walking but also non-locomotive activities such as household activities. The aim of this study was to examine the associations between locomotive/non-locomotive MVPA and physical performance in older females with and without LS. Methods Participants of this cross-sectional study were 143 older community-dwelling Japanese females. The participants were divided into two groups based on the results of the stand-up test: the normal group (NL) (n = 86) and the LS group (n = 57). Both the locomotive and non-locomotive PA seperately measured with its intensity. The intensity of physical activity (PA) was calculated as METs and classified as sedentary behavior (SB 1–1.5 metabolic equivalent tasks (METs)), low-intensity physical activity (LPA 1.6–2.9 METs), and MVPA (≥ 3 METs). For example, locomotive LPA is slow walking speed of 54 m/min, and locomotive MVPA is walking speed of 67 m/min. While non-locomotive LPA is office work and cooking, non-locomotive MVPA is housecleaning. Physical function was evaluated by handgrip strength, walking speed, and 2-step test. Results Walking speed, hand-grip strength, 2-step test, daily step counts, and all PA measurements were not significantly different between two groups. In the LS, locomotive MVPA (r = 0.293, p < 0.05) and total MVPA (r = 0.299, p < 0.05) was significantly correlated with walking speed, but not in the NL. Conclusions Walking speed was positively correlated with locomotive MVPA and total MVPA in the LS group, but not in NL group. This result suggests that slow walking speed in older people with LS occur in connection with lower locomotive MVPA and total MVPA.

Optimized hip–knee–ankle exoskeleton assistance at a range of walking speeds

Background Autonomous exoskeletons will need to be useful at a variety of walking speeds, but it is unclear how optimal hip–knee–ankle exoskeleton assistance should change with speed. Biological joint moments tend to increase with speed, and in some cases, optimized ankle exoskeleton torques follow a similar trend. Ideal hip–knee–ankle exoskeleton torque may also increase with speed. The purpose of this study was to characterize the relationship between walking speed, optimal hip–knee–ankle exoskeleton assistance, and the benefits to metabolic energy cost. Methods We optimized hip–knee–ankle exoskeleton assistance to reduce metabolic cost for three able-bodied participants walking at 1.0 m/s, 1.25 m/s and 1.5 m/s. We measured metabolic cost, muscle activity, exoskeleton assistance and kinematics. We performed Friedman’s tests to analyze trends across walking speeds and paired t-tests to determine if changes from the unassisted conditions to the assisted conditions were significant. Results Exoskeleton assistance reduced the metabolic cost of walking compared to wearing the exoskeleton with no torque applied by 26%, 47% and 50% at 1.0, 1.25 and 1.5 m/s, respectively. For all three participants, optimized exoskeleton ankle torque was the smallest for slow walking, while hip and knee torque changed slightly with speed in ways that varied across participants. Total applied positive power increased with speed for all three participants, largely due to increased joint velocities, which consistently increased with speed. Conclusions Exoskeleton assistance is effective at a range of speeds and is most effective at medium and fast walking speeds. Exoskeleton assistance was less effective for slow walking, which may explain the limited success in reducing metabolic cost for patient populations through exoskeleton assistance. Exoskeleton designers may have more success when targeting activities and groups with faster walking speeds. Speed-related changes in optimized exoskeleton assistance varied by participant, indicating either the benefit of participant-specific tuning or that a wide variety of torque profiles are similarly effective.

Effect of Walking Speeds on Complexity of Plantar Pressure Patterns

Various walking speeds may induce different responses on the plantar pressure patterns. Current methods used to analyze plantar pressure patterns are linear and ignore nonlinear features. The purpose of this study was to analyze the complexity of plantar pressure images after walking at various speeds using nonlinear bidimensional multiscale entropy (MSE2D). Twelve participants (age: 27.1 ± 5.8 years; height: 170.3 ± 10.0 cm; and weight: 63.5 ± 13.5 kg) were recruited for walking at three speeds (slow at 1.8 mph, moderate at 3.6 mph, and fast at 5.4 mph) for 20 minutes. A plantar pressure measurement system was used to measure plantar pressure patterns. Complexity index (CI), a summation of MSE2D from all time scales, was used to quantify the changes of complexity of plantar pressure images. The analysis of variance with repeated measures and Fisher’s least significant difference correction were used to examine the results of this study. The results showed that CI of plantar pressure images of 1.8 mph (1.780) was significantly lower compared with 3.6 (1.790) and 5.4 mph (1.792). The results also showed that CI significantly increased from the 1st min (1.780) to the 10th min (1.791) and 20th min (1.791) with slow walking (1.8 mph). Our results indicate that slow walking at 1.8 mph may not be good for postural control compared with moderate walking (3.6 mph) and fast walking (5.4 mph). This study demonstrates that bidimensional multiscale entropy is able to quantify complexity changes of plantar pressure images after different walking speeds.

A study of the accuracy of the Fitbit Zip in measuring steps both indoors and outdoors in a mixed rehabilitation population

Objective: To assess (1) step count accuracy of the Fitbit Zip, compared to manual step count, in people receiving outpatient rehabilitation, in indoor and outdoor conditions, and (2) impact of slow walking speed on Fitbit accuracy. Design: Observational study. Setting: A metropolitan rehabilitation hospital. Subjects: Adults ( n = 88) attending a subacute rehabilitation outpatient clinic with walking speeds of between 0.4 and 1.0 m/s. Interventions: Two 2-minute walk tests, one indoors and one outdoors, completed in random order. Main measures: Step count recorded manually by observation and by a Fitbit Zip, attached to the shoe on the dominant or non-affected side. Subgroup analysis included assessment accuracy for those considered limited community walkers (slower than 0.8 m/s) and those considered community walkers (faster than 0.8 m/s). Results: The Fitbit significantly ( P < 0.05) undercounted steps compared to manual step count, indoors and outdoors, with percentage agreement slightly higher outdoors (mean 92.4%) than indoors (90.1%). Overall, there was excellent consistent agreement between the Fitbit and manual step count for both indoor (ICC 0.83) and outdoor (ICC 0.88) walks. The accuracy of the Fitbit was significantly ( P < 0.05) reduced in those who walked slower than 0.8 m/s outdoors (ICC 0.80) compared to those who walk faster than 0.8 m/s (ICC 0.90). Conclusions: The Fitbit Zip shows high step count accuracy with manual step count in a mixed subacute rehabilitation population. However, accuracy is affected by walking speed, with decreased accuracy in limited community walkers.

Using hand grip strength to detect slow walking speed in older adults: the Yilan study

Background Walking speed is an important health indicator in older adults, although its measurement can be challenging because of the functional decline due to aging and limited environment. The aim of this study was to examine whether hand grip strength can be a useful proxy for detecting slow walking speed in this population. Methods A cross-sectional study was conducted using the cohort from the Yilan Study in Taiwan. Community-dwelling older adults aged 65 years and older were included. Slow walking speed was defined as a 6-meter walking speed < 1.0 m/s, according to the 2019 Asian Working Group for Sarcopenia diagnostic criteria. Stepwise multiple linear regression was used to determine the most significant variables associated with walking speed. Receiver operating characteristic analysis was used to determine the optimal cutoff values for hand grip strength in detecting slow walking speed. Results A total of 301 participants with an average age of 73.9 ± 6.8 years were included; 55.1 % participants were women. In stepwise multiple linear regression analysis that included various variables, hand grip strength was found to be the most explainable factor associated with walking speed among all participants and among participants of each sex. The optimal cutoff values for hand grip strength in the detection of slow walking speed were 19.73 kg for all participants (sensitivity: 55 %, specificity: 83 %, area under the curve: 0.74, accuracy: 66.9 %), 35.10 kg for men (sensitivity: 92 %, specificity: 42 %, area under the curve: 0.70, accuracy: 66.4 %), and 17.93 kg for women (sensitivity: 62 %, specificity: 80 %, area under the curve: 0.76, accuracy: 67.9 %). Conclusions Hand grip strength was found to be a useful proxy for the identification of slow walking speed in older adults.

Identification of Disability Risk in Addition to Slow Walking Speed in Older Adults

<b><i>Introduction:</i></b> A cutoff speed of 1.0 m/s for walking at a comfortable pace is critical for predicting future functional decline. However, some older adults with walking speeds below the cutoff point maintain an independent living. We aimed to identify specific predictors of disability development in older adults with slow walking speeds in contrast to those with a normal walking speed. <b><i>Methods:</i></b> This prospective cohort study on 12,046 community-dwelling independent Japanese older adults (mean age, 73.6 ± 5.4 years) was conducted between 2011 and 2015. Participants were classified into slow walking speed (comfortable walking speed slower than 1.0 m/s) and normal walking speed (speed of 1.0 m/s or faster) groups and followed up to assess disability incidence for 24 months after baseline assessments. Cox proportional hazards regression models were used to identify predictors of disability development in the slow and normal walking groups. <b><i>Results:</i></b> Overall, 26.8% of participants had a slow walking speed. At follow-up, 17.3% and 5.1% of participants in the slow and normal walking groups, respectively, developed disability (<i>p</i> &#x3c; 0.01). Cox regression models revealed that age (hazard ratio 1.07, 95% confidence interval 1.05–1.09), walking speed (0.12, 0.07–0.22), grip strength (0.97, 0.95–0.99), Parkinson’s disease (4.65, 2.59–8.33), word list memory-immediate recognition score (0.90, 0.85–0.97), word list memory-delayed recall score (0.94, 0.89–1.00), Symbol Digit Substitution Test (SDST) score (0.98, 0.96–0.99), and 15-item Geriatric Depression Scale (GDS) score (1.04, 1.01–1.07) were significantly associated with disability incidence in the slow walking group. In the normal walking group, age, grip strength, depression, diabetes, cognition, GDS score, and reduced participation in outdoor activity were significantly associated with disability incidence; however, there was no significant association with walking speed. <b><i>Conclusions:</i></b> Decreased walking speeds have considerably greater impact on disability development in older adults with a slow walking speed than in those with a normal walking speed. Health-care providers should explore modifiable factors for reducing walking speed; they should also encourage improvement of risk factors such as muscle weakness and depression to reduce disability risk in older adults with slow walking speeds.

What cut-point in gait speed best discriminates community dwelling older adults with mobility complaints from those without? A pooled analysis from the Sarcopenia Definitions and Outcomes Consortium

Background Cut-points to define slow walking speed have largely been derived from expert opinion. Methods Study participants (13,589 men and 5,043 women aged ≥65years) had walking speed (m/s) measured over 4-6 meters (mean ± SD: 1.20 ± 0.27 m/s in men and 0.94 ± 0.24 m/s in women.) Mobility limitation was defined as self-reported any difficulty with walking ~1/4 mile (prevalence: 12.6% men, 26.4% women). Sex-stratified classification and regression tree (CART) models with 10-fold cross-validation identified walking speed cut-points that optimally discriminated those who reported mobility limitation from those who did not. Results Among 5,043 women, CART analysis identified two cut-points, classifying 4,144 (82.2%) with walking speed ≥0.75 m/s, which we labeled as “fast”; 478 (9.5%) as “intermediate” (walking speed ≥0.62 m/s but &lt;0.75 m/s); and 421 (8.3%) as “slow” (walking speed &lt;0.62 m/s). Among 13,589 men, CART analysis identified three cut-points, classifying 10,001 (73.6%) with walking speed ≥1.00 m/s (“very fast”); 2,901 (21.3%) as “fast” (walking speed ≥0.74 m/s but &lt;1.00 m/s); 497 (3.7%) as “intermediate” (walking speed ≥0.57 m/s but &lt;0.74 m/s); and 190 (1.4%) as “slow” (walking speed &lt;0.57 m/s). Prevalence of self-reported mobility limitation was lowest in the “fast” or “very fast” (11% for men and 19% for women) and highest in the “slow” (60.5% in men and 71.0% in women). Rounding the two slower cut-points to 0.60 m/s and 0.75 m/s reclassified very few participants. Conclusions Cut-points in walking speed of ~0.60 m/s and 0.75 m/s discriminate those with self-reported mobility limitation from those without.