Variability, asymmetry, and bilateral coordination of gait during single- and dual-task walking of patients with cerebral small vessel disease

OBJECTIVE

We investigated coefficient of variation (CV), gait asymmetry (GA) and phase coordination index (PCI) in CSVD (cerebral small vessel disease) patients during STW (single task walking) and DTW (dual task walking) and explored the relationship between above parameters with disease severity and cognitive function.

METHODS

This cross-sectional study collected cognitive function indices and gait parameters from 23 healthy controls and 94 patients with CSVD during STW and DTW. According to the Fazekas scales, the severity of CSVD valued by white matter hyperintensity (WMH) were divided into control, mild, moderate, severe and control group. MRIs were analyzed for WMHs, CMB, lacunes, etc.

RESULTS

The control group showed lower PCI than CSVD patients during STW; no differences were detected among the disease severity groups. During DTW, all four groups exhibited significant differences in PCI and CV. For the moderate and severe groups, coordination and variation significantly differed between the two walking methods. There were correlations between the PCI and GA in the moderate and severe groups (R = 0.376, R = 0.573 during DTW; R = 0.414, R = 0.643 during STW), and no correlations in the control group and mild CSVD group. Conclusion: PCI and CV may be vital for detecting the symptoms in the early stage of CSVD disease. We also verified that the PCI could become the bridge across the cognition and motor disorder in CSVD, which was helpful for evaluating clinical symptoms comprehensively.

Smartwatch gait coordination index: New measure for human gait utilizing smartwatch sensor

Human walking reflects the state of human health. Numerous medical studies have been conducted to analyze walking patterns and to diagnose disease progression. However, this process requires expensive equipment and considerable time and manpower. Smartwatches are equipped with gyro sensors to detect human movements and graph-walking patterns. To measure the abnormality in walking using this graph, we developed a smartwatch gait coordination index (SGCI) and examined its usefulness. The phase coordination index was applied to analyze arm movements. Based on previous studies, the phase coordination index formula was applied to graphs obtained from arm movements, showing that arm and leg movements during walking are correlated with each other. To prove this, a smartwatch was worn on the arms and legs of 8 healthy adults and the difference in arm movements was measured. The SGCI values with abnormal walking patterns were compared with the SGCI values obtained during normal walking. In the first experiment, the measured leg SGCI in normal walking averaged 9.002 ± 3.872 and the arm SGCI averaged 9.847 ± 6.115. The movements of both arms and legs showed stable sinusoidal waves. In fact, as a result of performing a paired t test of both exercise phases measured by the strike point using the maximum and minimum values, it was confirmed that the 2 exercises were not statistically different, as it yielded a P value of 0.469 (significance level α = 0.05). The arm SGCI measured after applying the 3 kg weight impairment on 1 leg was 22.167 ± 4.705. It was confirmed that the leg SGCI and 3 kg weight arm SGCI were statistically significant, as it yielded a P value of 0.001 (significance level α = 0.05). The SCGI can be automatically and continuously measured with the gyro sensor of the smartwatch and can be used as an indirect indicator of human walking conditions.

Interlimb Coordination Performance in Seated Position in Persons With Multiple Sclerosis: Reduced Amplitude Over 6 min and Higher Coordination Variability in Persons With Walking Fatigability

Background: Walking fatigability is prevalent in MS and can be measured by a percentage distance decline during a 6-min walking test. Walking is characterized by an accurate and consistent interlimb antiphase coordination pattern. A decline in coordination each minute during a 6-min walking test is observed in persons with MS (pwMS). Measuring coordination during a 6-min seated coordination task with minimized balance and strength requirements, is assumed to examine a more fundamental interlimb antiphase coordination pattern in pwMS. This research aimed to answer the following research question: How does interlimb antiphase coordination pattern change during a seated coordination task in pwMS with walking fatigability (WF), non-walking fatigability (NWF) and Healthy Controls (HC)?

Methods: Thirty-five pwMS and 13 HC participated. Interlimb coordination was assessed by a seated 6-min coordination task (6MCT) with the instruction to perform antiphase lower leg movements as fast as possible. Outcomes were Phase Coordination Index (PCI) and movement parameters (amplitude, frequency).

Results: Mixed models revealed a significant effect of time for the the variability of generating interlimb movements, with a difference in mean values between WF and HC. A significant group^∗time interaction effect was found for movement amplitude, represented by a significant decrease in movement amplitude in the WF group from minute 1 to the end of the task.

Conclusion: The higher variability in interlimb coordination and decrease in movement amplitude over time during the 6MCT in the WF group could be an indicator of decreased control of fundamental antiphase coordination pattern in pwMS with walking fatigability.

Clinical Trial Registration:www.clinicaltrials.gov, identifier NCT04142853 (registration date: October 29, 2019) and NCT03938558 (registration date: May 6, 2019).

Soft-Material-Based Smart Insoles for a Gait Monitoring System

Spatiotemporal analysis of gait pattern is meaningful in diagnosing and prognosing foot and lower extremity musculoskeletal pathologies. Wearable smart sensors enable continuous real-time monitoring of gait, during daily life, without visiting clinics and the use of costly equipment. The purpose of this study was to develop a light-weight, durable, wireless, soft-material-based smart insole (SMSI) and examine its range of feasibility for real-time gait pattern analysis. A total of fifteen healthy adults (male: 10, female: 5, age 25.1 ± 2.64) were recruited for this study. Performance evaluation of the developed insole sensor was first executed by comparing the signal accuracy level between the SMSI and an F-scan. Gait data were simultaneously collected by two sensors for 3 min, on a treadmill, at a fixed speed. Each participant walked for four times, randomly, at the speed of 1.5 km/h (C1), 2.5 km/h (C2), 3.5 km/h (C3), and 4.5 km/h (C4). Step count from the two sensors resulted in 100% correlation in all four gait speed conditions (C1: 89 ± 7.4, C2: 113 ± 6.24, C3: 141 ± 9.74, and C4: 163 ± 7.38 steps). Stride-time was concurrently determined and R2 values showed a high correlation between the two sensors, in both feet (R² ≥ 0.90, p < 0.05). Bilateral gait coordination analysis using phase coordination index (PCI) was performed to test clinical feasibility. PCI values of the SMSI resulted in 1.75 ± 0.80% (C1), 1.72 ± 0.81% (C2), 1.72 ± 0.79% (C3), and 1.73 ± 0.80% (C4), and those of the F-scan resulted in 1.66 ± 0.66%, 1.70 ± 0.66%, 1.67 ± 0.62%, and 1.70 ± 0.62%, respectively, showing the presence of a high correlation (R² ≥ 0.94, p < 0.05). The insole developed in this study was found to have an equivalent performance to commercial sensors, and thus, can be used not only for future sensor-based monitoring device development studies but also in clinical setting for patient gait evaluations.