Biomechanical Correlates of Falls Risk in Gait Impaired Stroke Survivors

Predicting improvement in biofeedback gait training using short-term spectral features from minimum foot clearance data

Stroke rehabilitation interventions require multiple training sessions and repeated assessments to evaluate the improvements from training. Biofeedback-based treadmill training often involves 10 or more sessions to determine its effectiveness. The training and assessment process incurs time, labor, and cost to determine whether the training produces positive outcomes. Predicting the effectiveness of gait training based on baseline minimum foot clearance (MFC) data would be highly beneficial, potentially saving resources, costs, and patient time. This work proposes novel features using the Short-term Fourier Transform (STFT)-based magnitude spectrum of MFC data to predict the effectiveness of biofeedback training. This approach enables tracking non-stationary dynamics and capturing stride-to-stride MFC value fluctuations, providing a compact representation for efficient processing compared to time-domain analysis alone. The proposed STFT-based features outperform existing wavelet, histogram, and Poincaré-based features with a maximum accuracy of 95%, F1 score of 96%, sensitivity of 93.33% and specificity of 100%. The proposed features are also statistically significant (p < 0.001) compared to the descriptive statistical features extracted from the MFC series and the tone and entropy features extracted from the MFC percentage index series. The study found that short-term spectral components and the windowed mean value (DC value) possess predictive capabilities regarding the success of biofeedback training. The higher spectral amplitude and lower variance in the lower frequency zone indicate lower chances of improvement, while the lower spectral amplitude and higher variance indicate higher chances of improvement.

Effects of gait adaptation training on augmented reality treadmill for patients with stroke in community ambulation

Gait adaptability is essential for stroke survivors to achieve efficient and safe community ambulation. However, conventional treadmill rehabilitation is only a repetitive practice of leg movement. This study compared the effects of augmented reality treadmill-based gait adaptation training with regular treadmill programs for patients with stroke. Forty patients with stroke (n = 40) were randomly assigned to the gait adaptation training {n = 20, age: 49.85 [standard deviation (SD) 8.44] years; onset of stroke: 107.80 (SD 48.31) days} and regular training [n = 20, age: 50.75 (SD 8.05) years, onset of stroke: 111.60 (SD 49.62) days] groups. Both groups completed three sessions of training per week for 5 weeks (15 sessions). The primary outcomes were the 10-m walk test and success rate of obstacle avoidance, while secondary outcomes included the Berg balance scale, component timed-up-and-go, and fall rate in a 6-month follow-up period. Assessments were performed before and after the intervention. The paired t-test was applied to compare the differences within groups and independent sample t-test was performed to compare the differences between groups. The 10-m walk test, success rate of obstacle avoidance, Berg balance scale, and component timed-up-and-go all significantly improved in the both groups (P < .001). The success rate of obstacle avoidance [P = .02, 95% confidence interval (CI): -21.07, -1.64], Berg Balance Scale (P = .02, 95% CI: -8.03, -0.67), 'turning around time' (P = .04, 95% CI: 0.08, 2.81), 'stand-to-sit' (P = .03, 95% CI: 0.16, 2.41) and 'total time' (P = .048, 95% CI: 0.04, 10.32) improved significantly in gait adaptation training group after intervention, while the 10-m walk test (P = .09, 95% CI: -0.17, 0.01), timed 'sit-to-stand' (P = .09, 95% CI: -0.14, 2.04), and 'linear walking' (P = .09, 95% CI: -0.27, 3.25) in gait adaptation training group did not show statistical difference compared to the regular training group. Total fall rate during the follow-up period was statistically decreased in the gait adaptation training group (P = .045). Both interventions improved mobility outcomes, with augmented reality treadmill-based gait adaptation indicating greater improvement in obstacle avoidance, balance, turning, and stand-to-sit. Augmented reality treadmill-based gait adaptation training emerges as an effective and promising intervention for patients with stroke in early rehabilitation.

Gait Biomechanics for Fall Prevention among Older Adults

In our currently ageing society, fall prevention is important for better healthy life expectancy and sustainable healthcare systems. While active outdoor walking is recommended as adequate exercise for the senior population, falls due to tripping and slipping exist as the primary causes of severe injuries. Minimum foot clearance (MFC) is the lowest vertical height of the foot during the mid-swing phase and indicates the risk of tripping. In contrast, coefficient of friction (COF) factors determine the occurrence of falls from slipping. Optimisation of the MFC and the COF for every step cycle prevents tripping and slipping, respectively. Even after the initiation of hazardous balance loss (i.e., tripping and slipping), falls can still be prevented as long as the requirements for balance are restored. Biomechanically, dynamic balance is defined by the bodily centre of mass and by the base of support: spatially—margin of stability and temporally—available response time. Fall prevention strategies should, therefore, target controlling the MFC, the COF and dynamic balance. Practical intervention strategies include footwear modification (i.e., shoe-insole geometry and slip-resistant outsoles), exercise (i.e., ankle dorsiflexors and core stabilisers) and technological rehabilitation (i.e., electrical stimulators and active exoskeletons). Biomechanical concepts can be practically applied to various everyday settings for fall prevention among the older population.