Improvement of Gait in Patients with Stroke Using Rhythmic Sensory Stimulation: A Case-Control Study

Patients with stroke suffer from impaired locomotion, exhibiting unstable walking with increased gait variability. Effects of rhythmic sensory stimulation on unstable gait of patients with chronic stroke are unclear. This study aims to determine the effects of rhythmic sensory stimulation on the gait of patients with chronic stroke. Twenty older adults with stroke and twenty age- and gender-matched healthy controls walked 60 m under four conditions: normal walking with no stimulation, walking with rhythmic auditory stimulation (RAS) through an earphone in the ear, walking with rhythmic somatosensory stimulation (RSS) through a haptic device on the wrist of each participant, and walking with rhythmic combined stimulation (RCS: RAS + RSS). Gait performance in the stroke group significantly improved during walking with RAS, RSS, and RCS compared to that during normal walking (p < 0.008). Gait variability significantly decreased under the RAS, RSS, and RCS conditions compared to that during normal walking (p < 0.008). Rhythmic sensory stimulation is effective in improving the gait of patients with chronic stroke, regardless of the type of rhythmic stimuli, compared to healthy controls. The effect was greater in patients with reduced mobility, assessed by the Rivermead Mobility Index (RMI).

An Advanced Attention-Enhanced Hybrid Deep Learning Model for WBSNs’ Gait Pattern Recognition

Background: The deep learning techniques have been attracted increasing attention on wireless body sensor networks (WBSNs) gait pattern recognition that has a great contribution to monitoring gait change in clinical application. However, in existing studies, there are some challenging issues such as low generalization performance and no potential interpretation for gait variability. It is necessary to search for the advanced deep learning models to resolve these issues. Method: A public WARD database including acceleration and gyroscope data acquired from each subject wearing five sensors was selected, and the gait with different combination of on-body multi-sensors is considered as a WBSNs’ gait pattern. An advanced attention-enhanced hybrid deep learning model of DCNN and LSTM for WBSNs’ gait pattern recognition was proposed. In our proposed technique, the combination model of DCNN with LSTM is firstly to discover the spatial-temporary gait correlation features. And then the attention mechanism is introduced to exploit the more valuable intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability hidden in spatial-temporary gait space obtained. This significantly contributes to enhancing the generalization performance and taking insight on gait variability in a certain anatomical region. Results: The ten gait patterns are randomly selected from WARD database to evaluate the feasibility of our proposed method. Our experiments demonstrated the superior generalization ability of our method to some models such as CNN-LSTM, DCNN-LSTM. Our proposed model could classify ten gait patterns with the highest accuracy and F1-score of 91.48% and 91.46%, respectively. Moreover, we also found that the classification performance of a certain gait pattern was almost same best when the combinations of three or five on-body sensors were employed respectively, suggesting that our method possibly take insight on gait variability in a certain anatomical region. Conclusion: Our proposed technique could feasibly discover the more intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability from on-body multi-sensors gait data, which greatly contributed to best generalization performance and potential clinical interpretation. Our proposed technique would hopefully become a powerful tool of monitoring gait change in clinical application.

Spatiotemporal Parameters And Gait Variability In People With Psoriatic Arthritis (Psa) : A Cross-Sectional Study.

Background: Foot involvement is a major manifestation of psoriatic arthritis (PsA) and could lead to severe levels of foot pain and disability and impaired functional mobility and quality of life. Gait spatiotemporal parameters (STPs) and gait variability, used as a clinical index of gait stability, have been associated with several adverse health outcomes including risk of falling, functional decline, and mortality in a wide range of populations. Previous studies showed some alterations in STPs in people with PsA. However, gait variability and the relationships between STPs, gait variability and self-reported foot pain and disability have never been studied in this populations. Body-worn inertial measurement units (IMUs) are gaining interest in measuring gait parameters in clinical settings.Objectives: To assess STPs and gait variability in people with PsA using IMUs and, to explore their relationship with self-reported foot pain and function and to investigate the feasibility of using IMUs to discriminate patient groups based on gait speed-critical values.Methods: 21 participants with PsA (Age: 53.9 ± 8.9 yrs; median disease duration: 6 yrs) and 21 age and gender-matched healthy participants (Age 54.23 ± 9.3 yrs) were recruited. All the participants performed three 10-meter walk test trials at their comfortable speed. STPs and gait variability were recorded and calculated using six body-worn IMUs and the Mobility Lab software (APDM®). Foot pain and disability were assessed in participants with PsA using the foot function index (FFI).Results: Cadence, gait speed, stride length, and swing phase, were significantly lower, while double support was significantly higher, in the PsA group (p< 0.006). Strong correlations between STPs and the FFI total score were demonstrated (|r|> 0.57, p< 0.006). Gait variability was significantly increased in the PsA group, but it was not correlated with foot pain and function (p< 0.006). Using the IMUs three subgroups of participants with PsA with clinically meaningful differences in self-reported foot pain and disability were discriminated.Conclusion: STPs were significantly altered in participants with PsA which could be associated with self-reported foot pain and disability. Future studies are required to confirm the increased gait variability highlighted in this study and its potential underlying causes. Using IMUs in clinical settings has been useful to objectively assess foot function in people with PsA. Study registration: ClinicalTrials.gov, NCT05075343, Retrospectively registered on 29 September 2021.

Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke

Gait disturbance is a common sequela of stroke. Conventional gait analysis has limitations in simultaneously assessing multiple joints. Therefore, we investigated the gait characteristics in stroke patients using hip-knee cyclograms, which have the advantage of simultaneously visualizing the gait kinematics of multiple joints. Stroke patients (n = 47) were categorized into two groups according to stroke severity, and healthy controls (n = 32) were recruited. An inertial measurement unit sensor-based gait analysis system, which requires placing seven sensors on the dorsum of both feet, the shafts of both tibias, the middle of both femurs, and the lower abdomen, was used for the gait analysis. Then, the hip-knee cyclogram parameters (range of motion, perimeter, and area) were obtained from the collected data. The coefficient of variance of the cyclogram parameters was obtained to evaluate gait variability. The cyclogram parameters differed between the stroke patients and healthy controls, and differences according to stroke severity were also observed. The gait variability parameters mainly differed in patients with more severe stroke, and specific visualized gait patterns of stroke patients were obtained through cyclograms. In conclusion, the hip-knee cyclograms, which show inter-joint coordination and visualized gait cycle in stroke patients, are clinically significant.

The effect of a multi-modal boxing exercise program on cognitive locomotor tasks and gait in persons with Parkinson disease

BACKGROUND: Parkinson disease (PD) is a progressive neurological disease resulting in motor impairments, postural instability, and gait alterations which may result in self-care limitations and loss of mobility reducing quality of life. OBJECTIVE: This study’s purpose was to determine the impact of a community-based boxing program on gait parameters, dual task and backwards walking in individuals with PD. METHODS: This study included 26 community dwelling individuals with PD who participated in 12-week boxing classes (1 hour, 2 times a week). The focus was on upper/lower extremity exercises using punching bags, agility drills, and strengthening activities. Pre/post testing was performed for dual task and gait parameters and was analyzed using t-tests. RESULTS: Analysis of the scores indicated participants performed significantly better at post-test compared to pre-test on self-selected walking velocity (P = 0.041), cadence (P = 0.021); backwards walking velocity (P = 0.003), step length (P = 0.022); dual task walking velocity (P = 0.044), step length (P = 0.023), and gait variability index (P = 0.008). No significant differences for fast walking. CONCLUSIONS: Multi-modal boxing produced improvements in gait velocity, dual task velocity, step length, and gait variability, as well as backwards walking velocity and step length. These improvements may impact independence with functional mobility and may improve safety but require further studies.

ANALYSIS OF THE CORRELATION BETWEEN MUSCLE REACTION AND STRIDE INTERVAL VARIABILITY IN SINGLE-TASK AND DUAL-TASK WALKING

Analysis of leg muscle activation and gait variability during locomotion is an important area of research in physiological and sport sciences. In this paper, we analyzed the coupling between the alterations of leg muscle activation and gait variability in single-task and dual-task walking. Since leg muscle activation in the form of electromyogram (EMG) signals and gait variability in the form of stride interval time series have complex structures, fractal theory and approximate entropy were used to evaluate their correlation at various walking conditions. Sixty subjects walked at their preferred speed for 10 min under the single-task condition and for 90[Formula: see text]s under the cognitive dual-task condition, and we evaluated the variations of the fractal dimension and approximate entropy of EMG signals and stride interval time series. According to the results, dual-task walking caused reductions in the complexity of EMG signals and stride interval time series than single-task walking. This technique can be used to evaluate the correlation between other organs during different locomotion.