Pelvis-Toe Distance: 3-Dimensional Gait Characteristics of Functional Limb Shortening in Hemiparetic Stroke

We aimed to investigate whether a newly defined distance in the lower limb can capture the characteristics of hemiplegic gait compared to healthy controls. Three-dimensional gait analyses were performed on 42 patients with chronic stroke and 10 age-matched controls. Pelvis-toe distance (PTD) was calculated as the absolute distance between an anterior superior iliac spine marker and a toe marker during gait normalized by PTD in the bipedal stance. The shortening peak during the swing phase was then quantified as PTDmin. The sagittal clearance angle, the frontal compensatory angle, gait speed, and the observational gait scale were also collected. PTDmin in the stroke group showed less shortening on the affected side and excessive shortening on the non-affected side compared to controls. PTDmin on the affected side correlated negatively with the sagittal clearance peak angle and positively with the frontal compensatory peak angle in the stroke group. PTDmin in stroke patients showed moderate to high correlations with gait speed and observational gait scale. PTDmin adequately reflected gait quality without being affected by apparent improvements due to frontal compensatory patterns. Our results showed that various impairments and compensations were included in the inability to shorten PTD, which can provide new perspectives on gait rehabilitation in stroke patients.

Machine-learning-based hemiplegic-gait detection using an inertial sensor located freely in a pocket (Preprint)

BACKGROUND In most previous studies, the acceleration sensor is attached to a fixed position for gait analysis. However, if it is aimed at daily use, wearing it in a fixed position may cause discomfort. In addition, since an acceleration sensor can be built into the smartphones that people always carry, it is more efficient to use such a sensor rather than wear a separate acceleration sensor. OBJECTIVE We aim to distinguish between hemiplegic and normal walking by using the inertial signal measured by means of an acceleration sensor and a gyroscope. METHODS We used a machine-learning model based on a convolutional neural network to classify hemiplegic gaits, and used the acceleration and angular velocity signals obtained from the system freely located in the pocket as inputs without any pre-processing. We evaluated the performance of the developed model by means of a clinical trial including a walking test on 42 subjects (57.8 ± 13.8 years, 165.1 ± 9.3 cm, 66.3 ± 12.3 kg) including 21 hemiplegic patients. RESULTS The developed model showed an accuracy of 0.86, a precision of 0.90, a recall of 0.99, an area under the receiver operating characteristic curve of 0.91, and an area under the precision-recall curve of 0.97. CONCLUSIONS We confirmed the possibility of distinguishing hemiplegic gaits by means of a machine-learning model without additional pre-processing or feature extraction, using a 6-axis inertial signal measured at random locations in a pocket, like a smartphone. CLINICALTRIAL SCH2016-130

Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG-Based Brain Network

Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Time-domain analysis, power spectrum analysis, time-frequency analysis, brain network analysis, and statistical analysis were used to explore the neural mechanisms of motor coordination imagery involving the lower limbs. Then, EEG-based brain network features were extracted, and a support vector machine was used for decoding. The results showed that the two employed motor coordination imageries mainly activated sensorimotor areas; the frequency band power was mainly concentrated within theta and alpha bands, and brain functional connections mainly occurred in the right forehead. The combination of the network attributes of the EEG-based brain network and the spatial features of the adjacency matrix had good separability for the two kinds of gait imagery ( p < 0.05), and the average classification accuracy of the combination feature was 92.96% ± 7.54%. Taken together, our findings suggest that brain network features can be used to identify normal gait imagery and hemiplegic gait imagery after stroke.

Effect of Cognitive Function on Balance and Posture Control after Stroke

Hemiplegic gait is the most common sequela of stroke. Patients with hemiplegic gait are at a risk of falling because of poor balance. The theory of cognitive-motor networks paved the way for a new field of research. However, the mechanism of the relationship of cognition with gait or posture control networks is unclear because of the dynamic characteristics of walking and changing postures. To explore differences in the balance function and fall risk between patients with and without cognitive impairment after stroke, we utilized the Berg balance scale, Timed “Up and Go” test, and 10 m walking test. Patients were divided into two groups: the observation group (16 patients, female 6 and male 10), comprising patients with cognitive impairment after stroke, and the control group (16 patients, female 7 and male 9), comprising patients without cognitive impairment after stroke. We found that patients with cognitive impairment had worse balance function and a higher risk of falls. They needed a longer time to turn around or sit down. Our findings indicated that posture control in turning around and sitting down required more cognitive resources in daily life.

Promoting hemiplegic gait

Introduction and Aim: Balance synergy includes a number of postural response that enable an individual to arise and remain erect during standing and locomotion. Balance deficits causes an insufficient coordination, postural instability and impaired gait. Maintaining the independence in activities of activities of daily living is an important factor for the quality of life. Hence this study is done to promote Hemiplegic Gait in stroke patients. Materials and Methods: 150 community dwelling hemiparetic subjects from Chennai aged <65 both male and female subjects were allocated in three groups (n=50) using convenient sampling method and were followed up for a period of 12 weeks with intervention duration of 45 mins daily and were assessed with POMA, FALL RISK , TUG and 6 minute walk test. Paired‘t’ test was used for assessing pre and post-test values. Results: There was significant difference with p < 0.001 at both Tinetti Performance oriented mobility assessment, Time up and Go Test, Fall Risk and 6 minute walk test minimal significant difference in chair stand test in both the groups. Conclusion: There was significant difference in POMA, FALL RISK , TUG and 6 minute walk test p<.005 in Group C & Group B.

Rhythmic Auditory Stimulation in Health Care

One of the primary goals of rehabilitation is to restore mobility through gait training. Recent advances in technology and a culmination of medical research have revealed the role of auditory stimulation as an internalized time keeper for rhythmic patterned movements. The aim of this review was to investigate research on rhythmic auditory stimulation (RAS) in specific conditions in health care. Numerous studies using RAS has been shown to improve gait patterns in hemiplegic gait, parkinson’s disease, traumatic brain injuries, cerebral palsy and Huntington’s disease, but no changes in spinal cord injuries.