This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.

BackgroundLatin dance consists of various fast and stability-challenging movements that require constant body adjustments to maintain proper posture and balance. Although human gaits are assumed to be symmetrical, several factors can contribute to asymmetrical behavior of the lower extremities in healthy adults. These include lower limb dominance, ground reaction forces, lower limb muscle power, foot placement angle, and range of joint motion. Gait impairment can lead to a high risk of falling, diminished mobility, and even cognition impairment. We hypothesized that Latin dancers might have a more symmetric gait pattern and better balance ability than healthy non-dancer controls.MethodsWe investigated the impact of Latin dance training on gait behaviors and body balance. We recruited twenty Latin dancers and 22 normal healthy subjects to conduct walking experiments and one-leg stance tests, and we measured their kinematic data by inertial measurement units. We then defined four performance indexes to assess gait performance and body stability to quantify the potential advantages of dance training.ResultsWe found that the two gait asymmetric indexes during the walking test and the two performance indexes during the one-leg stance tests were better in Latin dancers compared with the healthy control group. The results confirmed the superiority of Latin dancers over the healthy control group in gait symmetry and balance stability. Our results suggest that Latin dancing training could effectively strengthen lower limb muscles and core muscle groups, thereby improving coordination and enhancing gait performance and balance.ConclusionLatin dance training can benefit gait performance and body balance. Further studies are needed to investigate the effect of Latin dance training on gait and balance outcomes in healthy subjects and patients with gait disorders.

This paper presents a clinical rehabilitation protocol for stroke patients using a movable trainer, which can automatically execute a neurodevelopmental treatment (NDT) intervention based on key gait events. The trainer consists of gait detection and motor control systems. The gait detection system applied recurrent neural networks (RNNs) to recognize important gait events in real time to trigger the motor control system to repeat the NDT intervention. This paper proposes a modified intervention method that simultaneously improves the user’s gait symmetry and pelvic rotation. We recruited ten healthy subjects and had them wear a rehabilitation gaiter on one knee joint to mimic stroke gaits for verification of the effectiveness of the trainer. We used the RNN model and a modified intervention method to increase the trainer’s effectiveness in improving gait symmetry and pelvic rotation. We then invited ten stroke patients to participate in the experiments, and we found improvement in gait symmetry in 80% and 90% of the patients during and after the training, respectively. Similarly, pelvic rotation improved in 80% of the patients during and after the training. These findings confirmed that the movable NDT trainer could improve gait performance for the rehabilitation of stroke patients.

This study investigates gait symmetry and single-leg stance balance of professional yoga instructors versus age-matched typically developed controls using inertial measurement unit (IMU)-based evaluation. We recruited twenty-five yoga instructors and twenty-five healthy control subjects to conduct the walking experiments and single-leg stance tests. Kinematic data were measured by attaching IMUs to the lower limbs and trunk. We assessed the asymmetry of swing phases during the normal-walk and tandem-walk tests with eyes open and closed, respectively. The subjects subsequently conducted four single-leg stance tests, including a single-leg stance on both legs with eyes open and closed. Two balance indexes regarding the angular velocities of the waist and chest were defined to assess postural stability. The gait asymmetry indexes of yoga instructors were significantly lower than those of the typically developed controls. Similarly, the yoga instructors had better body balance in all four single-leg stance tests. This study’s findings suggest that yoga improves gait asymmetry and balance ability in healthy adults. In the future, further intervention studies could be conducted to confirm the effect of yoga training.

Background Yoga movements involve a series of motions of the core and lower limb muscles that require constant body adjustments to maintain balance and proper body alignment. Inertial measurement unit, a wearable device that is consisted of 3-axis accelerometers, 3-axis gyroscopes and 3-axis magnetometers, can provide objective data for motion analysis. This study aimed to investigate gait symmetry and single-leg stance balance of professional yoga instructors versus age-matched normal controls using inertial measurement unit (IMU) - based evaluation. Methods Twenty-five yoga instructors and twenty-five healthy control subjects were recruited to conduct the walking experiments and single-leg stance tests. Kinematic data were measured by attaching IMUs to the lower limbs and trunk. The asymmetry of swing phases during gait cycles was assessed using the normal walk and tandem walk tests with eyes open and closed, respectively. The subjects subsequently conducted four single-leg stance tests, including a single-leg stance on both legs with eyes open and closed. Two balance indexes regarding the angular velocities of the waist and chest were defined to assess postural stability. Results The gait asymmetry indexes of yoga instructors were significantly lower than those of the normal controls on normal walk and open-eye tandem walk tests. The asymmetry indexes also showed a smaller value in the yoga instructors on close-eye tandem gait; however, it did not reach statistical significance. Similarly, the yoga instructors had better body balance, that is, smaller angular velocities on both the waist and chest, in all four single-leg stance tests. This indicates superior postural stability over both the waist and chest levels of yoga instructors during the single-leg stance. Conclusion The findings of this study suggest that yoga practice improves gait asymmetry and balance ability in healthy adults. However, further intervention studies are needed to confirm the effect of yoga training. Trial Registration: This study was registered with an ClinicalTrials.gov Identifier: NCT05449730.