Changing coupling between the arms and legs with slow walking speeds alters regulation of somatosensory feedback

Defining characteristics of independent walking persons after stroke presenting with different arm swing coordination patterns

BACKGROUND

Persons after stroke present with an altered arm swing during walking. Given the known influence of the arm swing on gait, it is important to identify the characteristics of persons with stroke with different arm-to-leg coordination patterns during walking.

METHODS

Twenty-five persons after stroke walked on a self-paced treadmill at comfortable walking speed. The frequency of shoulder movements per stride was detected by Fast Fourier transform analysis on the kinematic data for hemiplegic shoulder movements in the sagittal plane. An independent-sample t-test or Mann-Whitney U test was used to compare clinical and biomechanical parameters between identified subgroups.

RESULTS

Two earlier described subgroups based on the number of shoulder flexion-extension movements during one stride could be confirmed. Participants in the 1:1 ratio subgroup (one arm swing during one stride, N = 15) presented with a less upper limb impairment and less spasticity of the elbow extensors (p = 0.012) than the participants in the 2:1 ratio subgroup (two arm swings during one stride, N = 9). Although not significant, the participants in the 1:1 subgroup also seemed to have less spasticity of the shoulder internal rotators (p = 0.06) and a less walking variability based on the standard deviation of the step width. Further research on a greater sample should confirm these findings.

CONCLUSION

Fast Fourier transform analysis was used to identify subgroups based on sagittal shoulder kinematics during walking. The clinical and gait related differences between the identified subgroups can be taken into account in future research investigating post-stroke gait interventions aiming to improve the arm swing.

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.

Mechanical Design and Control System Development of a Rehabilitation Robotic System for Walking With Arm Swing

Background: Interlimb neural coupling implies that arm swing should be included during gait training to improve rehabilitation outcomes. We previously developed several systems for production of walking with arm swing, but the reaction forces on the foot sole during usage of the systems were not satisfactory and there was potential to improve control system performance. This work aimed to design and technically evaluate a novel system for producing walking with synchronised arm and leg movement and with dynamic force loading on the foot soles.

Methods: The robotic system included a passive curved treadmill and a trunk frame, upon which the rigs for the upper and lower limbs were mounted. Ten actuators and servocontrollers with EtherCAT communication protocol controlled the bilateral shoulder, elbow, hip, knee and ankle joints. Impedance control algorithms were developed and ran in an industrial PC. Flexible pressure sensors recorded the plantar forces on the foot soles. The criteria of implementation and responsiveness were used to formally evaluate the technical feasibility of the system.

Results: Using impedance algorithms, the system produced synchronous walking with arm swing on the curved treadmill, with mean RMS angular tracking error <2° in the 10 joint profiles. The foot trajectories relative to the hip presented similar shapes to those during normal gait, with mean RMS displacement error <1.5 cm. A force pattern that started at the heel and finished at the forefoot was observed during walking using the system, which was similar to the pattern from overground walking.

Conclusion: The robotic system produced walking-like kinematics in the 10 joints and in the foot trajectories. Integrated with the curved treadmill, the system also produced walking-like force patterns on the foot soles. The system is considered feasible as far as implementation and responsiveness are concerned. Future work will focus on improvement of the mechanical system for future clinical application.