Developing safe fall strategies for lower limb exoskeletons

Decoding Neural Activity Preceding Balance Loss During Standing with a Lower-limb Exoskeleton using an Interpretable Deep Learning Model

Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall. To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from 7 healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials. We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼ 180 ms) and the COP (∼ 350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3 %. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06. Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.

Stress analysis of the thoracolumbar junction in the process of backward fall: An experimental study and finite element analysis

The aim of the present study was to evaluate the biomechanical mechanism of injuries of the thoracolumbar junction by the methods of a backward fall simulation experiment and finite element (FE) analysis (FEA). In the backward fall simulation experiment, one volunteer was selected to obtain the contact force data of the sacrococcygeal region during a fall. Utilizing the fall data, the FEA simulation of the backward fall process was given to the trunk FE model to obtain the stress status of local bone structures of the thoracolumbar junction during the fall process. In the fall simulation test, the sacrococcygeal region of the volunteer landed first; the total impact time was 1.14±0.58 sec, and the impact force was up to 4,056±263 N. The stress of thoracic (T)11 was as high as 42 MPa, that of the posterior margin and the junction of T11 was as high as 70.67 MPa, and that of the inferior articular process and the superior articular process was as high as 128 MPa. The average stress of T12 and the anterior margin of lumbar 1 was 25 MPa, and that of the endplate was as high as 21.7 MPa, which was mostly distributed in the back of the endplate and the surrounding cortex. According to the data obtained from the fall experiment as the loading condition of the FE model, the backward fall process can be simulated to improve the accuracy of FEA results. In the process of backward fall, the front edge of the vertebral body and the root of vertebral arch in the thoracolumbar junction are stress concentration areas, which have a greater risk of injury.

Walking with UAN.GO Exoskeleton: Training and Compliance in a Multiple Sclerosis Patient

BACKGROUND

Multiple sclerosis is a progressive neurodegenerative disease that affects myelin in the central nervous system. It is complex and unpredictable and occurs predominantly in young adults, causing increasing disability and a significantly lower quality of life. Recent studies investigated how rehabilitation training through the use of a robotic exoskeleton can influence walking recovery in patients with a serious neurological disease.

AIM

The purpose of this study was to analyze the first approach of a multiple sclerosis patient to a robotic exoskeleton for the lower limbs, in order to assess the effectiveness of the protocol on walking ability, adaptability of the device, level of appreciation, variations in parameters related to walking, and fatigue perception.

METHODS

This study was conducted on a 71-year-old male diagnosed with primary progressive multiple sclerosis since 2012, with an EDSS score of 6. The patient underwent a cycle of 10 sessions of treatment with the exoskeleton for the lower limbs, the UAN.GO, lasting 1 h 30 min. Pre- and post-treatment evaluations were carried out with the 6 min walking test, the Fatigue Severity Scale, the Short Form-36 Health Survey, and a Likert scale for review. During each session, blood pressure, heart rate, and peripheral saturation were monitored; in addition, the perception of fatigue by the Borg scale was studied.

RESULT

A comparison between the initial and final evaluations showed improvements in the walked distance at 6 MWT (T0 = 53 m/T1 = 61 m). There was a positive trend in saturation and heart rate values collected during each session. Further improvements were found by the Borg scale (T0 = 15/T1 = 11).

DISCUSSION

The data collected in this case report show promising results regarding the treatment of multiple sclerosis patients with the UAN.GO exoskeleton, with benefits on both motor performance and vital parameters.

Studies on Practical Applications of Safe-Fall Control Strategies for Lower Limb Exoskeletons

Lower limb exoskeletons (LLEs) are susceptible to falls, and users are at risk of head and/or hip injuries. To address concerns regarding the safety of LLE users, optimization techniques were used to study safe-fall control strategies. Simulation results of these studies showed promising performance that leads to head impact avoidance and mitigation of hip impact velocity. The motivation for the current research was to extend the application of previously developed optimization techniques to study more realistic human-LLE fall conditions. We examined a range of feasible fall durations for the human-LLE model and found the optimal fall duration for which the user's safety is maximized. Next, we used a range of coefficients of friction to examine fall strategies on different ground surface conditions. We found that the effectiveness of a safe-fall strategy is higher when falling on less slippery surfaces compared to more slippery ones. The simulation results were implemented in a half-scale physical model of a three-link inverted pendulum, which represented a human-LLE model. Results of our experiments verified that the optimal safe-fall strategy could be implemented in a mechanical test setup. The hip linear velocity at impact was found to have similar values in both the experimental (2.04 m/s) and simulation results (2.09 m/s). Further studies should be conducted with appropriate software and hardware platforms to successfully implement safe-fall strategies in an actual LLE.