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This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the soft actuator, which uses long short-term memory (LSTM) neural networks to solve the end-to-end 3D posture for the soft actuators. The method takes into account the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors. The proposed approach uses a flexible bending sensor made from a thin layer of conductive sponge material designed for posture sensing. The LSTM network is used to model the posture of the soft actuator. The effectiveness of the method has been demonstrated on a finger-size 3 degree of freedom (DOF) pneumatic bellow-shaped actuator, with nine flexible sponge resistive sensors placed on the soft actuator’s outer surface. The sensor-characterizing results show that the maximum bending torque of the sensor installed on the actuator is 4.7 Nm, which has an insignificant impact on the actuator motion based on the working space test of the actuator. Moreover, the sensors exhibit a relatively low error rate in predicting the actuator tip position, with error percentages of 0.37%, 2.38%, and 1.58% along the x-, y-, and z-axes, respectively. This work is expected to contribute to the advancement of soft robot dynamic posture perception by using thin sponge sensors and LSTM or other machine learning methods for control.

BackgroundWearable ankle robotics could potentially facilitate intensive repetitive task-specific gait training on stair environment for stroke rehabilitation. A lightweight (0.5 kg) and portable exoskeleton ankle robot was designed to facilitate over-ground and stair training either providing active assistance to move paretic ankle augmenting residual motor function (power-assisted ankle robot, PAAR), or passively support dropped foot by lock/release ankle joint for foot clearance in swing phase (swing-controlled ankle robot, SCAR). In this two-center randomized controlled trial, we hypothesized that conventional training integrated with robot-assisted gait training using either PAAR or SCAR in stair environment are more effective to enhance gait recovery and promote independency in early stroke, than conventional training alone.MethodsSub-acute stroke survivors (within 2 months after stroke onset) received conventional training integrated with 20-session robot-assisted training (at least twice weekly, 30-min per session) on over-ground and stair environments, wearing PAAR (n = 14) or SCAR (n = 16), as compared to control group receiving conventional training only (CT, n = 17). Clinical assessments were performed before and after the 20-session intervention, including functional ambulatory category as primary outcome measure, along with Berg balance scale and timed 10-m walk test.ResultsAfter the 20-session interventions, all three groups showed statistically significant and clinically meaningful within-group functional improvement in all outcome measures (p ConclusionsRobot-assisted stair training would lead to greater functional improvement in gait independency and walking speed than conventional training in usual care. The active powered ankle assistance might facilitate users to walk more and faster with their paretic leg during stair and over-ground walking.Trial registration:ClinicalTrials.gov NCT03184259. Registered on 12 June 2017.

Chronic stroke survivors often suffer from gait impairment resistant to intervention. Recent rehabilitation strategies based on gait training with powered exoskeletons appear promising, but whether chronic survivors may benefit from them remains controversial. We evaluated the potential of exoskeletal gait training in restoring normal motor outputs in chronic survivors (N = 10) by recording electromyographic signals (EMGs, 28 muscles both legs) as they adapted to exoskeletal perturbations, and examined whether any EMG alterations after adaptation were underpinned by closer-to-normal muscle synergies. A unilateral ankle-foot orthosis that produced dorsiflexor torque on the paretic leg during swing was tested. Over a single session, subjects walked overground without exoskeleton (FREE), then with the unpowered exoskeleton (OFF), and finally with the powered exoskeleton (ON). Muscle synergies were identified from EMGs using non-negative matrix factorization. During adaptation to OFF, some paretic-side synergies became more dissimilar to their nonparetic-side counterparts. During adaptation to ON, in half of the subjects some paretic-side synergies became closer to their nonparetic references relative to their similarity at FREE as these paretic-side synergies became sparser in muscle components. Across subjects, level of inter-side similarity increase correlated negatively with the degree of gait temporal asymmetry at FREE. Our results demonstrate the possibility that for some survivors, exoskeletal training may promote closer-to-normal muscle synergies. But to fully achieve this, the active force must trigger adaptive processes that offset any undesired synergy changes arising from adaptation to the device’s mechanical properties while also fostering the reemergence of the normal synergies.

Depth sensors could be a portable, affordable, marker-less alternative to three-dimension motion capture systems for gait analysis, but the effects of camera viewing angles on their joint angle tracking performance have not been fully investigated.This study evaluated the accuracies of three depth sensors [Azure Kinect (AK); Kinect v2 (K2); Orbbec Astra (OA)] for tracking kinematic gait patterns during treadmill walking at five camera viewing angles (0°/22.5°/45°/67.5°/90°).Ten healthy subjects performed fifteen treadmill walking trials (3 speeds × 5 viewing angles) using the three depth sensors to measure joint angles in sagittal hip, frontal hip, sagittal knee, and sagittal ankle. Ten walking steps were recorded and averaged for each walking trial. Range of motion in terms of maximum and minimum joint angles measured by the depth sensors were compared with the Vicon motion capture system as the gold standard. Depth sensors tracking accuracies were compared against the Vicon reference using root-mean-square error (RMSE) on the joint angle time series. Effects of different walking speeds, viewing angles, and depth sensors on the tracking accuracy were observed using three-way repeated-measure analysis of variance (ANOVA).ANOVA results on RMSE showed significant interaction effects between viewing angles and depth sensors for sagittal hip [F(8,72) = 4.404, p = 0.005] and for sagittal knee [F(8,72)=13.211, p 0.001] joint angles. AK had better tracking performance when subjects walked at non-frontal camera viewing angles (22.5°/45°/67.5°/90°); while K2 performed better at frontal viewing angle (0°). The superior tracking performance of AK compared with K2/OA might be attributed to the improved depth sensor resolution and body tracking algorithm.Researchers should be cautious about camera viewing angle when using depth sensors for kinematic gait measurements. Our results demonstrated Azure Kinect had good tracking performance of sagittal hip and sagittal knee joint angles during treadmill walking tests at non-frontal camera viewing angles.

Background: Wearable ankle robotics could potentially facilitate intensive repetitive task-specific gait training on stair environment for stroke rehabilitation. A lightweight (0.5kg) and portable exoskeleton ankle robot was designed to facilitate over-ground and stair training either providing active assistance to move paretic ankle augmenting residual motor function (power-assisted ankle robot, PAAR), or passively support dropped foot by lock/release ankle joint for foot clearance in swing phase (swing-controlled ankle robot, SCAR).In this two-center randomized controlled trial, we hypothesized that robot-assisted gait training using either PAAR or SCAR in stair environment are more effective to enhance gait recovery and promote independency in early stroke, than conventional training.Methods: Sub-acute stroke survivors (within two months after stroke onset) received hospital usual care plus 20-session robot-assisted training (at least twice weekly, 30-minute per session) on over-ground and stair environments, wearing PAAR (n=14) or SCAR (n=16), as compared to control group receiving conventional training only (CT, n=17). PAAR provided 2.5Nm-4.2Nm torque which was calibratedin each training session to adjust for any progression of functional changes throughout the intervention. Clinical assessments were performed before and after the 20-session intervention, including functional ambulatory categoryas primary outcome measure, along with Berg balance scale and timed 10-metre walk test.Results: After the 20-session interventions, all three groups showed statistically significant and clinically meaningful within-group functional improvement in all outcome measures (pConclusions: Robot-assisted stair training would lead to greater functional improvement in gait independency and walking speed than conventional training in usual care. The active powered ankle assistance might facilitate users to walk more and faster with their paretic leg during stair and over-ground walking.Trial Registration:ClinicalTrials.gov NCT03184259. Registered on 12 June 2017.

In the last couple of years, active development of lower limb orthotic devices has mainly targeted individuals with spinal cord injury or stroke. However, due to the smaller population and lack of support from the wider community, there is a group of minorities in society suffering from poliomyelitis who do not benefit from these new technologies. They have unique gait patterns and rehabilitative needs which are very different from those with stroke or spinal cord injuries. To alleviate their difficulties in ambulation and pain, improving the gait performance of individuals with poliomyelitis through newly designed orthoses would be beneficial, our research team has designed a robotic knee orthosis equipped with an automatic knee-locking and actuating mechanism synchronized to the wearer's gait phase. In order to achieve better sensing accuracy, a 3D printed sensing insole with an optimized pressure-sensing module and a specific sensing logic different from existing orthoses has been introduced to identify foot pressure pattern and gait phase during ambulation. This chapter mainly focuses on how to design an exoskeleton robotic system to assist persons with poliomyelitis and to evaluate the effectiveness of the robotic knee orthosis in both clinical performance and gait analysis. A feasibility test has been carried out to investigate the efficacy of robotic knee orthoses on two subjects. Details of how to carry out the clinical training, controlled design of the robotic knee orthosis, and the results of training subjects are discussed.

Background Robot-assisted ankle-foot-orthosis (AFO) can provide immediate powered ankle assistance in post-stroke gait training. Our research team has developed a novel lightweight portable robot-assisted AFO which is capable of detecting walking intentions using sensor feedback of wearer’s gait pattern. This study aims to investigate the therapeutic effects of robot-assisted gait training with ankle dorsiflexion assistance. Methods This was a double-blinded randomized controlled trial. Nineteen chronic stroke patients with motor impairment at ankle participated in 20-session robot-assisted gait training for about five weeks, with 30-min over-ground walking and stair ambulation practices. Robot-assisted AFO either provided active powered ankle assistance during swing phase in Robotic Group (n = 9), or torque impedance at ankle joint as passive AFO in Sham Group (n = 10). Functional assessments were performed before and after the 20-session gait training with 3-month Follow-up. Primary outcome measure was gait independency assessed by Functional Ambulatory Category (FAC). Secondary outcome measures were clinical scores including Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), Timed 10-Meter Walk Test (10MWT), Six-minute Walk Test (SMWT), supplemented by gait analysis. All outcome measures were performed in unassisted gait after patients had taken off the robot-assisted AFO. Repeated-measures analysis of covariance was conducted to test the group differences referenced to clinical scores before training. Results After 20-session robot-assisted gait training with ankle dorsiflexion assistance, the active ankle assistance in Robotic Group induced changes in gait pattern with improved gait independency (all patients FAC ≥ 5 post-training and 3-month follow-up), motor recovery, walking speed, and greater confidence in affected side loading response (vertical ground reaction force + 1.49 N/kg, peak braking force + 0.24 N/kg) with heel strike instead of flat foot touch-down at initial contact (foot tilting + 1.91°). Sham Group reported reduction in affected leg range of motion (ankle dorsiflexion − 2.36° and knee flexion − 8.48°) during swing. Conclusions Robot-assisted gait training with ankle dorsiflexion assistance could improve gait independency and help stroke patients developing confidence in weight acceptance, but future development of robot-assisted AFO should consider more lightweight and custom-fit design. Trial registration ClinicalTrials.gov NCT02471248. Registered 15 June 2015 retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12984-018-0394-7) contains supplementary material, which is available to authorized users.

Figures of the kinetic and kinematic gait pattern pre-post gait training. Figures showing the ground reaction forces (in fore-aft and vertical directions) and joint angles (in hips, knees, and ankles) of both intervention groups before and after the 20-session gait training. (PDF 318 kb)

Lower Limb Exoskeleton robot that can facilitate stair walking is a big challenge, most systems could only provide level ground walking. In this study, a lightweight (0.5kg at ankle, 0.5kg at waist for control box) and autonomous exoskeleton Ankle Robot was proposed to provide power assistance for gait training of chronic stroke patients and it can facilitate three walking conditions in real-time: (1) level walking, (2) stair ascending, and (3) stair descending. Chronic stroke patients (n=3) with drop foot gait deficit and moderate motor impairment were recruited to evaluate the system under different walking conditions (Functional Ambulatory Category: FAC=4.7±0.5 and Fugl-Meyer Assessment for lower-extremity: FMA-LE=13.7±2.9). The system consisted of a specially designed carbon fiber AFO, servomotor, gear transmission system, IMU and force sensors, and control box. The IMU sensors embedded in the shank measured acceleration and angular velocity to identify distinct features in leg tilting angle and leg angular velocity between the three walking conditions. The results showed the powered ankle dorsiflexion assistance could reduce dropped foot of the stroke patients in swing phase and provide better gait pattern. A demo of the ankle robot will be conducted in the conference.